Duplicate from musicgen/MusicGen

Co-authored-by: Felix Kreuk <[email protected]>

.github/actions/audiocraft_build/action.yml

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+name: audiocraft_build
+description: 'Build audiocraft env.'
+runs:
+  using: "composite"
+  steps:
+  - uses: actions/setup-python@v2
+    with:
+      python-version: 3.8
+  - uses: actions/cache@v2
+    id: cache
+    with:
+      path: env
+      key: audiocraft_env-${{ hashFiles('**/requirements.txt') }}
+
+  - if: ${{ steps.cache.outputs.cache-hit != 'true' }}
+    name: Install dependencies
+    shell: bash
+    run: |
+      sudo apt-get update
+      sudo apt-get install libsndfile1-dev ffmpeg
+      python3 -m venv env
+      .  env/bin/activate
+      python -m pip install --upgrade pip
+      pip install -e '.[dev]'
+  - name: System Dependencies
+    shell: bash
+    run: |
+      sudo apt-get update
+      sudo apt-get install libsndfile1-dev ffmpeg

.github/workflows/audiocraft_docs.yml

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+name: audiocraft_docs
+on:
+  push:
+    branches: [ main ]
+
+jobs:
+  run_docs:
+    name: Run docs
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: ./.github/actions/audiocraft_build
+      - name: Config git
+        run: |
+          git config --global user.email "[email protected]"
+          git config --global user.name "Alexandre Défossez (autodoc)"
+
+      - name: Reset branch
+        run: |
+          git branch -f gh-docs main
+          git checkout gh-docs
+
+      - name: Make docs
+        run: |
+          . env/bin/activate
+          make docs
+          git add -f docs
+          git commit -m docs
+
+      - name: Push branch
+        run: |
+          git push -f -u origin gh-docs

.github/workflows/audiocraft_linter.yml

@@ -0,0 +1,17 @@
+name: audiocraft_linter
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  run_linter:
+    name: Run linter
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: ./.github/actions/audiocraft_build
+      - run: |
+          . env/bin/activate
+          make linter

.github/workflows/audiocraft_tests.yml

@@ -0,0 +1,17 @@
+name: audiocraft_tests
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  run_tests:
+    name: Run tests
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: ./.github/actions/audiocraft_build
+      - run: |
+          . env/bin/activate
+          make tests

.gitignore

@@ -0,0 +1,55 @@
+# Byte-compiled / optimized / DLL files
+__pycache__
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# macOS dir files
+.DS_Store
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.ipynb_checkpoints
+
+# Tests and linter
+.pytest_cache/
+.mypy_cache/
+.coverage
+
+# docs
+/docs
+
+# dotenv
+.env
+.envrc
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# personal notebooks & scripts
+*/local_scripts
+*/notes
+.vscode/
+/notebooks
+/local_scripts
+/notes

CHANGELOG.md

@@ -0,0 +1,9 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
+
+## [0.0.1a] - TBD
+
+Initial release, with model evaluation only.

CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <[email protected]>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -0,0 +1,35 @@
+# Contributing to Audiocraft
+
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+
+Audiocraft is the implementation of a research paper.
+Therefore, we do not plan on accepting many pull requests for new features.
+We certainly welcome them for bug fixes.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to encodec, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE_weights

@@ -0,0 +1,157 @@
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+
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MANIFEST.in

@@ -0,0 +1,8 @@
+include Makefile
+include LICENSE
+include LICENSE_weights
+include *.md
+include *.ini
+include requirements.txt
+include audiocraft/py.typed
+include assets/*.mp3

MODEL_CARD.md

@@ -0,0 +1,81 @@
+# MusicGen Model Card
+
+## Model details
+
+**Organization developing the model:** The FAIR team of Meta AI.
+
+**Model date:** MusicGen was trained between April 2023 and May 2023.
+
+**Model version:** This is the version 1 of the model.
+
+**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
+
+**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][arxiv].
+
+**Citation details** See [our paper][arxiv]
+
+**License** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
+
+**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
+
+## Intended use
+**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
+
+- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
+- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
+
+**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
+
+**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
+
+## Metrics
+
+**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark:
+
+- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
+- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
+- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
+
+Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
+
+- Overall quality of the music samples;
+- Text relevance to the provided text input;
+- Adherence to the melody for melody-guided music generation.
+
+More details on performance measures and human studies can be found in the paper.
+
+**Decision thresholds:** Not applicable.
+
+## Evaluation datasets
+
+The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set.
+
+## Training datasets
+
+The model was trained using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound),  [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
+
+## Quantitative analysis
+
+More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section.
+
+## Limitations and biases
+
+**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
+
+**Mitigations:** All vocals have been removed from the data source using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). The model is therefore not able to produce vocals.
+
+**Limitations:**
+
+- The model is not able to generate realistic vocals.
+- The model has been trained with English descriptions and will not perform as well in other languages.
+- The model does not perform equally well for all music styles and cultures.
+- The model sometimes generates end of songs, collapsing to silence.
+- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
+
+**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
+
+**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
+
+**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
+
+[arxiv]: https://arxiv.org/abs/2306.05284

Makefile

@@ -0,0 +1,21 @@
+default: linter tests
+
+install:
+	pip install -U pip
+	pip install -U -e '.[dev]'
+
+linter:
+	flake8 audiocraft && mypy audiocraft
+	flake8 tests && mypy tests
+
+tests:
+	coverage run -m pytest tests
+	coverage report --include 'audiocraft/*'
+
+docs:
+	pdoc3 --html -o docs -f audiocraft
+
+dist:
+	python setup.py sdist
+
+.PHONY: linter tests docs dist

README.md

@@ -0,0 +1,125 @@
+---
+title: MusicGen
+python_version: '3.9'
+tags:
+- music generation
+- language models
+- LLMs
+app_file: app.py
+emoji: 🎵
+colorFrom: white
+colorTo: blue
+sdk: gradio
+sdk_version: 3.34.0
+pinned: true
+suggested_hardware: a10g-large
+license: cc-by-nc-4.0
+duplicated_from: musicgen/MusicGen
+---
+# Audiocraft
+![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
+![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
+![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)
+
+Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
+
+## MusicGen
+
+Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
+Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't not require a self-supervised semantic representation, and it generates
+all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
+them in parallel, thus having only 50 auto-regressive steps per second of audio.
+Check out our [sample page][musicgen_samples] or test the available demo!
+
+<a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
+  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
+</a>
+<br>
+
+## Installation
+Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
+
+```shell
+# Best to make sure you have torch installed first, in particular before installing xformers.
+# Don't run this if you already have PyTorch installed.
+pip install 'torch>=2.0'
+# Then proceed to one of the following
+pip install -U audiocraft  # stable release
+pip install -U git+https://[email protected]/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge
+pip install -e .  # or if you cloned the repo locally
+```
+
+## Usage
+You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally, or use the provided [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing). Finally, a demo is also available on the [`facebook/MusiGen`  HugginFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
+
+## API
+
+We provide a simple API and 4 pre-trained models. The pre trained models are:
+- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
+- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
+- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
+- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
+
+We observe the best trade-off between quality and compute with the `medium` or `melody` model.
+In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
+GPUs will be able to generate short sequences, or longer sequences with the `small` model.
+
+**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
+You can install it with:
+```
+apt get install ffmpeg
+```
+
+See after a quick example for using the API.
+
+```python
+import torchaudio
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
+
+model = MusicGen.get_pretrained('melody')
+model.set_generation_params(duration=8)  # generate 8 seconds.
+wav = model.generate_unconditional(4)    # generates 4 unconditional audio samples
+descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
+wav = model.generate(descriptions)  # generates 3 samples.
+
+melody, sr = torchaudio.load('./assets/bach.mp3')
+# generates using the melody from the given audio and the provided descriptions.
+wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)
+
+for idx, one_wav in enumerate(wav):
+    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
+    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness")
+```
+
+
+## Model Card
+
+See [the model card page](./MODEL_CARD.md).
+
+## FAQ
+
+#### Will the training code be released?
+
+Yes. We will soon release the training code for MusicGen and EnCodec.
+
+
+## Citation
+```
+@article{copet2023simple,
+      title={Simple and Controllable Music Generation},
+      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
+      year={2023},
+      journal={arXiv preprint arXiv:2306.05284},
+}
+```
+
+## License
+* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
+* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
+
+[arxiv]: https://arxiv.org/abs/2306.05284
+[musicgen_samples]: https://ai.honu.io/papers/musicgen/

app.py

@@ -0,0 +1,155 @@
+"""
+Copyright (c) Meta Platforms, Inc. and affiliates.
+All rights reserved.
+
+This source code is licensed under the license found in the
+LICENSE file in the root directory of this source tree.
+"""
+
+from tempfile import NamedTemporaryFile
+import torch
+import gradio as gr
+import os
+from audiocraft.models import MusicGen
+
+from audiocraft.data.audio import audio_write
+
+
+MODEL = None
+IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ['SPACE_ID']
+
+def load_model(version):
+    print("Loading model", version)
+    return MusicGen.get_pretrained(version)
+
+
+def predict(model, text, melody, duration, topk, topp, temperature, cfg_coef):
+    global MODEL
+    topk = int(topk)
+    if MODEL is None or MODEL.name != model:
+        MODEL = load_model(model)
+
+    if duration > MODEL.lm.cfg.dataset.segment_duration:
+        raise gr.Error("MusicGen currently supports durations of up to 30 seconds!")
+    MODEL.set_generation_params(
+        use_sampling=True,
+        top_k=topk,
+        top_p=topp,
+        temperature=temperature,
+        cfg_coef=cfg_coef,
+        duration=duration,
+    )
+
+    if melody:
+        sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
+        print(melody.shape)
+        if melody.dim() == 2:
+            melody = melody[None]
+        melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
+        output = MODEL.generate_with_chroma(
+            descriptions=[text],
+            melody_wavs=melody,
+            melody_sample_rate=sr,
+            progress=False
+        )
+    else:
+        output = MODEL.generate(descriptions=[text], progress=False)
+
+    output = output.detach().cpu().float()[0]
+    with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
+        audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False)
+        waveform_video = gr.make_waveform(file.name)
+    return waveform_video
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # MusicGen
+        This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
+        presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
+        """
+    )
+    if IS_SHARED_SPACE:
+        gr.Markdown("""
+            ⚠ This Space doesn't work in this shared UI ⚠
+            
+            <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
+            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+            to use it privately, or use the <a href="https://huggingface.co/spaces/facebook/MusicGen">public demo</a>
+            """
+    )
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                text = gr.Text(label="Input Text", interactive=True)
+                melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
+            with gr.Row():
+                submit = gr.Button("Submit" if not IS_SHARED_SPACE else "Duplicate the Space to generate", interactive=not IS_SHARED_SPACE)
+            with gr.Row():
+                model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
+            with gr.Row():
+                duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
+            with gr.Row():
+                topk = gr.Number(label="Top-k", value=250, interactive=True)
+                topp = gr.Number(label="Top-p", value=0, interactive=True)
+                temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
+                cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
+        with gr.Column():
+            output = gr.Video(label="Generated Music")
+    submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
+    gr.Examples(
+        fn=predict,
+        examples=[
+            [
+                "An 80s driving pop song with heavy drums and synth pads in the background",
+                "./assets/bach.mp3",
+                "melody"
+            ],
+            [
+                "A cheerful country song with acoustic guitars",
+                "./assets/bolero_ravel.mp3",
+                "melody"
+            ],
+            [
+                "90s rock song with electric guitar and heavy drums",
+                None,
+                "medium"
+            ],
+            [
+                "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
+                "./assets/bach.mp3",
+                "melody"
+            ],
+            [
+                "lofi slow bpm electro chill with organic samples",
+                None,
+                "medium",
+            ],
+        ],
+        inputs=[text, melody, model],
+        outputs=[output]
+    )
+    gr.Markdown(
+        """
+        ### More details
+
+        The model will generate a short music extract based on the description you provided.
+        You can generate up to 30 seconds of audio.
+
+        We present 4 model variations:
+        1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
+        2. Small -- a 300M transformer decoder conditioned on text only.
+        3. Medium -- a 1.5B transformer decoder conditioned on text only.
+        4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
+
+        When using `melody`, ou can optionaly provide a reference audio from
+        which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
+
+        You can also use your own GPU or a Google Colab by following the instructions on our repo.
+        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
+        for more details.
+        """
+    )
+
+demo.launch()

app_batched.py

@@ -0,0 +1,130 @@
+"""
+Copyright (c) Meta Platforms, Inc. and affiliates.
+All rights reserved.
+
+This source code is licensed under the license found in the
+LICENSE file in the root directory of this source tree.
+"""
+
+from tempfile import NamedTemporaryFile
+import torch
+import gradio as gr
+from audiocraft.data.audio_utils import convert_audio
+from audiocraft.data.audio import audio_write
+from audiocraft.models import MusicGen
+
+
+MODEL = None
+
+
+def load_model():
+    print("Loading model")
+    return MusicGen.get_pretrained("melody")
+
+
+def predict(texts, melodies):
+    global MODEL
+    if MODEL is None:
+        MODEL = load_model()
+
+    duration = 12
+    MODEL.set_generation_params(duration=duration)
+
+    print(texts, melodies)
+    processed_melodies = []
+
+    target_sr = 32000
+    target_ac = 1
+    for melody in melodies:
+        if melody is None:
+            processed_melodies.append(None)
+        else:
+            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
+            if melody.dim() == 1:
+                melody = melody[None]
+            melody = melody[..., :int(sr * duration)]
+            melody = convert_audio(melody, sr, target_sr, target_ac)
+            processed_melodies.append(melody)
+
+    outputs = MODEL.generate_with_chroma(
+        descriptions=texts,
+        melody_wavs=processed_melodies,
+        melody_sample_rate=target_sr,
+        progress=False
+    )
+
+    outputs = outputs.detach().cpu().float()
+    out_files = []
+    for output in outputs:
+        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
+            audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False)
+            waveform_video = gr.make_waveform(file.name)
+            out_files.append(waveform_video)
+    return [out_files]
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # MusicGen
+
+        This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
+        presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
+        <br/>
+        <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
+        <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+        for longer sequences, more control and no queue.</p>
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                text = gr.Text(label="Describe your music", lines=2, interactive=True)
+                melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
+            with gr.Row():
+                submit = gr.Button("Generate")
+        with gr.Column():
+            output = gr.Video(label="Generated Music")
+    submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=12)
+    gr.Examples(
+        fn=predict,
+        examples=[
+            [
+                "An 80s driving pop song with heavy drums and synth pads in the background",
+                "./assets/bach.mp3",
+            ],
+            [
+                "A cheerful country song with acoustic guitars",
+                "./assets/bolero_ravel.mp3",
+            ],
+            [
+                "90s rock song with electric guitar and heavy drums",
+                None,
+            ],
+            [
+                "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
+                "./assets/bach.mp3",
+            ],
+            [
+                "lofi slow bpm electro chill with organic samples",
+                None,
+            ],
+        ],
+        inputs=[text, melody],
+        outputs=[output]
+    )
+    gr.Markdown("""
+    ### More details
+
+    The model will generate 12 seconds of audio based on the description you provided.
+    You can optionaly provide a reference audio from which a broad melody will be extracted.
+    The model will then try to follow both the description and melody provided.
+    All samples are generated with the `melody` model.
+  
+    You can also use your own GPU or a Google Colab by following the instructions on our repo.
+
+    See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
+    for more details.
+    """)
+
+demo.queue(max_size=15).launch()

audiocraft/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# flake8: noqa
+from . import data, modules, models
+
+__version__ = '0.0.1'

audiocraft/data/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# flake8: noqa
+from . import audio, audio_dataset

audiocraft/data/audio.py

@@ -0,0 +1,213 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Audio IO methods are defined in this module (info, read, write),
+We rely on av library for faster read when possible, otherwise on torchaudio.
+"""
+
+from dataclasses import dataclass
+from pathlib import Path
+import logging
+import typing as tp
+
+import numpy as np
+import soundfile
+import torch
+from torch.nn import functional as F
+import torchaudio as ta
+
+import av
+
+from .audio_utils import f32_pcm, i16_pcm, normalize_audio
+
+
+_av_initialized = False
+
+
+def _init_av():
+    global _av_initialized
+    if _av_initialized:
+        return
+    logger = logging.getLogger('libav.mp3')
+    logger.setLevel(logging.ERROR)
+    _av_initialized = True
+
+
+@dataclass(frozen=True)
+class AudioFileInfo:
+    sample_rate: int
+    duration: float
+    channels: int
+
+
+def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
+    _init_av()
+    with av.open(str(filepath)) as af:
+        stream = af.streams.audio[0]
+        sample_rate = stream.codec_context.sample_rate
+        duration = float(stream.duration * stream.time_base)
+        channels = stream.channels
+        return AudioFileInfo(sample_rate, duration, channels)
+
+
+def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
+    info = soundfile.info(filepath)
+    return AudioFileInfo(info.samplerate, info.duration, info.channels)
+
+
+def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
+    # torchaudio no longer returns useful duration informations for some formats like mp3s.
+    filepath = Path(filepath)
+    if filepath.suffix in ['.flac', '.ogg']:  # TODO: Validate .ogg can be safely read with av_info
+        # ffmpeg has some weird issue with flac.
+        return _soundfile_info(filepath)
+    else:
+        return _av_info(filepath)
+
+
+def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]:
+    """FFMPEG-based audio file reading using PyAV bindings.
+    Soundfile cannot read mp3 and av_read is more efficient than torchaudio.
+
+    Args:
+        filepath (str or Path): Path to audio file to read.
+        seek_time (float): Time at which to start reading in the file.
+        duration (float): Duration to read from the file. If set to -1, the whole file is read.
+    Returns:
+        Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate
+    """
+    _init_av()
+    with av.open(str(filepath)) as af:
+        stream = af.streams.audio[0]
+        sr = stream.codec_context.sample_rate
+        num_frames = int(sr * duration) if duration >= 0 else -1
+        frame_offset = int(sr * seek_time)
+        # we need a small negative offset otherwise we get some edge artifact
+        # from the mp3 decoder.
+        af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream)
+        frames = []
+        length = 0
+        for frame in af.decode(streams=stream.index):
+            current_offset = int(frame.rate * frame.pts * frame.time_base)
+            strip = max(0, frame_offset - current_offset)
+            buf = torch.from_numpy(frame.to_ndarray())
+            if buf.shape[0] != stream.channels:
+                buf = buf.view(-1, stream.channels).t()
+            buf = buf[:, strip:]
+            frames.append(buf)
+            length += buf.shape[1]
+            if num_frames > 0 and length >= num_frames:
+                break
+        assert frames
+        # If the above assert fails, it is likely because we seeked past the end of file point,
+        # in which case ffmpeg returns a single frame with only zeros, and a weird timestamp.
+        # This will need proper debugging, in due time.
+        wav = torch.cat(frames, dim=1)
+        assert wav.shape[0] == stream.channels
+        if num_frames > 0:
+            wav = wav[:, :num_frames]
+        return f32_pcm(wav), sr
+
+
+def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
+               duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]:
+    """Read audio by picking the most appropriate backend tool based on the audio format.
+
+    Args:
+        filepath (str or Path): Path to audio file to read.
+        seek_time (float): Time at which to start reading in the file.
+        duration (float): Duration to read from the file. If set to -1, the whole file is read.
+        pad (bool): Pad output audio if not reaching expected duration.
+    Returns:
+        Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate.
+    """
+    fp = Path(filepath)
+    if fp.suffix in ['.flac', '.ogg']:  # TODO: check if we can safely use av_read for .ogg
+        # There is some bug with ffmpeg and reading flac
+        info = _soundfile_info(filepath)
+        frames = -1 if duration <= 0 else int(duration * info.sample_rate)
+        frame_offset = int(seek_time * info.sample_rate)
+        wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32)
+        assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}"
+        wav = torch.from_numpy(wav).t().contiguous()
+        if len(wav.shape) == 1:
+            wav = torch.unsqueeze(wav, 0)
+    elif (
+        fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats()
+        and duration <= 0 and seek_time == 0
+    ):
+        # Torchaudio is faster if we load an entire file at once.
+        wav, sr = ta.load(fp)
+    else:
+        wav, sr = _av_read(filepath, seek_time, duration)
+    if pad and duration > 0:
+        expected_frames = int(duration * sr)
+        wav = F.pad(wav, (0, expected_frames - wav.shape[-1]))
+    return wav, sr
+
+
+def audio_write(stem_name: tp.Union[str, Path],
+                wav: torch.Tensor, sample_rate: int,
+                format: str = 'wav', mp3_rate: int = 320, normalize: bool = True,
+                strategy: str = 'peak', peak_clip_headroom_db: float = 1,
+                rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
+                log_clipping: bool = True, make_parent_dir: bool = True,
+                add_suffix: bool = True) -> Path:
+    """Convenience function for saving audio to disk. Returns the filename the audio was written to.
+
+    Args:
+        stem_name (str or Path): Filename without extension which will be added automatically.
+        format (str): Either "wav" or "mp3".
+        mp3_rate (int): kbps when using mp3s.
+        normalize (bool): if `True` (default), normalizes according to the prescribed
+            strategy (see after). If `False`, the strategy is only used in case clipping
+            would happen.
+        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
+            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
+            with extra headroom to avoid clipping. 'clip' just clips.
+        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
+        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
+            than the `peak_clip` one to avoid further clipping.
+        loudness_headroom_db (float): Target loudness for loudness normalization.
+        log_clipping (bool): If True, basic logging on stderr when clipping still
+            occurs despite strategy (only for 'rms').
+        make_parent_dir (bool): Make parent directory if it doesn't exist.
+    Returns:
+        Path: Path of the saved audio.
+    """
+    assert wav.dtype.is_floating_point, "wav is not floating point"
+    if wav.dim() == 1:
+        wav = wav[None]
+    elif wav.dim() > 2:
+        raise ValueError("Input wav should be at most 2 dimension.")
+    assert wav.isfinite().all()
+    wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db,
+                          rms_headroom_db, loudness_headroom_db, log_clipping=log_clipping,
+                          sample_rate=sample_rate, stem_name=str(stem_name))
+    kwargs: dict = {}
+    if format == 'mp3':
+        suffix = '.mp3'
+        kwargs.update({"compression": mp3_rate})
+    elif format == 'wav':
+        wav = i16_pcm(wav)
+        suffix = '.wav'
+        kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16})
+    else:
+        raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.")
+    if not add_suffix:
+        suffix = ''
+    path = Path(str(stem_name) + suffix)
+    if make_parent_dir:
+        path.parent.mkdir(exist_ok=True, parents=True)
+    try:
+        ta.save(path, wav, sample_rate, **kwargs)
+    except Exception:
+        if path.exists():
+            # we do not want to leave half written files around.
+            path.unlink()
+        raise
+    return path

audiocraft/data/audio_dataset.py

@@ -0,0 +1,525 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import copy
+from concurrent.futures import ThreadPoolExecutor, Future
+from dataclasses import dataclass, fields
+from contextlib import ExitStack
+import gzip
+import json
+import logging
+import os
+from pathlib import Path
+import random
+import sys
+import typing as tp
+
+import torch
+import torch.nn.functional as F
+
+from .audio import audio_read, audio_info
+from .audio_utils import convert_audio
+from .zip import PathInZip
+
+try:
+    import dora
+except ImportError:
+    dora = None  # type: ignore
+
+
+@dataclass(order=True)
+class BaseInfo:
+
+    @classmethod
+    def _dict2fields(cls, dictionary: dict):
+        return {
+            field.name: dictionary[field.name]
+            for field in fields(cls) if field.name in dictionary
+        }
+
+    @classmethod
+    def from_dict(cls, dictionary: dict):
+        _dictionary = cls._dict2fields(dictionary)
+        return cls(**_dictionary)
+
+    def to_dict(self):
+        return {
+            field.name: self.__getattribute__(field.name)
+            for field in fields(self)
+            }
+
+
+@dataclass(order=True)
+class AudioMeta(BaseInfo):
+    path: str
+    duration: float
+    sample_rate: int
+    amplitude: tp.Optional[float] = None
+    weight: tp.Optional[float] = None
+    # info_path is used to load additional information about the audio file that is stored in zip files.
+    info_path: tp.Optional[PathInZip] = None
+
+    @classmethod
+    def from_dict(cls, dictionary: dict):
+        base = cls._dict2fields(dictionary)
+        if 'info_path' in base and base['info_path'] is not None:
+            base['info_path'] = PathInZip(base['info_path'])
+        return cls(**base)
+
+    def to_dict(self):
+        d = super().to_dict()
+        if d['info_path'] is not None:
+            d['info_path'] = str(d['info_path'])
+        return d
+
+
+@dataclass(order=True)
+class SegmentInfo(BaseInfo):
+    meta: AudioMeta
+    seek_time: float
+    n_frames: int  # actual number of frames without padding
+    total_frames: int  # total number of frames, padding included
+    sample_rate: int  # actual sample rate
+
+
+DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a']
+
+logger = logging.getLogger(__name__)
+
+
+def _get_audio_meta(file_path: str, minimal: bool = True) -> AudioMeta:
+    """AudioMeta from a path to an audio file.
+
+    Args:
+        file_path (str): Resolved path of valid audio file.
+        minimal (bool): Whether to only load the minimal set of metadata (takes longer if not).
+    Returns:
+        AudioMeta: Audio file path and its metadata.
+    """
+    info = audio_info(file_path)
+    amplitude: tp.Optional[float] = None
+    if not minimal:
+        wav, sr = audio_read(file_path)
+        amplitude = wav.abs().max().item()
+    return AudioMeta(file_path, info.duration, info.sample_rate, amplitude)
+
+
+def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta:
+    """If Dora is available as a dependency, try to resolve potential relative paths
+    in list of AudioMeta. This method is expected to be used when loading meta from file.
+
+    Args:
+        m (AudioMeta): Audio meta to resolve.
+        fast (bool): If True, uses a really fast check for determining if a file is already absolute or not.
+            Only valid on Linux/Mac.
+    Returns:
+        AudioMeta: Audio meta with resolved path.
+    """
+    def is_abs(m):
+        if fast:
+            return str(m)[0] == '/'
+        else:
+            os.path.isabs(str(m))
+
+    if not dora:
+        return m
+
+    if not is_abs(m.path):
+        m.path = dora.git_save.to_absolute_path(m.path)
+    if m.info_path is not None and not is_abs(m.info_path.zip_path):
+        m.info_path.zip_path = dora.git_save.to_absolute_path(m.path)
+    return m
+
+
+def find_audio_files(path: tp.Union[Path, str],
+                     exts: tp.List[str] = DEFAULT_EXTS,
+                     resolve: bool = True,
+                     minimal: bool = True,
+                     progress: bool = False,
+                     workers: int = 0) -> tp.List[AudioMeta]:
+    """Build a list of AudioMeta from a given path,
+    collecting relevant audio files and fetching meta info.
+
+    Args:
+        path (str or Path): Path to folder containing audio files.
+        exts (list of str): List of file extensions to consider for audio files.
+        minimal (bool): Whether to only load the minimal set of metadata (takes longer if not).
+        progress (bool): Whether to log progress on audio files collection.
+        workers (int): number of parallel workers, if 0, use only the current thread.
+    Returns:
+        List[AudioMeta]: List of audio file path and its metadata.
+    """
+    audio_files = []
+    futures: tp.List[Future] = []
+    pool: tp.Optional[ThreadPoolExecutor] = None
+    with ExitStack() as stack:
+        if workers > 0:
+            pool = ThreadPoolExecutor(workers)
+            stack.enter_context(pool)
+
+        if progress:
+            print("Finding audio files...")
+        for root, folders, files in os.walk(path, followlinks=True):
+            for file in files:
+                full_path = Path(root) / file
+                if full_path.suffix.lower() in exts:
+                    audio_files.append(full_path)
+                    if pool is not None:
+                        futures.append(pool.submit(_get_audio_meta, str(audio_files[-1]), minimal))
+                    if progress:
+                        print(format(len(audio_files), " 8d"), end='\r', file=sys.stderr)
+
+        if progress:
+            print("Getting audio metadata...")
+        meta: tp.List[AudioMeta] = []
+        for idx, file_path in enumerate(audio_files):
+            try:
+                if pool is None:
+                    m = _get_audio_meta(str(file_path), minimal)
+                else:
+                    m = futures[idx].result()
+                if resolve:
+                    m = _resolve_audio_meta(m)
+            except Exception as err:
+                print("Error with", str(file_path), err, file=sys.stderr)
+                continue
+            meta.append(m)
+            if progress:
+                print(format((1 + idx) / len(audio_files), " 3.1%"), end='\r', file=sys.stderr)
+    meta.sort()
+    return meta
+
+
+def load_audio_meta(path: tp.Union[str, Path],
+                    resolve: bool = True, fast: bool = True) -> tp.List[AudioMeta]:
+    """Load list of AudioMeta from an optionally compressed json file.
+
+    Args:
+        path (str or Path): Path to JSON file.
+        resolve (bool): Whether to resolve the path from AudioMeta (default=True).
+        fast (bool): activates some tricks to make things faster.
+    Returns:
+        List[AudioMeta]: List of audio file path and its total duration.
+    """
+    open_fn = gzip.open if str(path).lower().endswith('.gz') else open
+    with open_fn(path, 'rb') as fp:  # type: ignore
+        lines = fp.readlines()
+    meta = []
+    for line in lines:
+        d = json.loads(line)
+        m = AudioMeta.from_dict(d)
+        if resolve:
+            m = _resolve_audio_meta(m, fast=fast)
+        meta.append(m)
+    return meta
+
+
+def save_audio_meta(path: tp.Union[str, Path], meta: tp.List[AudioMeta]):
+    """Save the audio metadata to the file pointer as json.
+
+    Args:
+        path (str or Path): Path to JSON file.
+        metadata (list of BaseAudioMeta): List of audio meta to save.
+    """
+    Path(path).parent.mkdir(exist_ok=True, parents=True)
+    open_fn = gzip.open if str(path).lower().endswith('.gz') else open
+    with open_fn(path, 'wb') as fp:  # type: ignore
+        for m in meta:
+            json_str = json.dumps(m.to_dict()) + '\n'
+            json_bytes = json_str.encode('utf-8')
+            fp.write(json_bytes)
+
+
+class AudioDataset:
+    """Base audio dataset.
+
+    The dataset takes a list of AudioMeta and create a dataset composed of segments of audio
+    and potentially additional information, by creating random segments from the list of audio
+    files referenced in the metadata and applying minimal data pre-processing such as resampling,
+    mixing of channels, padding, etc.
+
+    If no segment_duration value is provided, the AudioDataset will return the full wav for each
+    audio file. Otherwise, it will randomly sample audio files and create a segment of the specified
+    duration, applying padding if required.
+
+    By default, only the torch Tensor corresponding to the waveform is returned. Setting return_info=True
+    allows to return a tuple containing the torch Tensor and additional metadata on the segment and the
+    original audio meta.
+
+    Args:
+        meta (tp.List[AudioMeta]): List of audio files metadata.
+        segment_duration (float): Optional segment duration of audio to load.
+            If not specified, the dataset will load the full audio segment from the file.
+        shuffle (bool): Set to `True` to have the data reshuffled at every epoch.
+        sample_rate (int): Target sample rate of the loaded audio samples.
+        channels (int): Target number of channels of the loaded audio samples.
+        sample_on_duration (bool): Set to `True` to sample segments with probability
+            dependent on audio file duration. This is only used if `segment_duration` is provided.
+        sample_on_weight (bool): Set to `True` to sample segments using the `weight` entry of
+            `AudioMeta`. If `sample_on_duration` is also True, the actual weight will be the product
+            of the file duration and file weight. This is only used if `segment_duration` is provided.
+        min_segment_ratio (float): Minimum segment ratio to use when the audio file
+            is shorter than the desired segment.
+        max_read_retry (int): Maximum number of retries to sample an audio segment from the dataset.
+        return_info (bool): Whether to return the wav only or return wav along with segment info and metadata.
+        min_audio_duration (tp.Optional[float], optional): Minimum audio file duration, in seconds, if provided
+            audio shorter than this will be filtered out.
+        max_audio_duration (tp.Optional[float], optional): Maximal audio file duration in seconds, if provided
+            audio longer than this will be filtered out.
+    """
+    def __init__(self,
+                 meta: tp.List[AudioMeta],
+                 segment_duration: tp.Optional[float] = None,
+                 shuffle: bool = True,
+                 num_samples: int = 10_000,
+                 sample_rate: int = 48_000,
+                 channels: int = 2,
+                 pad: bool = True,
+                 sample_on_duration: bool = True,
+                 sample_on_weight: bool = True,
+                 min_segment_ratio: float = 0.5,
+                 max_read_retry: int = 10,
+                 return_info: bool = False,
+                 min_audio_duration: tp.Optional[float] = None,
+                 max_audio_duration: tp.Optional[float] = None
+                 ):
+        assert len(meta) > 0, 'No audio meta provided to AudioDataset. Please check loading of audio meta.'
+        assert segment_duration is None or segment_duration > 0
+        assert segment_duration is None or min_segment_ratio >= 0
+        logging.debug(f'sample_on_duration: {sample_on_duration}')
+        logging.debug(f'sample_on_weight: {sample_on_weight}')
+        logging.debug(f'pad: {pad}')
+        logging.debug(f'min_segment_ratio: {min_segment_ratio}')
+
+        self.segment_duration = segment_duration
+        self.min_segment_ratio = min_segment_ratio
+        self.max_audio_duration = max_audio_duration
+        self.min_audio_duration = min_audio_duration
+        if self.min_audio_duration is not None and self.max_audio_duration is not None:
+            assert self.min_audio_duration <= self.max_audio_duration
+        self.meta: tp.List[AudioMeta] = self._filter_duration(meta)
+        assert len(self.meta)  # Fail fast if all data has been filtered.
+        self.total_duration = sum(d.duration for d in self.meta)
+
+        if segment_duration is None:
+            num_samples = len(self.meta)
+        self.num_samples = num_samples
+        self.shuffle = shuffle
+        self.sample_rate = sample_rate
+        self.channels = channels
+        self.pad = pad
+        self.sample_on_weight = sample_on_weight
+        self.sample_on_duration = sample_on_duration
+        self.sampling_probabilities = self._get_sampling_probabilities()
+        self.max_read_retry = max_read_retry
+        self.return_info = return_info
+
+    def __len__(self):
+        return self.num_samples
+
+    def _get_sampling_probabilities(self, normalized: bool = True):
+        """Return the sampling probabilities for each file inside `self.meta`.
+        """
+        scores: tp.List[float] = []
+        for file_meta in self.meta:
+            score = 1.
+            if self.sample_on_weight and file_meta.weight is not None:
+                score *= file_meta.weight
+            if self.sample_on_duration:
+                score *= file_meta.duration
+            scores.append(score)
+        probabilities = torch.tensor(scores)
+        if normalized:
+            probabilities /= probabilities.sum()
+        return probabilities
+
+    def sample_file(self, rng: torch.Generator) -> AudioMeta:
+        """Sample a given file from `self.meta`. Can be overriden in subclasses.
+        This is only called if `segment_duration` is not None.
+
+        You must use the provided random number generator `rng` for reproducibility.
+        """
+        if not self.sample_on_weight and not self.sample_on_duration:
+            file_index = int(torch.randint(len(self.sampling_probabilities), (1,), generator=rng).item())
+        else:
+            file_index = int(torch.multinomial(self.sampling_probabilities, 1, generator=rng).item())
+
+        return self.meta[file_index]
+
+    def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentInfo]]:
+        if self.segment_duration is None:
+            file_meta = self.meta[index]
+            out, sr = audio_read(file_meta.path)
+            out = convert_audio(out, sr, self.sample_rate, self.channels)
+            n_frames = out.shape[-1]
+            segment_info = SegmentInfo(file_meta, seek_time=0., n_frames=n_frames, total_frames=n_frames,
+                                       sample_rate=self.sample_rate)
+        else:
+            rng = torch.Generator()
+            if self.shuffle:
+                # We use index, plus extra randomness
+                rng.manual_seed(index + self.num_samples * random.randint(0, 2**24))
+            else:
+                # We only use index
+                rng.manual_seed(index)
+
+            for retry in range(self.max_read_retry):
+                file_meta = self.sample_file(rng)
+                # We add some variance in the file position even if audio file is smaller than segment
+                # without ending up with empty segments
+                max_seek = max(0, file_meta.duration - self.segment_duration * self.min_segment_ratio)
+                seek_time = torch.rand(1, generator=rng).item() * max_seek
+                try:
+                    out, sr = audio_read(file_meta.path, seek_time, self.segment_duration, pad=False)
+                    out = convert_audio(out, sr, self.sample_rate, self.channels)
+                    n_frames = out.shape[-1]
+                    target_frames = int(self.segment_duration * self.sample_rate)
+                    if self.pad:
+                        out = F.pad(out, (0, target_frames - n_frames))
+                    segment_info = SegmentInfo(file_meta, seek_time, n_frames=n_frames, total_frames=target_frames,
+                                               sample_rate=self.sample_rate)
+                except Exception as exc:
+                    logger.warning("Error opening file %s: %r", file_meta.path, exc)
+                    if retry == self.max_read_retry - 1:
+                        raise
+                else:
+                    break
+
+        if self.return_info:
+            # Returns the wav and additional information on the wave segment
+            return out, segment_info
+        else:
+            return out
+
+    def collater(self, samples):
+        """The collater function has to be provided to the dataloader
+        if AudioDataset has return_info=True in order to properly collate
+        the samples of a batch.
+        """
+        if self.segment_duration is None and len(samples) > 1:
+            assert self.pad, "Must allow padding when batching examples of different durations."
+
+        # In this case the audio reaching the collater is of variable length as segment_duration=None.
+        to_pad = self.segment_duration is None and self.pad
+        if to_pad:
+            max_len = max([wav.shape[-1] for wav, _ in samples])
+
+            def _pad_wav(wav):
+                return F.pad(wav, (0, max_len - wav.shape[-1]))
+
+        if self.return_info:
+            if len(samples) > 0:
+                assert len(samples[0]) == 2
+                assert isinstance(samples[0][0], torch.Tensor)
+                assert isinstance(samples[0][1], SegmentInfo)
+
+            wavs = [wav for wav, _ in samples]
+            segment_infos = [copy.deepcopy(info) for _, info in samples]
+
+            if to_pad:
+                # Each wav could be of a different duration as they are not segmented.
+                for i in range(len(samples)):
+                    # Determines the total legth of the signal with padding, so we update here as we pad.
+                    segment_infos[i].total_frames = max_len
+                    wavs[i] = _pad_wav(wavs[i])
+
+            wav = torch.stack(wavs)
+            return wav, segment_infos
+        else:
+            assert isinstance(samples[0], torch.Tensor)
+            if to_pad:
+                samples = [_pad_wav(s) for s in samples]
+            return torch.stack(samples)
+
+    def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]:
+        """Filters out audio files with short durations.
+        Removes from meta files that have durations that will not allow to samples examples from them.
+        """
+        orig_len = len(meta)
+
+        # Filter data that is too short.
+        if self.min_audio_duration is not None:
+            meta = [m for m in meta if m.duration >= self.min_audio_duration]
+
+        # Filter data that is too long.
+        if self.max_audio_duration is not None:
+            meta = [m for m in meta if m.duration <= self.max_audio_duration]
+
+        filtered_len = len(meta)
+        removed_percentage = 100*(1-float(filtered_len)/orig_len)
+        msg = 'Removed %.2f percent of the data because it was too short or too long.' % removed_percentage
+        if removed_percentage < 10:
+            logging.debug(msg)
+        else:
+            logging.warning(msg)
+        return meta
+
+    @classmethod
+    def from_meta(cls, root: tp.Union[str, Path], **kwargs):
+        """Instantiate AudioDataset from a path to a directory containing a manifest as a jsonl file.
+
+        Args:
+            root (str or Path): Path to root folder containing audio files.
+            kwargs: Additional keyword arguments for the AudioDataset.
+        """
+        root = Path(root)
+        if root.is_dir():
+            if (root / 'data.jsonl').exists():
+                root = root / 'data.jsonl'
+            elif (root / 'data.jsonl.gz').exists():
+                root = root / 'data.jsonl.gz'
+            else:
+                raise ValueError("Don't know where to read metadata from in the dir. "
+                                 "Expecting either a data.jsonl or data.jsonl.gz file but none found.")
+        meta = load_audio_meta(root)
+        return cls(meta, **kwargs)
+
+    @classmethod
+    def from_path(cls, root: tp.Union[str, Path], minimal_meta: bool = True,
+                  exts: tp.List[str] = DEFAULT_EXTS, **kwargs):
+        """Instantiate AudioDataset from a path containing (possibly nested) audio files.
+
+        Args:
+            root (str or Path): Path to root folder containing audio files.
+            minimal_meta (bool): Whether to only load minimal metadata or not.
+            exts (list of str): Extensions for audio files.
+            kwargs: Additional keyword arguments for the AudioDataset.
+        """
+        root = Path(root)
+        if root.is_file():
+            meta = load_audio_meta(root, resolve=True)
+        else:
+            meta = find_audio_files(root, exts, minimal=minimal_meta, resolve=True)
+        return cls(meta, **kwargs)
+
+
+def main():
+    logging.basicConfig(stream=sys.stderr, level=logging.INFO)
+    parser = argparse.ArgumentParser(
+        prog='audio_dataset',
+        description='Generate .jsonl files by scanning a folder.')
+    parser.add_argument('root', help='Root folder with all the audio files')
+    parser.add_argument('output_meta_file',
+                        help='Output file to store the metadata, ')
+    parser.add_argument('--complete',
+                        action='store_false', dest='minimal', default=True,
+                        help='Retrieve all metadata, even the one that are expansive '
+                             'to compute (e.g. normalization).')
+    parser.add_argument('--resolve',
+                        action='store_true', default=False,
+                        help='Resolve the paths to be absolute and with no symlinks.')
+    parser.add_argument('--workers',
+                        default=10, type=int,
+                        help='Number of workers.')
+    args = parser.parse_args()
+    meta = find_audio_files(args.root, DEFAULT_EXTS, progress=True,
+                            resolve=args.resolve, minimal=args.minimal, workers=args.workers)
+    save_audio_meta(args.output_meta_file, meta)
+
+
+if __name__ == '__main__':
+    main()

audiocraft/data/audio_utils.py

@@ -0,0 +1,169 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import sys
+import typing as tp
+
+import julius
+import torch
+import torchaudio
+
+
+def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
+    """Convert audio to the given number of channels.
+
+    Args:
+        wav (torch.Tensor): Audio wave of shape [B, C, T].
+        channels (int): Expected number of channels as output.
+    Returns:
+        torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
+    """
+    *shape, src_channels, length = wav.shape
+    if src_channels == channels:
+        pass
+    elif channels == 1:
+        # Case 1:
+        # The caller asked 1-channel audio, and the stream has multiple
+        # channels, downmix all channels.
+        wav = wav.mean(dim=-2, keepdim=True)
+    elif src_channels == 1:
+        # Case 2:
+        # The caller asked for multiple channels, but the input file has
+        # a single channel, replicate the audio over all channels.
+        wav = wav.expand(*shape, channels, length)
+    elif src_channels >= channels:
+        # Case 3:
+        # The caller asked for multiple channels, and the input file has
+        # more channels than requested. In that case return the first channels.
+        wav = wav[..., :channels, :]
+    else:
+        # Case 4: What is a reasonable choice here?
+        raise ValueError('The audio file has less channels than requested but is not mono.')
+    return wav
+
+
+def convert_audio(wav: torch.Tensor, from_rate: float,
+                  to_rate: float, to_channels: int) -> torch.Tensor:
+    """Convert audio to new sample rate and number of audio channels.
+    """
+    wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
+    wav = convert_audio_channels(wav, to_channels)
+    return wav
+
+
+def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 12,
+                       energy_floor: float = 2e-3):
+    """Normalize an input signal to a user loudness in dB LKFS.
+    Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
+
+    Args:
+        wav (torch.Tensor): Input multichannel audio data.
+        sample_rate (int): Sample rate.
+        loudness_headroom_db (float): Target loudness of the output in dB LUFS.
+        energy_floor (float): anything below that RMS level will not be rescaled.
+    Returns:
+        output (torch.Tensor): Loudness normalized output data.
+    """
+    energy = wav.pow(2).mean().sqrt().item()
+    if energy < energy_floor:
+        return wav
+    transform = torchaudio.transforms.Loudness(sample_rate)
+    input_loudness_db = transform(wav).item()
+    # calculate the gain needed to scale to the desired loudness level
+    delta_loudness = -loudness_headroom_db - input_loudness_db
+    gain = 10.0 ** (delta_loudness / 20.0)
+    output = gain * wav
+    assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
+    return output
+
+
+def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
+    """Utility function to clip the audio with logging if specified."""
+    max_scale = wav.abs().max()
+    if log_clipping and max_scale > 1:
+        clamp_prob = (wav.abs() > 1).float().mean().item()
+        print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
+              clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
+    wav.clamp_(-1, 1)
+
+
+def normalize_audio(wav: torch.Tensor, normalize: bool = True,
+                    strategy: str = 'peak', peak_clip_headroom_db: float = 1,
+                    rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
+                    log_clipping: bool = False, sample_rate: tp.Optional[int] = None,
+                    stem_name: tp.Optional[str] = None) -> torch.Tensor:
+    """Normalize the audio according to the prescribed strategy (see after).
+
+    Args:
+        wav (torch.Tensor): Audio data.
+        normalize (bool): if `True` (default), normalizes according to the prescribed
+            strategy (see after). If `False`, the strategy is only used in case clipping
+            would happen.
+        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
+            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
+            with extra headroom to avoid clipping. 'clip' just clips.
+        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
+        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
+            than the `peak_clip` one to avoid further clipping.
+        loudness_headroom_db (float): Target loudness for loudness normalization.
+        log_clipping (bool): If True, basic logging on stderr when clipping still
+            occurs despite strategy (only for 'rms').
+        sample_rate (int): Sample rate for the audio data (required for loudness).
+        stem_name (Optional[str]): Stem name for clipping logging.
+    Returns:
+        torch.Tensor: Normalized audio.
+    """
+    scale_peak = 10 ** (-peak_clip_headroom_db / 20)
+    scale_rms = 10 ** (-rms_headroom_db / 20)
+    if strategy == 'peak':
+        rescaling = (scale_peak / wav.abs().max())
+        if normalize or rescaling < 1:
+            wav = wav * rescaling
+    elif strategy == 'clip':
+        wav = wav.clamp(-scale_peak, scale_peak)
+    elif strategy == 'rms':
+        mono = wav.mean(dim=0)
+        rescaling = scale_rms / mono.pow(2).mean().sqrt()
+        if normalize or rescaling < 1:
+            wav = wav * rescaling
+        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
+    elif strategy == 'loudness':
+        assert sample_rate is not None, "Loudness normalization requires sample rate."
+        wav = normalize_loudness(wav, sample_rate, loudness_headroom_db)
+        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
+    else:
+        assert wav.abs().max() < 1
+        assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
+    return wav
+
+
+def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
+    """Convert audio to float 32 bits PCM format.
+    """
+    if wav.dtype.is_floating_point:
+        return wav
+    else:
+        assert wav.dtype == torch.int16
+        return wav.float() / 2**15
+
+
+def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
+    """Convert audio to int 16 bits PCM format.
+
+    ..Warning:: There exist many formula for doing this convertion. None are perfect
+    due to the asymetry of the int16 range. One either have possible clipping, DC offset,
+    or inconsistancies with f32_pcm. If the given wav doesn't have enough headroom,
+    it is possible that `i16_pcm(f32_pcm)) != Identity`.
+    """
+    if wav.dtype.is_floating_point:
+        assert wav.abs().max() <= 1
+        candidate = (wav * 2 ** 15).round()
+        if candidate.max() >= 2 ** 15:  # clipping would occur
+            candidate = (wav * (2 ** 15 - 1)).round()
+        return candidate.short()
+    else:
+        assert wav.dtype == torch.int16
+        return wav

audiocraft/data/zip.py

@@ -0,0 +1,74 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import typing
+import zipfile
+
+from dataclasses import dataclass
+from functools import lru_cache
+from typing_extensions import Literal
+
+
+DEFAULT_SIZE = 32
+MODE = Literal['r', 'w', 'x', 'a']
+
+
+@dataclass(order=True)
+class PathInZip:
+    """Class for holding a path of file within a zip file.
+
+    Args:
+        path: The convention is <path_to_zip>:<relative_path_inside_zip>
+            Let's assume there is a zip file /some/location/foo.zip
+            and inside of it is a json file located at /data/file1.json,
+            Then we expect path = "/some/location/foo.zip:/data/file1.json"
+    """
+
+    INFO_PATH_SEP = ':'
+    zip_path: str
+    file_path: str
+
+    def __init__(self, path: str) -> None:
+        split_path = path.split(self.INFO_PATH_SEP)
+        assert len(split_path) == 2
+        self.zip_path, self.file_path = split_path
+
+    @classmethod
+    def from_paths(cls, zip_path: str, file_path: str):
+        return cls(zip_path + cls.INFO_PATH_SEP + file_path)
+
+    def __str__(self) -> str:
+        return self.zip_path + self.INFO_PATH_SEP + self.file_path
+
+
+def _open_zip(path: str, mode: MODE = 'r'):
+    return zipfile.ZipFile(path, mode)
+
+
+_cached_open_zip = lru_cache(DEFAULT_SIZE)(_open_zip)
+
+
+def set_zip_cache_size(max_size: int):
+    """Sets the maximal LRU caching for zip file opening.
+
+    Args:
+        max_size: the maximal LRU cache.
+    """
+    global _cached_open_zip
+    _cached_open_zip = lru_cache(max_size)(_open_zip)
+
+
+def open_file_in_zip(path_in_zip: PathInZip, mode: str = 'r') -> typing.IO:
+    """Opens a file stored inside a zip and returns a file-like object.
+
+    Args:
+        path_in_zip: A PathInZip object representing the file to return a file-like object of.
+        mode: The mode in which to open the file with.
+    Returns:
+        A file-like object for PathInZip.
+    """
+    zf = _cached_open_zip(path_in_zip.zip_path)
+    return zf.open(path_in_zip.file_path)

audiocraft/models/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# flake8: noqa
+from .musicgen import MusicGen
+from .lm import LMModel
+from .encodec import CompressionModel, EncodecModel

audiocraft/models/builders.py

@@ -0,0 +1,218 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+All the functions to build the relevant models and modules
+from the Hydra config.
+"""
+
+import typing as tp
+import warnings
+
+import audiocraft
+import omegaconf
+import torch
+
+from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel  # noqa
+from .lm import LMModel
+from ..modules.codebooks_patterns import (
+    CodebooksPatternProvider,
+    DelayedPatternProvider,
+    ParallelPatternProvider,
+    UnrolledPatternProvider,
+    VALLEPattern,
+    MusicLMPattern,
+)
+from ..modules.conditioners import (
+    BaseConditioner,
+    ConditioningProvider,
+    LUTConditioner,
+    T5Conditioner,
+    ConditionFuser,
+    ChromaStemConditioner,
+)
+from .. import quantization as qt
+from ..utils.utils import dict_from_config
+
+
+def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
+    klass = {
+        'no_quant': qt.DummyQuantizer,
+        'rvq': qt.ResidualVectorQuantizer
+    }[quantizer]
+    kwargs = dict_from_config(getattr(cfg, quantizer))
+    if quantizer != 'no_quant':
+        kwargs['dimension'] = dimension
+    return klass(**kwargs)
+
+
+def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
+    if encoder_name == 'seanet':
+        kwargs = dict_from_config(getattr(cfg, 'seanet'))
+        encoder_override_kwargs = kwargs.pop('encoder')
+        decoder_override_kwargs = kwargs.pop('decoder')
+        encoder_kwargs = {**kwargs, **encoder_override_kwargs}
+        decoder_kwargs = {**kwargs, **decoder_override_kwargs}
+        encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
+        decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
+        return encoder, decoder
+    else:
+        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
+
+
+def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
+    """Instantiate a compression model.
+    """
+    if cfg.compression_model == 'encodec':
+        kwargs = dict_from_config(getattr(cfg, 'encodec'))
+        encoder_name = kwargs.pop('autoencoder')
+        quantizer_name = kwargs.pop('quantizer')
+        encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
+        quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
+        frame_rate = kwargs['sample_rate'] // encoder.hop_length
+        renormalize = kwargs.pop('renormalize', None)
+        renorm = kwargs.pop('renorm')
+        if renormalize is None:
+            renormalize = renorm is not None
+            warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.")
+        return EncodecModel(encoder, decoder, quantizer,
+                            frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
+    else:
+        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
+
+
+def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
+    """Instantiate a transformer LM.
+    """
+    if cfg.lm_model == 'transformer_lm':
+        kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
+        n_q = kwargs['n_q']
+        q_modeling = kwargs.pop('q_modeling', None)
+        codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
+        attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
+        cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
+        cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"]
+        fuser = get_condition_fuser(cfg)
+        condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
+        if len(fuser.fuse2cond['cross']) > 0:  # enforce cross-att programatically
+            kwargs['cross_attention'] = True
+        if codebooks_pattern_cfg.modeling is None:
+            assert q_modeling is not None, \
+                'LM model should either have a codebook pattern defined or transformer_lm.q_modeling'
+            codebooks_pattern_cfg = omegaconf.OmegaConf.create(
+                {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
+            )
+        pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
+        return LMModel(
+            pattern_provider=pattern_provider,
+            condition_provider=condition_provider,
+            fuser=fuser,
+            cfg_dropout=cfg_prob,
+            cfg_coef=cfg_coef,
+            attribute_dropout=attribute_dropout,
+            dtype=getattr(torch, cfg.dtype),
+            device=cfg.device,
+            **kwargs
+        ).to(cfg.device)
+    else:
+        raise KeyError(f'Unexpected LM model {cfg.lm_model}')
+
+
+def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
+    """Instantiate a conditioning model.
+    """
+    device = cfg.device
+    duration = cfg.dataset.segment_duration
+    cfg = getattr(cfg, "conditioners")
+    cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg
+    conditioners: tp.Dict[str, BaseConditioner] = {}
+    with omegaconf.open_dict(cfg):
+        condition_provider_args = cfg.pop('args', {})
+    for cond, cond_cfg in cfg.items():
+        model_type = cond_cfg["model"]
+        model_args = cond_cfg[model_type]
+        if model_type == "t5":
+            conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
+        elif model_type == "lut":
+            conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
+        elif model_type == "chroma_stem":
+            model_args.pop('cache_path', None)
+            conditioners[str(cond)] = ChromaStemConditioner(
+                output_dim=output_dim,
+                duration=duration,
+                device=device,
+                **model_args
+            )
+        else:
+            raise ValueError(f"unrecognized conditioning model: {model_type}")
+    conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
+    return conditioner
+
+
+def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
+    """Instantiate a condition fuser object.
+    """
+    fuser_cfg = getattr(cfg, "fuser")
+    fuser_methods = ["sum", "cross", "prepend", "input_interpolate"]
+    fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
+    kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
+    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
+    return fuser
+
+
+def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
+    """Instantiate a codebooks pattern provider object.
+    """
+    pattern_providers = {
+        'parallel': ParallelPatternProvider,
+        'delay': DelayedPatternProvider,
+        'unroll': UnrolledPatternProvider,
+        'valle': VALLEPattern,
+        'musiclm': MusicLMPattern,
+    }
+    name = cfg.modeling
+    kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
+    klass = pattern_providers[name]
+    return klass(n_q, **kwargs)
+
+
+def get_debug_compression_model(device='cpu'):
+    """Instantiate a debug compression model to be used for unit tests.
+    """
+    seanet_kwargs = {
+        'n_filters': 4,
+        'n_residual_layers': 1,
+        'dimension': 32,
+        'ratios': [10, 8, 16]  # 25 Hz at 32kHz
+    }
+    encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
+    decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
+    quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
+    init_x = torch.randn(8, 32, 128)
+    quantizer(init_x, 1)  # initialize kmeans etc.
+    compression_model = EncodecModel(
+        encoder, decoder, quantizer,
+        frame_rate=25, sample_rate=32000, channels=1).to(device)
+    return compression_model.eval()
+
+
+def get_debug_lm_model(device='cpu'):
+    """Instantiate a debug LM to be used for unit tests.
+    """
+    pattern = DelayedPatternProvider(n_q=4)
+    dim = 16
+    providers = {
+        'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
+    }
+    condition_provider = ConditioningProvider(providers)
+    fuser = ConditionFuser(
+        {'cross': ['description'], 'prepend': [],
+         'sum': [], 'input_interpolate': []})
+    lm = LMModel(
+        pattern, condition_provider, fuser,
+        n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
+        cross_attention=True, causal=True)
+    return lm.to(device).eval()

audiocraft/models/encodec.py

@@ -0,0 +1,302 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+import typing as tp
+
+from einops import rearrange
+import torch
+from torch import nn
+
+from .. import quantization as qt
+
+
+class CompressionModel(ABC, nn.Module):
+
+    @abstractmethod
+    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
+        ...
+
+    @abstractmethod
+    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
+        """See `EncodecModel.encode`"""
+        ...
+
+    @abstractmethod
+    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
+        """See `EncodecModel.decode`"""
+        ...
+
+    @property
+    @abstractmethod
+    def channels(self) -> int:
+        ...
+
+    @property
+    @abstractmethod
+    def frame_rate(self) -> int:
+        ...
+
+    @property
+    @abstractmethod
+    def sample_rate(self) -> int:
+        ...
+
+    @property
+    @abstractmethod
+    def cardinality(self) -> int:
+        ...
+
+    @property
+    @abstractmethod
+    def num_codebooks(self) -> int:
+        ...
+
+    @property
+    @abstractmethod
+    def total_codebooks(self) -> int:
+        ...
+
+    @abstractmethod
+    def set_num_codebooks(self, n: int):
+        """Set the active number of codebooks used by the quantizer.
+        """
+        ...
+
+
+class EncodecModel(CompressionModel):
+    """Encodec model operating on the raw waveform.
+
+    Args:
+        encoder (nn.Module): Encoder network.
+        decoder (nn.Module): Decoder network.
+        quantizer (qt.BaseQuantizer): Quantizer network.
+        frame_rate (int): Frame rate for the latent representation.
+        sample_rate (int): Audio sample rate.
+        channels (int): Number of audio channels.
+        causal (bool): Whether to use a causal version of the model.
+        renormalize (bool): Whether to renormalize the audio before running the model.
+    """
+    # we need assignement to override the property in the abstract class,
+    # I couldn't find a better way...
+    frame_rate: int = 0
+    sample_rate: int = 0
+    channels: int = 0
+
+    def __init__(self,
+                 encoder: nn.Module,
+                 decoder: nn.Module,
+                 quantizer: qt.BaseQuantizer,
+                 frame_rate: int,
+                 sample_rate: int,
+                 channels: int,
+                 causal: bool = False,
+                 renormalize: bool = False):
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.quantizer = quantizer
+        self.frame_rate = frame_rate
+        self.sample_rate = sample_rate
+        self.channels = channels
+        self.renormalize = renormalize
+        self.causal = causal
+        if self.causal:
+            # we force disabling here to avoid handling linear overlap of segments
+            # as supported in original EnCodec codebase.
+            assert not self.renormalize, 'Causal model does not support renormalize'
+
+    @property
+    def total_codebooks(self):
+        """Total number of quantizer codebooks available.
+        """
+        return self.quantizer.total_codebooks
+
+    @property
+    def num_codebooks(self):
+        """Active number of codebooks used by the quantizer.
+        """
+        return self.quantizer.num_codebooks
+
+    def set_num_codebooks(self, n: int):
+        """Set the active number of codebooks used by the quantizer.
+        """
+        self.quantizer.set_num_codebooks(n)
+
+    @property
+    def cardinality(self):
+        """Cardinality of each codebook.
+        """
+        return self.quantizer.bins
+
+    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
+        scale: tp.Optional[torch.Tensor]
+        if self.renormalize:
+            mono = x.mean(dim=1, keepdim=True)
+            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
+            scale = 1e-8 + volume
+            x = x / scale
+            scale = scale.view(-1, 1)
+        else:
+            scale = None
+        return x, scale
+
+    def postprocess(self,
+                    x: torch.Tensor,
+                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
+        if scale is not None:
+            assert self.renormalize
+            x = x * scale.view(-1, 1, 1)
+        return x
+
+    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
+        assert x.dim() == 3
+        length = x.shape[-1]
+        x, scale = self.preprocess(x)
+
+        emb = self.encoder(x)
+        q_res = self.quantizer(emb, self.frame_rate)
+        out = self.decoder(q_res.x)
+
+        # remove extra padding added by the encoder and decoder
+        assert out.shape[-1] >= length, (out.shape[-1], length)
+        out = out[..., :length]
+
+        q_res.x = self.postprocess(out, scale)
+
+        return q_res
+
+    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
+        """Encode the given input tensor to quantized representation along with scale parameter.
+
+        Args:
+            x (torch.Tensor): Float tensor of shape [B, C, T]
+
+        Returns:
+            codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
+                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
+                scale a float tensor containing the scale for audio renormalizealization.
+        """
+        assert x.dim() == 3
+        x, scale = self.preprocess(x)
+        emb = self.encoder(x)
+        codes = self.quantizer.encode(emb)
+        return codes, scale
+
+    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
+        """Decode the given codes to a reconstructed representation, using the scale to perform
+        audio denormalization if needed.
+
+        Args:
+            codes (torch.Tensor): Int tensor of shape [B, K, T]
+            scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
+
+        Returns:
+            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
+        """
+        emb = self.quantizer.decode(codes)
+        out = self.decoder(emb)
+        out = self.postprocess(out, scale)
+        # out contains extra padding added by the encoder and decoder
+        return out
+
+
+class FlattenedCompressionModel(CompressionModel):
+    """Wraps a CompressionModel and flatten its codebooks, e.g.
+    instead of returning [B, K, T], return [B, S, T * (K // S)] with
+    S the number of codebooks per step, and `K // S` the number of 'virtual steps'
+    for each real time step.
+
+    Args:
+        model (CompressionModel): compression model to wrap.
+        codebooks_per_step (int): number of codebooks to keep per step,
+            this must divide the number of codebooks provided by the wrapped model.
+        extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
+            if each codebook has a cardinality N, then the first codebook will
+            use the range [0, N - 1], and the second [N, 2 N - 1] etc.
+            On decoding, this can lead to potentially invalid sequences.
+            Any invalid entry will be silently remapped to the proper range
+            with a modulo.
+    """
+    def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
+                 extend_cardinality: bool = True):
+        super().__init__()
+        self.model = model
+        self.codebooks_per_step = codebooks_per_step
+        self.extend_cardinality = extend_cardinality
+
+    @property
+    def total_codebooks(self):
+        return self.model.total_codebooks
+
+    @property
+    def num_codebooks(self):
+        """Active number of codebooks used by the quantizer.
+
+        ..Warning:: this reports the number of codebooks after the flattening
+        of the codebooks!
+        """
+        assert self.model.num_codebooks % self.codebooks_per_step == 0
+        return self.codebooks_per_step
+
+    def set_num_codebooks(self, n: int):
+        """Set the active number of codebooks used by the quantizer.
+
+        ..Warning:: this sets the number of codebooks **before** the flattening
+        of the codebooks.
+        """
+        assert n % self.codebooks_per_step == 0
+        self.model.set_num_codebooks(n)
+
+    @property
+    def num_virtual_steps(self) -> int:
+        """Return the number of virtual steps, e.g. one real step
+        will be split into that many steps.
+        """
+        return self.model.num_codebooks // self.codebooks_per_step
+
+    @property
+    def frame_rate(self) -> int:
+        return self.model.frame_rate * self.num_virtual_steps
+
+    @property
+    def sample_rate(self) -> int:
+        return self.model.sample_rate
+
+    @property
+    def channels(self) -> int:
+        return self.model.channels
+
+    @property
+    def cardinality(self):
+        """Cardinality of each codebook.
+        """
+        if self.extend_cardinality:
+            return self.model.cardinality * self.num_virtual_steps
+        else:
+            return self.model.cardinality
+
+    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
+        raise NotImplementedError("Not supported, use encode and decode.")
+
+    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
+        indices, scales = self.model.encode(x)
+        B, K, T = indices.shape
+        indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
+        if self.extend_cardinality:
+            for virtual_step in range(1, self.num_virtual_steps):
+                indices[..., virtual_step] += self.model.cardinality * virtual_step
+        indices = rearrange(indices, 'b k t v -> b k (t v)')
+        return (indices, scales)
+
+    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
+        B, K, T = codes.shape
+        assert T % self.num_virtual_steps == 0
+        codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
+        # We silently ignore potential errors from the LM when
+        # using extend_cardinality.
+        codes = codes % self.model.cardinality
+        return self.model.decode(codes, scale)

audiocraft/models/lm.py

@@ -0,0 +1,526 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+from functools import partial
+import logging
+import math
+import typing as tp
+
+import torch
+from torch import nn
+
+from ..utils import utils
+from ..modules.streaming import StreamingModule, State
+from ..modules.transformer import StreamingTransformer, create_norm_fn
+from ..modules.conditioners import (
+    ConditionFuser,
+    ClassifierFreeGuidanceDropout,
+    AttributeDropout,
+    ConditioningProvider,
+    ConditioningAttributes,
+    ConditionType,
+)
+from ..modules.codebooks_patterns import CodebooksPatternProvider
+from ..modules.activations import get_activation_fn
+
+
+logger = logging.getLogger(__name__)
+ConditionTensors = tp.Dict[str, ConditionType]
+CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
+
+
+def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
+    """LM layer initialization.
+    Inspired from xlformers: https://github.com/fairinternal/xlformers
+
+    Args:
+        method (str): Method name for init function. Valid options are:
+            'gaussian', 'uniform'.
+        input_dim (int): Input dimension of the initialized module.
+        init_depth (Optional[int]): Optional init depth value used to rescale
+            the standard deviation if defined.
+    """
+    # Compute std
+    std = 1 / math.sqrt(input_dim)
+    # Rescale with depth
+    if init_depth is not None:
+        std = std / math.sqrt(2 * init_depth)
+
+    if method == 'gaussian':
+        return partial(
+            torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
+        )
+    elif method == 'uniform':
+        bound = math.sqrt(3) * std  # ensure the standard deviation is `std`
+        return partial(torch.nn.init.uniform_, a=-bound, b=bound)
+    else:
+        raise ValueError("Unsupported layer initialization method")
+
+
+def init_layer(m: nn.Module,
+               method: str,
+               init_depth: tp.Optional[int] = None,
+               zero_bias_init: bool = False):
+    """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
+
+    Args:
+        m (nn.Module): Module to initialize.
+        method (str): Method name for the init function.
+        init_depth (Optional[int]): Optional init depth value used to rescale
+            the standard deviation if defined.
+        zero_bias_init (bool): Whether to initialize the bias to 0 or not.
+    """
+    if isinstance(m, nn.Linear):
+        init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
+        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
+            weight = m.weight.float()
+            init_fn(weight)
+            m.weight.data[:] = weight.half()
+        else:
+            init_fn(m.weight)
+        if zero_bias_init and m.bias is not None:
+            nn.init.constant_(m.bias, 0)
+    elif isinstance(m, nn.Embedding):
+        init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
+        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
+            weight = m.weight.float()
+            init_fn(weight)
+            m.weight.data[:] = weight.half()
+        else:
+            init_fn(m.weight)
+
+
+class ScaledEmbedding(nn.Embedding):
+    """Boost learning rate for embeddings (with `scale`).
+    """
+    def __init__(self, *args, lr=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lr = lr
+
+    def make_optim_group(self):
+        group = {"params": list(self.parameters())}
+        if self.lr is not None:
+            group["lr"] = self.lr
+        return group
+
+
+@dataclass
+class LMOutput:
+    # The logits are already re-aligned with the input codes
+    # hence no extra shift is required, e.g. when computing CE
+    logits: torch.Tensor  # [B, K, T, card]
+    mask: torch.Tensor  # [B, K, T]
+
+
+class LMModel(StreamingModule):
+    """Transformer-based language model on multiple streams of codes.
+
+    Args:
+        pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
+        condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
+        fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
+        n_q (int): Number of parallel streams to model.
+        card (int): Cardinality, vocabulary size.
+        dim (int): Dimension of the transformer encoder.
+        num_heads (int): Number of heads for the transformer encoder.
+        hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
+        norm (str): Normalization method.
+        norm_first (bool): Use pre-norm instead of post-norm.
+        emb_lr (Optional[float]): Embedding-specific learning rate.
+        bias_proj (bool): Use bias for output projections.
+        weight_init (Optional[str]): Method for weight initialization.
+        depthwise_init (Optional[str]): Method for depthwise weight initialization.
+        zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
+        cfg_dropout (float): Classifier-free guidance dropout.
+        cfg_coef (float): Classifier-free guidance coefficient.
+        attribute_dropout (dict): Attribute dropout probabilities.
+        two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
+        **kwargs: Additional parameters for the transformer encoder.
+    """
+    def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
+                 fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
+                 hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
+                 emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
+                 weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
+                 zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
+                 attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
+                 **kwargs):
+        super().__init__()
+        self.cfg_coef = cfg_coef
+        self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
+        self.att_dropout = AttributeDropout(p=attribute_dropout)
+        self.condition_provider = condition_provider
+        self.fuser = fuser
+        self.card = card
+        embed_dim = self.card + 1
+        self.n_q = n_q
+        self.dim = dim
+        self.pattern_provider = pattern_provider
+        self.two_step_cfg = two_step_cfg
+        self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
+        if 'activation' in kwargs:
+            kwargs['activation'] = get_activation_fn(kwargs['activation'])
+        self.transformer = StreamingTransformer(
+            d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
+            norm=norm, norm_first=norm_first, **kwargs)
+        self.out_norm: tp.Optional[nn.Module] = None
+        if norm_first:
+            self.out_norm = create_norm_fn(norm, dim)
+        self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
+        self._init_weights(weight_init, depthwise_init, zero_bias_init)
+        self._fsdp: tp.Optional[nn.Module]
+        self.__dict__['_fsdp'] = None
+
+    def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
+        """Initialization of the transformer module weights.
+
+        Args:
+            weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
+            depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
+                'current' where the depth corresponds to the current layer index or 'global' where the total number
+                of layer is used as depth. If not set, no depthwise initialization strategy is used.
+            zero_bias_init (bool): Whether to initalize bias to zero or not.
+        """
+        assert depthwise_init is None or depthwise_init in ['current', 'global']
+        assert depthwise_init is None or weight_init is not None, \
+            "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
+        assert not zero_bias_init or weight_init is not None, \
+            "If 'zero_bias_init', a 'weight_init' method should be provided"
+
+        if weight_init is None:
+            return
+
+        for emb_layer in self.emb:
+            init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
+
+        for layer_idx, tr_layer in enumerate(self.transformer.layers):
+            depth = None
+            if depthwise_init == 'current':
+                depth = layer_idx + 1
+            elif depthwise_init == 'global':
+                depth = len(self.transformer.layers)
+            init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
+            tr_layer.apply(init_fn)
+
+        for linear in self.linears:
+            init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
+
+    @property
+    def special_token_id(self) -> int:
+        return self.card
+
+    @property
+    def num_codebooks(self) -> int:
+        return self.n_q
+
+    def forward(self, sequence: torch.Tensor,
+                conditions: tp.List[ConditioningAttributes],
+                condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
+        """Apply language model on sequence and conditions.
+        Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
+        S the sequence steps, return the logits with shape [B, card, K, S].
+
+        Args:
+            indices (torch.Tensor): indices of the codes to model.
+            conditions (list[ConditioningAttributes]): conditionings to use when modeling
+                the given codes. Note that when evaluating multiple time with the same conditioning
+                you should pre-compute those and pass them as `condition_tensors`.
+            condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
+                tensors, see `conditions`.
+        Returns:
+            torch.Tensor: Logits.
+        """
+        B, K, S = sequence.shape
+        assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
+        input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
+        if condition_tensors is None:
+            assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
+            # apply dropout modules
+            conditions = self.cfg_dropout(conditions)
+            conditions = self.att_dropout(conditions)
+            tokenized = self.condition_provider.tokenize(conditions)
+            # encode conditions and fuse, both have a streaming cache to not recompute when generating.
+            condition_tensors = self.condition_provider(tokenized)
+        else:
+            assert not conditions, "Shouldn't pass both conditions and condition_tensors."
+
+        input_, cross_attention_input = self.fuser(input_, condition_tensors)
+
+        out = self.transformer(input_, cross_attention_src=cross_attention_input)
+        if self.out_norm:
+            out = self.out_norm(out)
+        logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1)  # [B, K, S, card]
+
+        # remove the prefix from the model outputs
+        if len(self.fuser.fuse2cond['prepend']) > 0:
+            logits = logits[:, :, -S:]
+
+        return logits  # [B, K, S, card]
+
+    def compute_predictions(
+            self, codes: torch.Tensor,
+            conditions: tp.List[ConditioningAttributes],
+            condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
+        """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
+        forward using the specified codes interleaving pattern.
+
+        Args:
+            codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
+                K the number of codebooks and T the number of timesteps.
+            conditions (list[ConditioningAttributes]): conditionings to use when modeling
+                the given codes. Note that when evaluating multiple time with the same conditioning
+                you should pre-compute those and pass them as `condition_tensors`.
+            condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
+                tensors, see `conditions`.
+        Returns:
+            LMOutput: Language model outputs
+                logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
+                    i.e. the first item corresponds to logits to predict the first code, meaning that
+                    no additional shifting of codes and logits is required.
+                mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
+                    Given the specified interleaving strategies, parts of the logits and codes should
+                    not be considered as valid predictions because of invalid context.
+        """
+        B, K, T = codes.shape
+        codes = codes.contiguous()
+        # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
+        pattern = self.pattern_provider.get_pattern(T)
+        sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
+            codes, self.special_token_id, keep_only_valid_steps=True
+        )
+        # apply model on pattern sequence
+        model = self if self._fsdp is None else self._fsdp
+        logits = model(sequence_codes, conditions, condition_tensors)  # [B, K, S, card]
+        # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
+        # and provide the corresponding mask over invalid positions of tokens
+        logits = logits.permute(0, 3, 1, 2)  # [B, card, K, S]
+        # note: we use nans as special token to make it obvious if we feed unexpected logits
+        logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
+            logits, float('nan'), keep_only_valid_steps=True
+        )
+        logits = logits.permute(0, 2, 3, 1)  # [B, K, T, card]
+        logits_mask = logits_mask[None, :, :].expand(B, -1, -1)  # [K, T] -> [B, K, T]
+        return LMOutput(logits, logits_mask)
+
+    def _sample_next_token(self,
+                           sequence: torch.Tensor,
+                           cfg_conditions: CFGConditions,
+                           unconditional_state: State,
+                           use_sampling: bool = False,
+                           temp: float = 1.0,
+                           top_k: int = 0,
+                           top_p: float = 0.0,
+                           cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
+        """Sample next token from the model given a sequence and a set of conditions. The model supports
+        multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
+
+        Args:
+            sequence (torch.Tensor): Current sequence of shape [B, K, S]
+                with K corresponding to the number of codebooks and S the number of sequence steps.
+                S = 1 in streaming mode, except for the first step that contains a bigger prompt.
+            condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
+                should be twice the batch size, being the concatenation of the conditions + null conditions.
+            use_sampling (bool): Whether to use a sampling strategy or not.
+            temp (float): Sampling temperature.
+            top_k (int): K for "top-k" sampling.
+            top_p (float): P for "top-p" sampling.
+            cfg_coef (float): classifier free guidance coefficient
+        Returns:
+            next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
+        """
+        B = sequence.shape[0]
+        cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
+        model = self if self._fsdp is None else self._fsdp
+        if self.two_step_cfg and cfg_conditions != {}:
+            assert isinstance(cfg_conditions, tuple)
+            condition_tensors, null_condition_tensors = cfg_conditions
+            cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
+            state = self.get_streaming_state()
+            self.set_streaming_state(unconditional_state)
+            uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
+            unconditional_state.update(self.get_streaming_state())
+            self.set_streaming_state(state)
+            logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
+        else:
+            assert isinstance(cfg_conditions, dict)
+            condition_tensors = cfg_conditions
+            if condition_tensors:
+                # Preparing for CFG, predicting both conditional and unconditional logits.
+                sequence = torch.cat([sequence, sequence], dim=0)
+            all_logits = model(
+                sequence,
+                conditions=[], condition_tensors=condition_tensors)
+            if condition_tensors:
+                cond_logits, uncond_logits = all_logits.split(B, dim=0)  # [B, K, T, card]
+                logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
+            else:
+                logits = all_logits
+
+        logits = logits.permute(0, 1, 3, 2)  # [B, K, card, T]
+        logits = logits[..., -1]  # [B x K x card]
+
+        if use_sampling:
+            probs = torch.softmax(logits / temp, dim=-1)
+            if top_p > 0.0:
+                next_token = utils.sample_top_p(probs, p=top_p)
+            elif top_k > 0:
+                next_token = utils.sample_top_k(probs, k=top_k)
+            else:
+                next_token = utils.multinomial(probs, num_samples=1)
+        else:
+            next_token = torch.argmax(logits, dim=-1, keepdim=True)
+
+        return next_token
+
+    @torch.no_grad()
+    def generate(self,
+                 prompt: tp.Optional[torch.Tensor] = None,
+                 conditions: tp.List[ConditioningAttributes] = [],
+                 num_samples: tp.Optional[int] = None,
+                 max_gen_len: int = 256,
+                 use_sampling: bool = True,
+                 temp: float = 1.0,
+                 top_k: int = 250,
+                 top_p: float = 0.0,
+                 cfg_coef: tp.Optional[float] = None,
+                 two_step_cfg: bool = False,
+                 remove_prompts: bool = False,
+                 check: bool = False,
+                 callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
+        """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
+        be perform in a greedy fashion or using sampling with top K and top P strategies.
+
+        Args:
+            prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
+            conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
+            num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
+            max_gen_len (int): Maximum generation length.
+            use_sampling (bool): Whether to use a sampling strategy or not.
+            temp (float): Sampling temperature.
+            top_k (int): K for "top-k" sampling.
+            top_p (float): P for "top-p" sampling.
+            remove_prompts (bool): Whether to remove prompts from generation or not.
+        Returns:
+            torch.Tensor: Generated tokens.
+        """
+        assert not self.training, "generation shouldn't be used in training mode."
+        first_param = next(iter(self.parameters()))
+        device = first_param.device
+
+        # Checking all input shapes are consistents.
+        possible_num_samples = []
+        if num_samples is not None:
+            possible_num_samples.append(num_samples)
+        elif prompt is not None:
+            possible_num_samples.append(prompt.shape[0])
+        elif conditions:
+            possible_num_samples.append(len(conditions))
+        else:
+            possible_num_samples.append(1)
+        assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
+        num_samples = possible_num_samples[0]
+
+        # below we create set of conditions: one conditional and one unconditional
+        # to do that we merge the regular condition together with the null condition
+        # we then do 1 forward pass instead of 2.
+        # the reason for that is two-fold:
+        # 1. it is about x2 faster than doing 2 forward passes
+        # 2. avoid the streaming API treating the 2 passes as part of different time steps
+        # We also support doing two different passes, in particular to ensure that
+        # the padding structure is exactly the same between train anf test.
+        # With a batch size of 1, this can be slower though.
+        cfg_conditions: CFGConditions
+        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
+        if conditions:
+            null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
+            if two_step_cfg:
+                cfg_conditions = (
+                    self.condition_provider(self.condition_provider.tokenize(conditions)),
+                    self.condition_provider(self.condition_provider.tokenize(null_conditions)),
+                )
+            else:
+                conditions = conditions + null_conditions
+                tokenized = self.condition_provider.tokenize(conditions)
+                cfg_conditions = self.condition_provider(tokenized)
+        else:
+            cfg_conditions = {}
+
+        if prompt is None:
+            assert num_samples > 0
+            prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
+
+        B, K, T = prompt.shape
+        start_offset = T
+        assert start_offset < max_gen_len
+
+        pattern = self.pattern_provider.get_pattern(max_gen_len)
+        # this token is used as default value for codes that are not generated yet
+        unknown_token = -1
+
+        # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
+        gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
+        # filling the gen_codes with the prompt if needed
+        gen_codes[..., :start_offset] = prompt
+        # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
+        gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
+        # retrieve the start_offset in the sequence:
+        # it is the first sequence step that contains the `start_offset` timestep
+        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
+        assert start_offset_sequence is not None
+
+        with self.streaming():
+            unconditional_state = self.get_streaming_state()
+            prev_offset = 0
+            gen_sequence_len = gen_sequence.shape[-1]  # gen_sequence shape is [B, K, S]
+            for offset in range(start_offset_sequence, gen_sequence_len):
+                # get current sequence (note that the streaming API is providing the caching over previous offsets)
+                curr_sequence = gen_sequence[..., prev_offset:offset]
+                curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
+                if check:
+                    # check coherence between mask and sequence
+                    assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
+                    # should never happen as gen_sequence is filled progressively
+                    assert not (curr_sequence == unknown_token).any()
+                # sample next token from the model, next token shape is [B, K, 1]
+                next_token = self._sample_next_token(
+                    curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
+                    cfg_coef=cfg_coef)
+                # ensure the tokens that should be masked are properly set to special_token_id
+                # as the model never output special_token_id
+                valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
+                next_token[~valid_mask] = self.special_token_id
+                # ensure we don't overwrite prompt tokens, we only write over unknown tokens
+                # (then mask tokens should be left as is as well, which is correct)
+                gen_sequence[..., offset:offset+1] = torch.where(
+                    gen_sequence[..., offset:offset+1] == unknown_token,
+                    next_token, gen_sequence[..., offset:offset+1]
+                )
+                prev_offset = offset
+                if callback is not None:
+                    callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
+        unconditional_state.clear()
+
+        # ensure sequence has been entirely filled
+        assert not (gen_sequence == unknown_token).any()
+        # ensure gen_sequence pattern and mask are matching
+        # which means the gen_sequence is valid according to the pattern
+        assert (
+            gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
+        ).all()
+        # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
+        out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
+
+        # sanity checks over the returned codes and corresponding masks
+        assert (out_codes[..., :max_gen_len] != unknown_token).all()
+        assert (out_mask[..., :max_gen_len] == 1).all()
+
+        out_start_offset = start_offset if remove_prompts else 0
+        out_codes = out_codes[..., out_start_offset:max_gen_len]
+
+        # ensure the returned codes are all valid
+        assert (out_codes >= 0).all() and (out_codes <= self.card).all()
+        return out_codes

audiocraft/models/loaders.py

@@ -0,0 +1,92 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Utility functions to load from the checkpoints.
+Each checkpoint is a torch.saved dict with the following keys:
+- 'xp.cfg': the hydra config as dumped during training. This should be used
+    to rebuild the object using the audiocraft.models.builders functions,
+- 'model_best_state': a readily loadable best state for the model, including
+    the conditioner. The model obtained from `xp.cfg` should be compatible
+    with this state dict. In the case of a LM, the encodec model would not be
+    bundled along but instead provided separately.
+
+Those functions also support loading from a remote location with the Torch Hub API.
+They also support overriding some parameters, in particular the device and dtype
+of the returned model.
+"""
+
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+import typing as tp
+import os
+
+from omegaconf import OmegaConf
+import torch
+
+from . import builders
+
+
+HF_MODEL_CHECKPOINTS_MAP = {
+    "small": "facebook/musicgen-small",
+    "medium": "facebook/musicgen-medium",
+    "large": "facebook/musicgen-large",
+    "melody": "facebook/musicgen-melody",
+}
+
+
+def _get_state_dict(
+    file_or_url_or_id: tp.Union[Path, str],
+    filename: tp.Optional[str] = None,
+    device='cpu',
+    cache_dir: tp.Optional[str] = None,
+):
+    # Return the state dict either from a file or url
+    file_or_url_or_id = str(file_or_url_or_id)
+    assert isinstance(file_or_url_or_id, str)
+
+    if os.path.isfile(file_or_url_or_id):
+        return torch.load(file_or_url_or_id, map_location=device)
+
+    elif file_or_url_or_id.startswith('https://'):
+        return torch.hub.load_state_dict_from_url(file_or_url_or_id, map_location=device, check_hash=True)
+
+    elif file_or_url_or_id in HF_MODEL_CHECKPOINTS_MAP:
+        assert filename is not None, "filename needs to be defined if using HF checkpoints"
+
+        repo_id = HF_MODEL_CHECKPOINTS_MAP[file_or_url_or_id]
+        file = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
+        return torch.load(file, map_location=device)
+
+    else:
+        raise ValueError(f"{file_or_url_or_id} is not a valid name, path or link that can be loaded.")
+
+
+def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
+    pkg = _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir)
+    cfg = OmegaConf.create(pkg['xp.cfg'])
+    cfg.device = str(device)
+    model = builders.get_compression_model(cfg)
+    model.load_state_dict(pkg['best_state'])
+    model.eval()
+    return model
+
+
+def load_lm_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
+    pkg = _get_state_dict(file_or_url_or_id, filename="state_dict.bin", cache_dir=cache_dir)
+    cfg = OmegaConf.create(pkg['xp.cfg'])
+    cfg.device = str(device)
+    if cfg.device == 'cpu':
+        cfg.transformer_lm.memory_efficient = False
+        cfg.transformer_lm.custom = True
+        cfg.dtype = 'float32'
+    else:
+        cfg.dtype = 'float16'
+    model = builders.get_lm_model(cfg)
+    model.load_state_dict(pkg['best_state'])
+    model.eval()
+    model.cfg = cfg
+    return model

audiocraft/models/musicgen.py

@@ -0,0 +1,283 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Main model for using MusicGen. This will combine all the required components
+and provide easy access to the generation API.
+"""
+
+import os
+import typing as tp
+
+import torch
+
+from .encodec import CompressionModel
+from .lm import LMModel
+from .builders import get_debug_compression_model, get_debug_lm_model
+from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP
+from ..data.audio_utils import convert_audio
+from ..modules.conditioners import ConditioningAttributes, WavCondition
+from ..utils.autocast import TorchAutocast
+
+
+MelodyList = tp.List[tp.Optional[torch.Tensor]]
+MelodyType = tp.Union[torch.Tensor, MelodyList]
+
+
+class MusicGen:
+    """MusicGen main model with convenient generation API.
+
+    Args:
+        name (str): name of the model.
+        compression_model (CompressionModel): Compression model
+            used to map audio to invertible discrete representations.
+        lm (LMModel): Language model over discrete representations.
+    """
+    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel):
+        self.name = name
+        self.compression_model = compression_model
+        self.lm = lm
+        self.device = next(iter(lm.parameters())).device
+        self.generation_params: dict = {}
+        self.set_generation_params(duration=15)  # 15 seconds by default
+        if self.device.type == 'cpu':
+            self.autocast = TorchAutocast(enabled=False)
+        else:
+            self.autocast = TorchAutocast(
+                enabled=True, device_type=self.device.type, dtype=torch.float16)
+
+    @property
+    def frame_rate(self) -> int:
+        """Roughly the number of AR steps per seconds."""
+        return self.compression_model.frame_rate
+
+    @property
+    def sample_rate(self) -> int:
+        """Sample rate of the generated audio."""
+        return self.compression_model.sample_rate
+
+    @property
+    def audio_channels(self) -> int:
+        """Audio channels of the generated audio."""
+        return self.compression_model.channels
+
+    @staticmethod
+    def get_pretrained(name: str = 'melody', device='cuda'):
+        """Return pretrained model, we provide four models:
+        - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small
+        - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium
+        - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody
+        - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large
+        """
+
+        if name == 'debug':
+            # used only for unit tests
+            compression_model = get_debug_compression_model(device)
+            lm = get_debug_lm_model(device)
+            return MusicGen(name, compression_model, lm)
+
+        if name not in HF_MODEL_CHECKPOINTS_MAP:
+            raise ValueError(
+                f"{name} is not a valid checkpoint name. "
+                f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}"
+            )
+
+        cache_dir = os.environ.get('MUSICGEN_ROOT', None)
+        compression_model = load_compression_model(name, device=device, cache_dir=cache_dir)
+        lm = load_lm_model(name, device=device, cache_dir=cache_dir)
+
+        return MusicGen(name, compression_model, lm)
+
+    def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
+                              top_p: float = 0.0, temperature: float = 1.0,
+                              duration: float = 30.0, cfg_coef: float = 3.0,
+                              two_step_cfg: bool = False):
+        """Set the generation parameters for MusicGen.
+
+        Args:
+            use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
+            top_k (int, optional): top_k used for sampling. Defaults to 250.
+            top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
+            temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
+            duration (float, optional): Duration of the generated waveform. Defaults to 30.0.
+            cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
+            two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
+                instead of batching together the two. This has some impact on how things
+                are padded but seems to have little impact in practice.
+        """
+        assert duration <= 30, "The MusicGen cannot generate more than 30 seconds"
+        self.generation_params = {
+            'max_gen_len': int(duration * self.frame_rate),
+            'use_sampling': use_sampling,
+            'temp': temperature,
+            'top_k': top_k,
+            'top_p': top_p,
+            'cfg_coef': cfg_coef,
+            'two_step_cfg': two_step_cfg,
+        }
+
+    def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor:
+        """Generate samples in an unconditional manner.
+
+        Args:
+            num_samples (int): Number of samples to be generated.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        """
+        descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
+        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
+        return self._generate_tokens(attributes, prompt_tokens, progress)
+
+    def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor:
+        """Generate samples conditioned on text.
+
+        Args:
+            descriptions (tp.List[str]): A list of strings used as text conditioning.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        """
+        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
+        assert prompt_tokens is None
+        return self._generate_tokens(attributes, prompt_tokens, progress)
+
+    def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType,
+                             melody_sample_rate: int, progress: bool = False) -> torch.Tensor:
+        """Generate samples conditioned on text and melody.
+
+        Args:
+            descriptions (tp.List[str]): A list of strings used as text conditioning.
+            melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
+                melody conditioning. Should have shape [B, C, T] with B matching the description length,
+                C=1 or 2. It can be [C, T] if there is a single description. It can also be
+                a list of [C, T] tensors.
+            melody_sample_rate: (int): Sample rate of the melody waveforms.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        """
+        if isinstance(melody_wavs, torch.Tensor):
+            if melody_wavs.dim() == 2:
+                melody_wavs = melody_wavs[None]
+            if melody_wavs.dim() != 3:
+                raise ValueError("Melody wavs should have a shape [B, C, T].")
+            melody_wavs = list(melody_wavs)
+        else:
+            for melody in melody_wavs:
+                if melody is not None:
+                    assert melody.dim() == 2, "One melody in the list has the wrong number of dims."
+
+        melody_wavs = [
+            convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels)
+            if wav is not None else None
+            for wav in melody_wavs]
+        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
+                                                                        melody_wavs=melody_wavs)
+        assert prompt_tokens is None
+        return self._generate_tokens(attributes, prompt_tokens, progress)
+
+    def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
+                              descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
+                              progress: bool = False) -> torch.Tensor:
+        """Generate samples conditioned on audio prompts.
+
+        Args:
+            prompt (torch.Tensor): A batch of waveforms used for continuation.
+                Prompt should be [B, C, T], or [C, T] if only one sample is generated.
+            prompt_sample_rate (int): Sampling rate of the given audio waveforms.
+            descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        """
+        if prompt.dim() == 2:
+            prompt = prompt[None]
+        if prompt.dim() != 3:
+            raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
+        prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
+        if descriptions is None:
+            descriptions = [None] * len(prompt)
+        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
+        assert prompt_tokens is not None
+        return self._generate_tokens(attributes, prompt_tokens, progress)
+
+    @torch.no_grad()
+    def _prepare_tokens_and_attributes(
+            self,
+            descriptions: tp.Sequence[tp.Optional[str]],
+            prompt: tp.Optional[torch.Tensor],
+            melody_wavs: tp.Optional[MelodyList] = None,
+    ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
+        """Prepare model inputs.
+
+        Args:
+            descriptions (tp.List[str]): A list of strings used as text conditioning.
+            prompt (torch.Tensor): A batch of waveforms used for continuation.
+            melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms
+                used as melody conditioning. Defaults to None.
+        """
+        attributes = [
+            ConditioningAttributes(text={'description': description})
+            for description in descriptions]
+
+        if melody_wavs is None:
+            for attr in attributes:
+                attr.wav['self_wav'] = WavCondition(
+                    torch.zeros((1, 1), device=self.device),
+                    torch.tensor([0], device=self.device),
+                    path='null_wav')  # type: ignore
+        else:
+            if self.name != "melody":
+                raise RuntimeError("This model doesn't support melody conditioning. "
+                                   "Use the `melody` model.")
+            assert len(melody_wavs) == len(descriptions), \
+                f"number of melody wavs must match number of descriptions! " \
+                f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}"
+            for attr, melody in zip(attributes, melody_wavs):
+                if melody is None:
+                    attr.wav['self_wav'] = WavCondition(
+                        torch.zeros((1, 1), device=self.device),
+                        torch.tensor([0], device=self.device),
+                        path='null_wav')  # type: ignore
+                else:
+                    attr.wav['self_wav'] = WavCondition(
+                        melody.to(device=self.device),
+                        torch.tensor([melody.shape[-1]], device=self.device))
+
+        if prompt is not None:
+            if descriptions is not None:
+                assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
+            prompt = prompt.to(self.device)
+            prompt_tokens, scale = self.compression_model.encode(prompt)
+            assert scale is None
+        else:
+            prompt_tokens = None
+        return attributes, prompt_tokens
+
+    def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
+                         prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
+        """Generate discrete audio tokens given audio prompt and/or conditions.
+
+        Args:
+            attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody).
+            prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        Returns:
+            torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
+        """
+        def _progress_callback(generated_tokens: int, tokens_to_generate: int):
+            print(f'{generated_tokens: 6d} / {tokens_to_generate: 6d}', end='\r')
+
+        if prompt_tokens is not None:
+            assert self.generation_params['max_gen_len'] > prompt_tokens.shape[-1], \
+                "Prompt is longer than audio to generate"
+
+        callback = None
+        if progress:
+            callback = _progress_callback
+
+        # generate by sampling from LM
+        with self.autocast:
+            gen_tokens = self.lm.generate(prompt_tokens, attributes, callback=callback, **self.generation_params)
+
+        # generate audio
+        assert gen_tokens.dim() == 3
+        with torch.no_grad():
+            gen_audio = self.compression_model.decode(gen_tokens, None)
+        return gen_audio

audiocraft/modules/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# flake8: noqa
+from .conv import (
+    NormConv1d,
+    NormConv2d,
+    NormConvTranspose1d,
+    NormConvTranspose2d,
+    StreamableConv1d,
+    StreamableConvTranspose1d,
+    pad_for_conv1d,
+    pad1d,
+    unpad1d,
+)
+from .lstm import StreamableLSTM
+from .seanet import SEANetEncoder, SEANetDecoder

audiocraft/modules/activations.py

@@ -0,0 +1,96 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from typing import Union, Callable
+
+
+class CustomGLU(nn.Module):
+    """Custom Gated Linear Unit activation.
+    Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
+    of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
+    function (i.e. sigmoid, swish, etc.).
+
+    Args:
+        activation (nn.Module): The custom activation to apply in the Gated Linear Unit
+        dim (int): the dimension on which to split the input. Default: -1
+
+    Shape:
+        - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
+          dimensions
+        - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
+
+    Examples::
+        >>> m = CustomGLU(nn.Sigmoid())
+        >>> input = torch.randn(4, 2)
+        >>> output = m(input)
+    """
+    def __init__(self, activation: nn.Module, dim: int = -1):
+        super(CustomGLU, self).__init__()
+        self.dim = dim
+        self.activation = activation
+
+    def forward(self, x: Tensor):
+        assert x.shape[self.dim] % 2 == 0  # M = N / 2
+        a, b = torch.chunk(x, 2, dim=self.dim)
+        return a * self.activation(b)
+
+
+class SwiGLU(CustomGLU):
+    """SiLU Gated Linear Unit activation.
+    Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
+    the first half of the input matrices, :math:`b` is the second half.
+
+    Args:
+        dim (int): the dimension on which to split the input. Default: -1
+    """
+    def __init__(self, dim: int = -1):
+        super(SwiGLU, self).__init__(nn.SiLU(), dim)
+
+
+class GeGLU(CustomGLU):
+    """GeLU Gated Linear Unit activation.
+    Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
+    the first half of the input matrices, :math:`b` is the second half.
+
+    Args:
+        dim (int): the dimension on which to split the input. Default: -1
+    """
+    def __init__(self, dim: int = -1):
+        super(GeGLU, self).__init__(nn.GELU(), dim)
+
+
+class ReGLU(CustomGLU):
+    """ReLU Gated Linear Unit activation.
+    Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
+    the first half of the input matrices, :math:`b` is the second half.
+
+    Args:
+        dim (int): the dimension on which to split the input. Default: -1
+    """
+    def __init__(self, dim: int = -1):
+        super(ReGLU, self).__init__(nn.ReLU(), dim)
+
+
+def get_activation_fn(
+    activation: Union[str, Callable[[Tensor], Tensor]]
+) -> Union[str, Callable[[Tensor], Tensor]]:
+    """Helper function to map an activation string to the activation class.
+    If the supplied activation is not a string that is recognized, the activation is passed back.
+
+    Args:
+        activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check
+    """
+    if isinstance(activation, str):
+        if activation == "reglu":
+            return ReGLU()
+        elif activation == "geglu":
+            return GeGLU()
+        elif activation == "swiglu":
+            return SwiGLU()
+    return activation

audiocraft/modules/codebooks_patterns.py

@@ -0,0 +1,539 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import namedtuple
+from dataclasses import dataclass
+from functools import lru_cache
+import logging
+import typing as tp
+
+from abc import ABC, abstractmethod
+import torch
+
+LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
+PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Pattern:
+    """Base implementation of a pattern over a sequence with multiple codebooks.
+
+    The codebook pattern consists in a layout, defining for each sequence step
+    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
+    The first item of the pattern is always an empty list in order to properly insert a special token
+    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
+    and ``timesteps`` the number of timesteps corresponding to the original sequence.
+
+    The pattern provides convenient methods to build and revert interleaved sequences from it:
+    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
+        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
+        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
+        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
+        is returned along with a mask indicating valid tokens.
+    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
+        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
+        to fill and specify invalid positions if needed.
+    See the dedicated methods for more details.
+    """
+    # Pattern layout, for each sequence step, we have a list of coordinates
+    # corresponding to the original codebook timestep and position.
+    # The first list is always an empty list in order to properly insert
+    # a special token to start with.
+    layout: PatternLayout
+    timesteps: int
+    n_q: int
+
+    def __post_init__(self):
+        assert len(self.layout) > 0
+        assert self.layout[0] == []
+        self._validate_layout()
+        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
+        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
+        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
+
+    def _validate_layout(self):
+        """Runs checks on the layout to ensure a valid pattern is defined.
+        A pattern is considered invalid if:
+            - Multiple timesteps for a same codebook are defined in the same sequence step
+            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
+              (this would mean that we have future timesteps before past timesteps).
+        """
+        q_timesteps = {q: 0 for q in range(self.n_q)}
+        for s, seq_coords in enumerate(self.layout):
+            if len(seq_coords) > 0:
+                qs = set()
+                for coord in seq_coords:
+                    qs.add(coord.q)
+                    last_q_timestep = q_timesteps[coord.q]
+                    assert coord.t >= last_q_timestep, \
+                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
+                    q_timesteps[coord.q] = coord.t
+                # each sequence step contains at max 1 coordinate per codebook
+                assert len(qs) == len(seq_coords), \
+                    f"Multiple entries for a same codebook are found at step {s}"
+
+    @property
+    def num_sequence_steps(self):
+        return len(self.layout) - 1
+
+    @property
+    def max_delay(self):
+        max_t_in_seq_coords = 0
+        for seq_coords in self.layout[1:]:
+            for coords in seq_coords:
+                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
+        return max_t_in_seq_coords - self.timesteps
+
+    @property
+    def valid_layout(self):
+        valid_step = len(self.layout) - self.max_delay
+        return self.layout[:valid_step]
+
+    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
+        """Get codebook coordinates in the layout that corresponds to the specified timestep t
+        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
+        and the actual codebook coordinates.
+        """
+        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
+        if q is not None:
+            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
+        coords = []
+        for s, seq_codes in enumerate(self.layout):
+            for code in seq_codes:
+                if code.t == t and (q is None or code.q == q):
+                    coords.append((s, code))
+        return coords
+
+    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
+        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
+
+    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
+        steps_with_timesteps = self.get_steps_with_timestep(t, q)
+        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
+
+    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
+                                                device: tp.Union[torch.device, str] = 'cpu'):
+        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
+
+        Args:
+            timesteps (int): Maximum number of timesteps steps to consider.
+            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
+            device (Union[torch.device, str]): Device for created tensors.
+        Returns:
+            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
+        """
+        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
+        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
+        # use the proper layout based on whether we limit ourselves to valid steps only or not,
+        # note that using the valid_layout will result in a truncated sequence up to the valid steps
+        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
+        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
+        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
+        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
+        # fill indexes with last sequence step value that will correspond to our special token
+        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
+        # which will correspond to the index: n_q * timesteps
+        indexes[:] = n_q * timesteps
+        # iterate over the pattern and fill scattered indexes and mask
+        for s, sequence_coords in enumerate(ref_layout):
+            for coords in sequence_coords:
+                if coords.t < timesteps:
+                    indexes[coords.q, s] = coords.t + coords.q * timesteps
+                    mask[coords.q, s] = 1
+        indexes = torch.from_numpy(indexes).to(device)
+        mask = torch.from_numpy(mask).to(device)
+        return indexes, mask
+
+    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
+        """Build sequence corresponding to the pattern from the input tensor z.
+        The sequence is built using up to sequence_steps if specified, and non-pattern
+        coordinates are filled with the special token.
+
+        Args:
+            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
+            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
+            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
+                Steps that are beyond valid steps will be replaced by the special_token in that case.
+        Returns:
+            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
+                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
+            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
+        """
+        B, K, T = z.shape
+        indexes, mask = self._build_pattern_sequence_scatter_indexes(
+            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
+        )
+        z = z.view(B, -1)
+        # we append the special token as the last index of our flattened z tensor
+        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
+        values = z[:, indexes.view(-1)]
+        values = values.view(B, K, indexes.shape[-1])
+        return values, indexes, mask
+
+    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
+                                                 keep_only_valid_steps: bool = False,
+                                                 is_model_output: bool = False,
+                                                 device: tp.Union[torch.device, str] = 'cpu'):
+        """Builds scatter indexes required to retrieve the original multi-codebook sequence
+        from interleaving pattern.
+
+        Args:
+            sequence_steps (int): Sequence steps.
+            n_q (int): Number of codebooks.
+            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
+                Steps that are beyond valid steps will be replaced by the special_token in that case.
+            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
+            device (Union[torch.device, str]): Device for created tensors.
+        Returns:
+            torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
+        """
+        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
+        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
+        timesteps = self.timesteps
+        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
+        assert sequence_steps <= len(ref_layout), \
+            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
+
+        # ensure we take the appropriate indexes to keep the model output from the first special token as well
+        if is_model_output:
+            ref_layout = ref_layout[1:]
+
+        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
+        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
+        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
+        # fill indexes with last sequence step value that will correspond to our special token
+        indexes[:] = n_q * sequence_steps
+        for s, sequence_codes in enumerate(ref_layout):
+            if s < sequence_steps:
+                for code in sequence_codes:
+                    if code.t < timesteps:
+                        indexes[code.q, code.t] = s + code.q * sequence_steps
+                        mask[code.q, code.t] = 1
+        indexes = torch.from_numpy(indexes).to(device)
+        mask = torch.from_numpy(mask).to(device)
+        return indexes, mask
+
+    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
+        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
+        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
+        are filled with the special token.
+
+        Args:
+            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
+            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
+        Returns:
+            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
+                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
+            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
+        """
+        B, K, S = s.shape
+        indexes, mask = self._build_reverted_sequence_scatter_indexes(
+            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
+        )
+        s = s.view(B, -1)
+        # we append the special token as the last index of our flattened z tensor
+        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
+        values = s[:, indexes.view(-1)]
+        values = values.view(B, K, indexes.shape[-1])
+        return values, indexes, mask
+
+    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
+        """Revert model logits obtained on a sequence built from the pattern
+        back to a tensor matching the original sequence.
+
+        This method is similar to ``revert_pattern_sequence`` with the following specificities:
+        1. It is designed to work with the extra cardinality dimension
+        2. We return the logits for the first sequence item that matches the special_token and
+        which matching target in the original sequence is the first item of the sequence,
+        while we skip the last logits as there is no matching target
+        """
+        B, card, K, S = logits.shape
+        indexes, mask = self._build_reverted_sequence_scatter_indexes(
+            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
+        )
+        logits = logits.reshape(B, card, -1)
+        # we append the special token as the last index of our flattened z tensor
+        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
+        values = logits[:, :, indexes.view(-1)]
+        values = values.view(B, card, K, indexes.shape[-1])
+        return values, indexes, mask
+
+
+class CodebooksPatternProvider(ABC):
+    """Abstraction around providing pattern for interleaving codebooks.
+
+    The CodebooksPatternProvider abstraction allows to implement various strategies to
+    define interleaving pattern of sequences composed of multiple codebooks. For a given
+    number of codebooks `n_q`, the pattern provider can generate a specified pattern
+    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
+    can be used to construct a new sequence from the original codes respecting the specified
+    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
+    being a tuple with the original timestep and codebook to build the new sequence.
+    Note that all patterns must start with an empty list that is then used to insert a first
+    sequence step of special tokens in the newly generated sequence.
+
+    Args:
+        n_q (int): number of codebooks.
+        cached (bool): if True, patterns for a given length are cached. In general
+            that should be true for efficiency reason to avoid synchronization points.
+    """
+    def __init__(self, n_q: int, cached: bool = True):
+        assert n_q > 0
+        self.n_q = n_q
+        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
+
+    @abstractmethod
+    def get_pattern(self, timesteps: int) -> Pattern:
+        """Builds pattern with specific interleaving between codebooks.
+
+        Args:
+            timesteps (int): Total numer of timesteps.
+        """
+        raise NotImplementedError()
+
+
+class DelayedPatternProvider(CodebooksPatternProvider):
+    """Provider for delayed pattern across delayed codebooks.
+    Codebooks are delayed in the sequence and sequence steps will contain codebooks
+    from different timesteps.
+
+    Example:
+        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
+        [[1, 2, 3, 4],
+        [1, 2, 3, 4],
+        [1, 2, 3, 4]]
+        The resulting sequence obtained from the returned pattern is:
+        [[S, 1, 2, 3, 4],
+        [S, S, 1, 2, 3],
+        [S, S, S, 1, 2]]
+        (with S being a special token)
+
+    Args:
+        n_q (int): Number of codebooks.
+        delays (Optional[List[int]]): Delay for each of the codebooks.
+            If delays not defined, each codebook is delayed by 1 compared to the previous one.
+        flatten_first (int): Flatten the first N timesteps.
+        empty_initial (int): Prepend with N empty list of coordinates.
+    """
+    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
+                 flatten_first: int = 0, empty_initial: int = 0):
+        super().__init__(n_q)
+        if delays is None:
+            delays = list(range(n_q))
+        self.delays = delays
+        self.flatten_first = flatten_first
+        self.empty_initial = empty_initial
+        assert len(self.delays) == self.n_q
+        assert sorted(self.delays) == self.delays
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        out: PatternLayout = [[]]
+        max_delay = max(self.delays)
+        if self.empty_initial:
+            out += [[] for _ in range(self.empty_initial)]
+        if self.flatten_first:
+            for t in range(min(timesteps, self.flatten_first)):
+                for q in range(self.n_q):
+                    out.append([LayoutCoord(t, q)])
+        for t in range(self.flatten_first, timesteps + max_delay):
+            v = []
+            for q, delay in enumerate(self.delays):
+                t_for_q = t - delay
+                if t_for_q >= self.flatten_first:
+                    v.append(LayoutCoord(t_for_q, q))
+            out.append(v)
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
+
+
+class ParallelPatternProvider(DelayedPatternProvider):
+    """Provider for parallel pattern across codebooks.
+    This pattern provider is a special case of the delayed pattern with actually no delay,
+    hence delays=repeat(0, n_q).
+
+    Args:
+        n_q (int): Number of codebooks.
+    """
+    def __init__(self, n_q: int):
+        super().__init__(n_q, [0] * n_q)
+
+
+class UnrolledPatternProvider(CodebooksPatternProvider):
+    """Provider for unrolling codebooks pattern.
+    This pattern provider enables to represent the codebook flattened completely or only to some extend
+    while also specifying a given delay between the flattened codebooks representation, allowing to
+    unroll the codebooks in the sequence.
+
+    Example:
+        1. Flattening of the codebooks.
+        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
+        taking n_q = 3 and timesteps = 4:
+        [[1, 2, 3, 4],
+         [1, 2, 3, 4],
+         [1, 2, 3, 4]]
+        will result into:
+        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
+         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
+         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
+        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
+        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
+        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
+        [[1, 2, 3, 4],
+         [1, 2, 3, 4],
+         [1, 2, 3, 4]]
+        will result into:
+        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
+         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
+         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
+        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
+        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
+        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
+        and delays = [0, 3, 3]:
+        [[1, 2, 3, 4],
+         [1, 2, 3, 4],
+         [1, 2, 3, 4]]
+        will result into:
+        [[S, S, S, 1, S, 2, S, 3, S, 4],
+         [S, S, S, 1, S, 2, S, 3, S, 4],
+         [1, 2, 3, S, 4, S, 5, S, 6, S]]
+
+    Args:
+        n_q (int): Number of codebooks.
+        flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
+            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
+            have n_q extra steps for each timestep.
+        delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
+            no delay is added and therefore will default to [0] * ``n_q``.
+            Note that two codebooks that will be flattened to the same inner step
+            should have the same delay, otherwise the pattern is considered as invalid.
+    """
+    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
+
+    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
+                 delays: tp.Optional[tp.List[int]] = None):
+        super().__init__(n_q)
+        if flattening is None:
+            flattening = list(range(n_q))
+        if delays is None:
+            delays = [0] * n_q
+        assert len(flattening) == n_q
+        assert len(delays) == n_q
+        assert sorted(flattening) == flattening
+        assert sorted(delays) == delays
+        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
+        self.max_delay = max(delays)
+
+    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
+        """Build a flattened codebooks representation as a dictionary of inner step
+        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
+        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
+        """
+        flattened_codebooks: dict = {}
+        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
+            if inner_step not in flattened_codebooks:
+                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
+            else:
+                flat_codebook = flattened_codebooks[inner_step]
+                assert flat_codebook.delay == delay, (
+                    "Delay and flattening between codebooks is inconsistent: ",
+                    "two codebooks flattened to the same position should have the same delay."
+                )
+                flat_codebook.codebooks.append(q)
+            flattened_codebooks[inner_step] = flat_codebook
+        return flattened_codebooks
+
+    @property
+    def _num_inner_steps(self):
+        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
+        """
+        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
+
+    def num_virtual_steps(self, timesteps: int) -> int:
+        return timesteps * self._num_inner_steps + 1
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        """Builds pattern for delay across codebooks.
+
+        Args:
+            timesteps (int): Total numer of timesteps.
+        """
+        # the PatternLayout is built as a tuple of sequence position and list of coordinates
+        # so that it can be reordered properly given the required delay between codebooks of given timesteps
+        indexed_out: list = [(-1, [])]
+        max_timesteps = timesteps + self.max_delay
+        for t in range(max_timesteps):
+            # for each timestep, we unroll the flattened codebooks,
+            # emitting the sequence step with the corresponding delay
+            for step in range(self._num_inner_steps):
+                if step in self._flattened_codebooks:
+                    # we have codebooks at this virtual step to emit
+                    step_codebooks = self._flattened_codebooks[step]
+                    t_for_q = t + step_codebooks.delay
+                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
+                    if t_for_q < max_timesteps and t < max_timesteps:
+                        indexed_out.append((t_for_q, coords))
+                else:
+                    # there is no codebook in this virtual step so we emit an empty list
+                    indexed_out.append((t, []))
+        out = [coords for _, coords in sorted(indexed_out)]
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
+
+
+class VALLEPattern(CodebooksPatternProvider):
+    """Almost VALL-E style pattern. We futher allow some delays for the
+    codebooks other than the first one.
+
+    Args:
+        n_q (int): Number of codebooks.
+        delays (Optional[List[int]]): Delay for each of the codebooks.
+            If delays not defined, each codebook is delayed by 1 compared to the previous one.
+    """
+    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
+        super().__init__(n_q)
+        if delays is None:
+            delays = [0] * (n_q - 1)
+        self.delays = delays
+        assert len(self.delays) == self.n_q - 1
+        assert sorted(self.delays) == self.delays
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        out: PatternLayout = [[]]
+        for t in range(timesteps):
+            out.append([LayoutCoord(t, 0)])
+        max_delay = max(self.delays)
+        for t in range(timesteps + max_delay):
+            v = []
+            for q, delay in enumerate(self.delays):
+                t_for_q = t - delay
+                if t_for_q >= 0:
+                    v.append(LayoutCoord(t_for_q, q + 1))
+            out.append(v)
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
+
+
+class MusicLMPattern(CodebooksPatternProvider):
+    """Almost MusicLM style pattern. This is equivalent to full flattening
+    but in a different order.
+
+    Args:
+        n_q (int): Number of codebooks.
+        group_by (int): Number of codebooks to group together.
+    """
+    def __init__(self, n_q: int, group_by: int = 2):
+        super().__init__(n_q)
+        self.group_by = group_by
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        out: PatternLayout = [[]]
+        for offset in range(0, self.n_q, self.group_by):
+            for t in range(timesteps):
+                for q in range(offset, offset + self.group_by):
+                    out.append([LayoutCoord(t, q)])
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)

audiocraft/modules/conditioners.py

@@ -0,0 +1,986 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import defaultdict
+from copy import deepcopy
+from dataclasses import dataclass, field
+from itertools import chain
+import logging
+import random
+import re
+import typing as tp
+import warnings
+
+from einops import rearrange
+from num2words import num2words
+import spacy
+from transformers import T5EncoderModel, T5Tokenizer  # type: ignore
+import torchaudio
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+from .streaming import StreamingModule
+from .transformer import create_sin_embedding
+from ..data.audio_dataset import SegmentInfo
+from ..utils.autocast import TorchAutocast
+from ..utils.utils import hash_trick, length_to_mask, collate
+
+
+logger = logging.getLogger(__name__)
+TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
+ConditionType = tp.Tuple[Tensor, Tensor]  # condition, mask
+
+
+class WavCondition(tp.NamedTuple):
+    wav: Tensor
+    length: Tensor
+    path: tp.List[tp.Optional[str]] = []
+
+
+def nullify_condition(condition: ConditionType, dim: int = 1):
+    """This function transforms an input condition to a null condition.
+    The way it is done by converting it to a single zero vector similarly
+    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
+
+    Args:
+        condition (ConditionType): a tuple of condition and mask (tp.Tuple[Tensor, Tensor])
+        dim (int): the dimension that will be truncated (should be the time dimension)
+        WARNING!: dim should not be the batch dimension!
+    Returns:
+        ConditionType: a tuple of null condition and mask
+    """
+    assert dim != 0, "dim cannot be the batch dimension!"
+    assert type(condition) == tuple and \
+        type(condition[0]) == Tensor and \
+        type(condition[1]) == Tensor, "'nullify_condition' got an unexpected input type!"
+    cond, mask = condition
+    B = cond.shape[0]
+    last_dim = cond.dim() - 1
+    out = cond.transpose(dim, last_dim)
+    out = 0. * out[..., :1]
+    out = out.transpose(dim, last_dim)
+    mask = torch.zeros((B, 1), device=out.device).int()
+    assert cond.dim() == out.dim()
+    return out, mask
+
+
+def nullify_wav(wav: Tensor) -> WavCondition:
+    """Create a nullified WavCondition from a wav tensor with appropriate shape.
+
+    Args:
+        wav (Tensor): tensor of shape [B, T]
+    Returns:
+        WavCondition: wav condition with nullified wav.
+    """
+    null_wav, _ = nullify_condition((wav, torch.zeros_like(wav)), dim=wav.dim() - 1)
+    return WavCondition(
+        wav=null_wav,
+        length=torch.tensor([0] * wav.shape[0], device=wav.device),
+        path=['null_wav'] * wav.shape[0]
+    )
+
+
+@dataclass
+class ConditioningAttributes:
+    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
+    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
+
+    def __getitem__(self, item):
+        return getattr(self, item)
+
+    @property
+    def text_attributes(self):
+        return self.text.keys()
+
+    @property
+    def wav_attributes(self):
+        return self.wav.keys()
+
+    @property
+    def attributes(self):
+        return {"text": self.text_attributes, "wav": self.wav_attributes}
+
+    def to_flat_dict(self):
+        return {
+            **{f"text.{k}": v for k, v in self.text.items()},
+            **{f"wav.{k}": v for k, v in self.wav.items()},
+        }
+
+    @classmethod
+    def from_flat_dict(cls, x):
+        out = cls()
+        for k, v in x.items():
+            kind, att = k.split(".")
+            out[kind][att] = v
+        return out
+
+
+class SegmentWithAttributes(SegmentInfo):
+    """Base class for all dataclasses that are used for conditioning.
+    All child classes should implement `to_condition_attributes` that converts
+    the existing attributes to a dataclass of type ConditioningAttributes.
+    """
+    def to_condition_attributes(self) -> ConditioningAttributes:
+        raise NotImplementedError()
+
+
+class Tokenizer:
+    """Base class for all tokenizers
+    (in case we want to introduce more advances tokenizers in the future).
+    """
+    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
+        raise NotImplementedError()
+
+
+class WhiteSpaceTokenizer(Tokenizer):
+    """This tokenizer should be used for natural language descriptions.
+    For example:
+    ["he didn't, know he's going home.", 'shorter sentence'] =>
+    [[78, 62, 31,  4, 78, 25, 19, 34],
+    [59, 77,  0,  0,  0,  0,  0,  0]]
+    """
+    PUNCTUATIONS = "?:!.,;"
+
+    def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
+                 lemma: bool = True, stopwords: bool = True) -> None:
+        self.n_bins = n_bins
+        self.pad_idx = pad_idx
+        self.lemma = lemma
+        self.stopwords = stopwords
+        try:
+            self.nlp = spacy.load(language)
+        except IOError:
+            spacy.cli.download(language)  # type: ignore
+            self.nlp = spacy.load(language)
+
+    @tp.no_type_check
+    def __call__(
+        self,
+        texts: tp.List[tp.Optional[str]],
+        return_text: bool = False
+    ) -> tp.Tuple[Tensor, Tensor]:
+        """Take a list of strings and convert them to a tensor of indices.
+
+        Args:
+            texts (tp.List[str]): List of strings.
+            return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
+        Returns:
+            tp.Tuple[Tensor, Tensor]:
+                - Indices of words in the LUT.
+                - And a mask indicating where the padding tokens are
+        """
+        output, lengths = [], []
+        texts = deepcopy(texts)
+        for i, text in enumerate(texts):
+            # if current sample doesn't have a certain attribute, replace with pad token
+            if text is None:
+                output.append(Tensor([self.pad_idx]))
+                lengths.append(0)
+                continue
+
+            # convert numbers to words
+            text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text)  # type: ignore
+            # normalize text
+            text = self.nlp(text)  # type: ignore
+            # remove stopwords
+            if self.stopwords:
+                text = [w for w in text if not w.is_stop]  # type: ignore
+            # remove punctuations
+            text = [w for w in text if w.text not in self.PUNCTUATIONS]  # type: ignore
+            # lemmatize if needed
+            text = [getattr(t, "lemma_" if self.lemma else "text") for t in text]  # type: ignore
+
+            texts[i] = " ".join(text)
+            lengths.append(len(text))
+            # convert to tensor
+            tokens = Tensor([hash_trick(w, self.n_bins) for w in text])
+            output.append(tokens)
+
+        mask = length_to_mask(torch.IntTensor(lengths)).int()
+        padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
+        if return_text:
+            return padded_output, mask, texts  # type: ignore
+        return padded_output, mask
+
+
+class NoopTokenizer(Tokenizer):
+    """This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
+    The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
+    strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
+    split it to ["Jeff", "Buckley"] and return an index per word.
+
+    For example:
+    ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
+    ["Metal", "Rock", "Classical"] => [0, 223, 51]
+    """
+    def __init__(self, n_bins: int, pad_idx: int = 0):
+        self.n_bins = n_bins
+        self.pad_idx = pad_idx
+
+    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
+        output, lengths = [], []
+        for text in texts:
+            # if current sample doesn't have a certain attribute, replace with pad token
+            if text is None:
+                output.append(self.pad_idx)
+                lengths.append(0)
+            else:
+                output.append(hash_trick(text, self.n_bins))
+                lengths.append(1)
+
+        tokens = torch.LongTensor(output).unsqueeze(1)
+        mask = length_to_mask(torch.IntTensor(lengths)).int()
+        return tokens, mask
+
+
+class BaseConditioner(nn.Module):
+    """Base model for all conditioner modules. We allow the output dim to be different
+    than the hidden dim for two reasons: 1) keep our LUTs small when the vocab is large;
+    2) make all condition dims consistent.
+
+    Args:
+        dim (int): Hidden dim of the model (text-encoder/LUT).
+        output_dim (int): Output dim of the conditioner.
+    """
+    def __init__(self, dim, output_dim):
+        super().__init__()
+        self.dim = dim
+        self.output_dim = output_dim
+        self.output_proj = nn.Linear(dim, output_dim)
+
+    def tokenize(self, *args, **kwargs) -> tp.Any:
+        """Should be any part of the processing that will lead to a synchronization
+        point, e.g. BPE tokenization with transfer to the GPU.
+
+        The returned value will be saved and return later when calling forward().
+        """
+        raise NotImplementedError()
+
+    def forward(self, inputs: tp.Any) -> ConditionType:
+        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
+        Outputs a ConditionType, after the input data was embedded as a dense vector.
+
+        Returns:
+            ConditionType:
+                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
+                  output embedding and D is the dimension of the embedding.
+                - And a mask indicating where the padding tokens.
+        """
+        raise NotImplementedError()
+
+
+class TextConditioner(BaseConditioner):
+    ...
+
+
+class LUTConditioner(TextConditioner):
+    """Lookup table TextConditioner.
+
+    Args:
+        n_bins (int): Number of bins.
+        dim (int): Hidden dim of the model (text-encoder/LUT).
+        output_dim (int): Output dim of the conditioner.
+        tokenizer (str): Name of the tokenizer.
+        pad_idx (int, optional): Index for padding token. Defaults to 0.
+    """
+    def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
+        super().__init__(dim, output_dim)
+        self.embed = nn.Embedding(n_bins, dim)
+        self.tokenizer: Tokenizer
+        if tokenizer == "whitespace":
+            self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
+        elif tokenizer == "noop":
+            self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
+        else:
+            raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
+
+    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        device = self.embed.weight.device
+        tokens, mask = self.tokenizer(x)
+        tokens, mask = tokens.to(device), mask.to(device)
+        return tokens, mask
+
+    def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
+        tokens, mask = inputs
+        embeds = self.embed(tokens)
+        embeds = self.output_proj(embeds)
+        embeds = (embeds * mask.unsqueeze(-1))
+        return embeds, mask
+
+
+class T5Conditioner(TextConditioner):
+    """T5-based TextConditioner.
+
+    Args:
+        name (str): Name of the T5 model.
+        output_dim (int): Output dim of the conditioner.
+        finetune (bool): Whether to fine-tune T5 at train time.
+        device (str): Device for T5 Conditioner.
+        autocast_dtype (tp.Optional[str], optional): Autocast dtype.
+        word_dropout (float, optional): Word dropout probability.
+        normalize_text (bool, optional): Whether to apply text normalization.
+    """
+    MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
+              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
+              "google/flan-t5-xl", "google/flan-t5-xxl"]
+    MODELS_DIMS = {
+        "t5-small": 512,
+        "t5-base": 768,
+        "t5-large": 1024,
+        "t5-3b": 1024,
+        "t5-11b": 1024,
+        "google/flan-t5-small": 512,
+        "google/flan-t5-base": 768,
+        "google/flan-t5-large": 1024,
+        "google/flan-t5-3b": 1024,
+        "google/flan-t5-11b": 1024,
+    }
+
+    def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
+                 autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
+                 normalize_text: bool = False):
+        assert name in self.MODELS, f"unrecognized t5 model name (should in {self.MODELS})"
+        super().__init__(self.MODELS_DIMS[name], output_dim)
+        self.device = device
+        self.name = name
+        self.finetune = finetune
+        self.word_dropout = word_dropout
+
+        if autocast_dtype is None or self.device == 'cpu':
+            self.autocast = TorchAutocast(enabled=False)
+            if self.device != 'cpu':
+                logger.warning("T5 has no autocast, this might lead to NaN")
+        else:
+            dtype = getattr(torch, autocast_dtype)
+            assert isinstance(dtype, torch.dtype)
+            logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
+            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
+        # Let's disable logging temporarily because T5 will vomit some errors otherwise.
+        # thanks https://gist.github.com/simon-weber/7853144
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
+                t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
+            finally:
+                logging.disable(previous_level)
+        if finetune:
+            self.t5 = t5
+        else:
+            # this makes sure that the t5 models is not part
+            # of the saved checkpoint
+            self.__dict__["t5"] = t5.to(device)
+
+        self.normalize_text = normalize_text
+        if normalize_text:
+            self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
+
+    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
+        # if current sample doesn't have a certain attribute, replace with empty string
+        entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
+        if self.normalize_text:
+            _, _, entries = self.text_normalizer(entries, return_text=True)
+        if self.word_dropout > 0. and self.training:
+            new_entries = []
+            for entry in entries:
+                words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
+                new_entries.append(" ".join(words))
+            entries = new_entries
+
+        empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
+
+        inputs = self.t5_tokenizer(entries, return_tensors="pt", padding=True).to(self.device)
+        mask = inputs["attention_mask"]
+        mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
+        return inputs
+
+    def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
+        mask = inputs["attention_mask"]
+        with torch.set_grad_enabled(self.finetune), self.autocast:
+            embeds = self.t5(**inputs).last_hidden_state
+        embeds = self.output_proj(embeds.to(self.output_proj.weight))
+        embeds = (embeds * mask.unsqueeze(-1))
+        return embeds, mask
+
+
+class WaveformConditioner(BaseConditioner):
+    """Base class for all conditioners that take a waveform as input.
+    Classes that inherit must implement `_get_wav_embedding` that outputs
+    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
+    factor of the embedding model.
+
+    Args:
+        dim (int): The internal representation dimension.
+        output_dim (int): Output dimension.
+        device (tp.Union[torch.device, str]): Device.
+    """
+    def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
+        super().__init__(dim, output_dim)
+        self.device = device
+
+    def tokenize(self, wav_length: WavCondition) -> WavCondition:
+        wav, length, path = wav_length
+        assert length is not None
+        return WavCondition(wav.to(self.device), length.to(self.device), path)
+
+    def _get_wav_embedding(self, wav: Tensor) -> Tensor:
+        """Gets as input a wav and returns a dense vector of conditions."""
+        raise NotImplementedError()
+
+    def _downsampling_factor(self):
+        """Returns the downsampling factor of the embedding model."""
+        raise NotImplementedError()
+
+    def forward(self, inputs: WavCondition) -> ConditionType:
+        """
+        Args:
+            input (WavCondition): Tuple of (waveform, lengths).
+        Returns:
+            ConditionType: Dense vector representing the conditioning along with its' mask.
+        """
+        wav, lengths, path = inputs
+        with torch.no_grad():
+            embeds = self._get_wav_embedding(wav)
+        embeds = embeds.to(self.output_proj.weight)
+        embeds = self.output_proj(embeds)
+
+        if lengths is not None:
+            lengths = lengths / self._downsampling_factor()
+            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
+        else:
+            mask = torch.ones_like(embeds)
+        embeds = (embeds * mask.unsqueeze(2).to(self.device))
+
+        return embeds, mask
+
+
+class ChromaStemConditioner(WaveformConditioner):
+    """Chroma conditioner that uses DEMUCS to first filter out drums and bass. The is followed by
+    the insight the drums and bass often dominate the chroma, leading to the chroma not containing the
+    information about melody.
+
+    Args:
+        output_dim (int): Output dimension for the conditioner.
+        sample_rate (int): Sample rate for the chroma extractor.
+        n_chroma (int): Number of chroma for the chroma extractor.
+        radix2_exp (int): Radix2 exponent for the chroma extractor.
+        duration (float): Duration used during training. This is later used for correct padding
+            in case we are using chroma as prefix.
+        match_len_on_eval (bool, optional): If True then all chromas are padded to the training
+            duration. Defaults to False.
+        eval_wavs (str, optional): Path to a json egg with waveform, this waveforms are used as
+            conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
+            Defaults to None.
+        n_eval_wavs (int, optional): Limits the number of waveforms used for conditioning. Defaults to 0.
+        device (tp.Union[torch.device, str], optional): Device for the conditioner.
+        **kwargs: Additional parameters for the chroma extractor.
+    """
+    def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
+                 duration: float, match_len_on_eval: bool = False, eval_wavs: tp.Optional[str] = None,
+                 n_eval_wavs: int = 0, device: tp.Union[torch.device, str] = "cpu", **kwargs):
+        from demucs import pretrained
+        super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
+        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
+        self.sample_rate = sample_rate
+        self.match_len_on_eval = match_len_on_eval
+        self.duration = duration
+        self.__dict__["demucs"] = pretrained.get_model('htdemucs').to(device)
+        self.stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
+        self.stem_idx = torch.LongTensor([self.stem2idx['vocal'], self.stem2idx['other']]).to(device)
+        self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma, radix2_exp=radix2_exp,
+                                      device=device, **kwargs)
+        self.chroma_len = self._get_chroma_len()
+
+    def _downsampling_factor(self):
+        return self.chroma.winhop
+
+    def _get_chroma_len(self):
+        """Get length of chroma during training"""
+        dummy_wav = torch.zeros((1, self.sample_rate * self.duration), device=self.device)
+        dummy_chr = self.chroma(dummy_wav)
+        return dummy_chr.shape[1]
+
+    @torch.no_grad()
+    def _get_filtered_wav(self, wav):
+        from demucs.apply import apply_model
+        from demucs.audio import convert_audio
+        with self.autocast:
+            wav = convert_audio(wav, self.sample_rate, self.demucs.samplerate, self.demucs.audio_channels)
+            stems = apply_model(self.demucs, wav, device=self.device)
+            stems = stems[:, self.stem_idx]  # extract stem
+            stems = stems.sum(1)  # merge extracted stems
+            stems = stems.mean(1, keepdim=True)  # mono
+            stems = convert_audio(stems, self.demucs.samplerate, self.sample_rate, 1)
+            return stems
+
+    @torch.no_grad()
+    def _get_wav_embedding(self, wav):
+        # avoid 0-size tensors when we are working with null conds
+        if wav.shape[-1] == 1:
+            return self.chroma(wav)
+        stems = self._get_filtered_wav(wav)
+        chroma = self.chroma(stems)
+
+        if self.match_len_on_eval:
+            b, t, c = chroma.shape
+            if t > self.chroma_len:
+                chroma = chroma[:, :self.chroma_len]
+                logger.debug(f'chroma was truncated! ({t} -> {chroma.shape[1]})')
+            elif t < self.chroma_len:
+                chroma = F.pad(chroma, (0, 0, 0, self.chroma_len - t))
+                logger.debug(f'chroma was zero-padded! ({t} -> {chroma.shape[1]})')
+        return chroma
+
+
+class ChromaExtractor(nn.Module):
+    """Chroma extraction class, handles chroma extraction and quantization.
+
+    Args:
+        sample_rate (int): Sample rate.
+        n_chroma (int): Number of chroma to consider.
+        radix2_exp (int): Radix2 exponent.
+        nfft (tp.Optional[int], optional): Number of FFT.
+        winlen (tp.Optional[int], optional): Window length.
+        winhop (tp.Optional[int], optional): Window hop size.
+        argmax (bool, optional): Whether to use argmax. Defaults to False.
+        norm (float, optional): Norm for chroma normalization. Defaults to inf.
+        device (tp.Union[torch.device, str], optional): Device to use. Defaults to cpu.
+    """
+    def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12,
+                 nfft: tp.Optional[int] = None, winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None,
+                 argmax: bool = False, norm: float = torch.inf, device: tp.Union[torch.device, str] = "cpu"):
+        super().__init__()
+        from librosa import filters
+        self.device = device
+        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
+        self.winlen = winlen or 2 ** radix2_exp
+        self.nfft = nfft or self.winlen
+        self.winhop = winhop or (self.winlen // 4)
+        self.sr = sample_rate
+        self.n_chroma = n_chroma
+        self.norm = norm
+        self.argmax = argmax
+        self.window = torch.hann_window(self.winlen).to(device)
+        self.fbanks = torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
+                                                      n_chroma=self.n_chroma)).to(device)
+        self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
+                                                      hop_length=self.winhop, power=2, center=True,
+                                                      pad=0, normalized=True).to(device)
+
+    def forward(self, wav):
+        with self.autocast:
+            T = wav.shape[-1]
+            # in case we are getting a wav that was dropped out (nullified)
+            # make sure wav length is no less that nfft
+            if T < self.nfft:
+                pad = self.nfft - T
+                r = 0 if pad % 2 == 0 else 1
+                wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
+                assert wav.shape[-1] == self.nfft, f'expected len {self.nfft} but got {wav.shape[-1]}'
+            spec = self.spec(wav).squeeze(1)
+            raw_chroma = torch.einsum("cf,...ft->...ct", self.fbanks, spec)
+            norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
+            norm_chroma = rearrange(norm_chroma, "b d t -> b t d")
+
+            if self.argmax:
+                idx = norm_chroma.argmax(-1, keepdims=True)
+                norm_chroma[:] = 0
+                norm_chroma.scatter_(dim=-1, index=idx, value=1)
+
+            return norm_chroma
+
+
+def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str):
+    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
+    If the condition is of type "wav", then nullify it using "nullify_condition".
+    If the condition is of any other type, set its' value to None.
+    Works in-place.
+    """
+    if condition_type not in ["text", "wav"]:
+        raise ValueError(
+            "dropout_condition got an unexpected condition type!"
+            f" expected 'wav' or 'text' but got '{condition_type}'"
+        )
+
+    if condition not in getattr(sample, condition_type):
+        raise ValueError(
+            "dropout_condition received an unexpected condition!"
+            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
+            f"but got '{condition}' of type '{condition_type}'!"
+        )
+
+    if condition_type == "wav":
+        wav, length, path = sample.wav[condition]
+        sample.wav[condition] = nullify_wav(wav)
+    else:
+        sample.text[condition] = None
+
+    return sample
+
+
+class DropoutModule(nn.Module):
+    """Base class for all dropout modules."""
+    def __init__(self, seed: int = 1234):
+        super().__init__()
+        self.rng = torch.Generator()
+        self.rng.manual_seed(seed)
+
+
+class AttributeDropout(DropoutModule):
+    """Applies dropout with a given probability per attribute. This is different from the behavior of
+    ClassifierFreeGuidanceDropout as this allows for attributes to be dropped out separately. For example,
+    "artist" can be dropped while "genre" remains. This is in contrast to ClassifierFreeGuidanceDropout
+    where if "artist" is dropped "genre" must also be dropped.
+
+    Args:
+        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
+            ...
+            "genre": 0.1,
+            "artist": 0.5,
+            "wav": 0.25,
+            ...
+        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
+        seed (int, optional): Random seed.
+    """
+    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
+        super().__init__(seed=seed)
+        self.active_on_eval = active_on_eval
+        # construct dict that return the values from p otherwise 0
+        self.p = {}
+        for condition_type, probs in p.items():
+            self.p[condition_type] = defaultdict(lambda: 0, probs)
+
+    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
+        """
+        Args:
+            samples (tp.List[ConditioningAttributes]): List of conditions.
+        Returns:
+            tp.List[ConditioningAttributes]: List of conditions after certain attributes were set to None.
+        """
+        if not self.training and not self.active_on_eval:
+            return samples
+
+        samples = deepcopy(samples)
+
+        for condition_type, ps in self.p.items():  # for condition types [text, wav]
+            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
+                if torch.rand(1, generator=self.rng).item() < p:
+                    for sample in samples:
+                        dropout_condition(sample, condition_type, condition)
+
+        return samples
+
+    def __repr__(self):
+        return f"AttributeDropout({dict(self.p)})"
+
+
+class ClassifierFreeGuidanceDropout(DropoutModule):
+    """Applies Classifier Free Guidance dropout, meaning all attributes
+    are dropped with the same probability.
+
+    Args:
+        p (float): Probability to apply condition dropout during training.
+        seed (int): Random seed.
+    """
+    def __init__(self, p: float, seed: int = 1234):
+        super().__init__(seed=seed)
+        self.p = p
+
+    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
+        """
+        Args:
+            samples (tp.List[ConditioningAttributes]): List of conditions.
+        Returns:
+            tp.List[ConditioningAttributes]: List of conditions after all attributes were set to None.
+        """
+        if not self.training:
+            return samples
+
+        # decide on which attributes to drop in a batched fashion
+        drop = torch.rand(1, generator=self.rng).item() < self.p
+        if not drop:
+            return samples
+
+        # nullify conditions of all attributes
+        samples = deepcopy(samples)
+
+        for condition_type in ["wav", "text"]:
+            for sample in samples:
+                for condition in sample.attributes[condition_type]:
+                    dropout_condition(sample, condition_type, condition)
+
+        return samples
+
+    def __repr__(self):
+        return f"ClassifierFreeGuidanceDropout(p={self.p})"
+
+
+class ConditioningProvider(nn.Module):
+    """Main class to provide conditions given all the supported conditioners.
+
+    Args:
+        conditioners (dict): Dictionary of conditioners.
+        merge_text_conditions_p (float, optional): Probability to merge all text sources
+            into a single text condition. Defaults to 0.
+        drop_desc_p (float, optional): Probability to drop the original description
+            when merging all text sources into a single text condition. Defaults to 0.
+        device (tp.Union[torch.device, str], optional): Device for conditioners and output condition types.
+    """
+    def __init__(
+        self,
+        conditioners: tp.Dict[str, BaseConditioner],
+        merge_text_conditions_p: float = 0,
+        drop_desc_p: float = 0,
+        device: tp.Union[torch.device, str] = "cpu",
+    ):
+        super().__init__()
+        self.device = device
+        self.merge_text_conditions_p = merge_text_conditions_p
+        self.drop_desc_p = drop_desc_p
+        self.conditioners = nn.ModuleDict(conditioners)
+
+    @property
+    def text_conditions(self):
+        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
+
+    @property
+    def wav_conditions(self):
+        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
+
+    @property
+    def has_wav_condition(self):
+        return len(self.wav_conditions) > 0
+
+    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
+        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
+        This should be called before starting any real GPU work to avoid synchronization points.
+        This will return a dict matching conditioner names to their arbitrary tokenized representations.
+
+        Args:
+            inputs (list[ConditioningAttribres]): List of ConditioningAttributes objects containing
+                text and wav conditions.
+        """
+        assert all([type(x) == ConditioningAttributes for x in inputs]), \
+            "got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]" \
+            f" but types were {set([type(x) for x in inputs])}"
+
+        output = {}
+        text = self._collate_text(inputs)
+        wavs = self._collate_wavs(inputs)
+
+        assert set(text.keys() | wavs.keys()).issubset(set(self.conditioners.keys())), \
+            f"got an unexpected attribute! Expected {self.conditioners.keys()}, got {text.keys(), wavs.keys()}"
+
+        for attribute, batch in chain(text.items(), wavs.items()):
+            output[attribute] = self.conditioners[attribute].tokenize(batch)
+        return output
+
+    def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
+        """Compute pairs of `(embedding, mask)` using the configured conditioners
+        and the tokenized representations. The output is for example:
+
+            {
+                "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
+                "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
+                ...
+            }
+
+        Args:
+            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
+        """
+        output = {}
+        for attribute, inputs in tokenized.items():
+            condition, mask = self.conditioners[attribute](inputs)
+            output[attribute] = (condition, mask)
+        return output
+
+    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
+        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
+        are the attributes and the values are the aggregated input per attribute.
+        For example:
+        Input:
+        [
+            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
+            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
+        ]
+        Output:
+        {
+            "genre": ["Rock", "Hip-hop"],
+            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
+        }
+        """
+        batch_per_attribute: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
+
+        def _merge_conds(cond, merge_text_conditions_p=0, drop_desc_p=0):
+            def is_valid(k, v):
+                k_valid = k in ['key', 'bpm', 'genre', 'moods', 'instrument']
+                v_valid = v is not None and isinstance(v, (int, float, str, list))
+                return k_valid and v_valid
+
+            def process_value(v):
+                if isinstance(v, (int, float, str)):
+                    return v
+                if isinstance(v, list):
+                    return ", ".join(v)
+                else:
+                    RuntimeError(f"unknown type for text value! ({type(v), v})")
+
+            desc = cond.text['description']
+            meta_data = ""
+            if random.uniform(0, 1) < merge_text_conditions_p:
+                meta_pairs = [f'{k}: {process_value(v)}' for k, v in cond.text.items() if is_valid(k, v)]
+                random.shuffle(meta_pairs)
+                meta_data = ". ".join(meta_pairs)
+                desc = desc if not random.uniform(0, 1) < drop_desc_p else None
+
+            if desc is None:
+                desc = meta_data if len(meta_data) > 1 else None
+            else:
+                desc = desc.rstrip('.') + ". " + meta_data
+            cond.text['description'] = desc.strip() if desc else None
+
+        if self.training and self.merge_text_conditions_p:
+            for sample in samples:
+                _merge_conds(sample, self.merge_text_conditions_p, self.drop_desc_p)
+
+        texts = [x.text for x in samples]
+        for text in texts:
+            for condition in self.text_conditions:
+                batch_per_attribute[condition].append(text[condition])
+
+        return batch_per_attribute
+
+    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]):
+        """Generate a dict where the keys are attributes by which we fetch similar wavs,
+        and the values are Tensors of wavs according to said attribtues.
+
+        *Note*: by the time the samples reach this function, each sample should have some waveform
+        inside the "wav" attribute. It should be either:
+        1. A real waveform
+        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
+        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
+
+        Args:
+            samples (tp.List[ConditioningAttributes]): List of ConditioningAttributes samples.
+        Returns:
+            dict: A dicionary mapping an attribute name to wavs.
+        """
+        wavs = defaultdict(list)
+        lens = defaultdict(list)
+        paths = defaultdict(list)
+        out = {}
+
+        for sample in samples:
+            for attribute in self.wav_conditions:
+                wav, length, path = sample.wav[attribute]
+                wavs[attribute].append(wav.flatten())
+                lens[attribute].append(length)
+                paths[attribute].append(path)
+
+        # stack all wavs to a single tensor
+        for attribute in self.wav_conditions:
+            stacked_wav, _ = collate(wavs[attribute], dim=0)
+            out[attribute] = WavCondition(stacked_wav.unsqueeze(1),
+                                          torch.cat(lens['self_wav']), paths[attribute])  # type: ignore
+
+        return out
+
+
+class ConditionFuser(StreamingModule):
+    """Condition fuser handles the logic to combine the different conditions
+    to the actual model input.
+
+    Args:
+        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
+            each condition. For example:
+            {
+                "prepend": ["description"],
+                "sum": ["genre", "bpm"],
+                "cross": ["description"],
+            }
+        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
+        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
+    """
+    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
+
+    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
+                 cross_attention_pos_emb_scale: float = 1.0):
+        super().__init__()
+        assert all(
+            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
+        ), f"got invalid fuse method, allowed methods: {self.FUSING_MEHTODS}"
+        self.cross_attention_pos_emb = cross_attention_pos_emb
+        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
+        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
+        self.cond2fuse: tp.Dict[str, str] = {}
+        for fuse_method, conditions in fuse2cond.items():
+            for condition in conditions:
+                self.cond2fuse[condition] = fuse_method
+
+    def forward(
+        self,
+        input: Tensor,
+        conditions: tp.Dict[str, ConditionType]
+    ) -> tp.Tuple[Tensor, tp.Optional[Tensor]]:
+        """Fuse the conditions to the provided model input.
+
+        Args:
+            input (Tensor): Transformer input.
+            conditions (tp.Dict[str, ConditionType]): Dict of conditions.
+        Returns:
+            tp.Tuple[Tensor, Tensor]: The first tensor is the transformer input
+                after the conditions have been fused. The second output tensor is the tensor
+                used for cross-attention or None if no cross attention inputs exist.
+        """
+        B, T, _ = input.shape
+
+        if 'offsets' in self._streaming_state:
+            first_step = False
+            offsets = self._streaming_state['offsets']
+        else:
+            first_step = True
+            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
+
+        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
+            f"given conditions contain unknown attributes for fuser, " \
+            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
+        cross_attention_output = None
+        for cond_type, (cond, cond_mask) in conditions.items():
+            op = self.cond2fuse[cond_type]
+            if op == "sum":
+                input += cond
+            elif op == "input_interpolate":
+                cond = rearrange(cond, "b t d -> b d t")
+                cond = F.interpolate(cond, size=input.shape[1])
+                input += rearrange(cond, "b d t -> b t d")
+            elif op == "prepend":
+                if first_step:
+                    input = torch.cat([cond, input], dim=1)
+            elif op == "cross":
+                if cross_attention_output is not None:
+                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
+                else:
+                    cross_attention_output = cond
+            else:
+                raise ValueError(f"unknown op ({op})")
+
+        if self.cross_attention_pos_emb and cross_attention_output is not None:
+            positions = torch.arange(
+                cross_attention_output.shape[1],
+                device=cross_attention_output.device
+            ).view(1, -1, 1)
+            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
+            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
+
+        if self._is_streaming:
+            self._streaming_state['offsets'] = offsets + T
+
+        return input, cross_attention_output

audiocraft/modules/conv.py

@@ -0,0 +1,245 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import typing as tp
+import warnings
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import spectral_norm, weight_norm
+
+
+CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
+                                 'time_group_norm'])
+
+
+def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
+    assert norm in CONV_NORMALIZATIONS
+    if norm == 'weight_norm':
+        return weight_norm(module)
+    elif norm == 'spectral_norm':
+        return spectral_norm(module)
+    else:
+        # We already check was in CONV_NORMALIZATION, so any other choice
+        # doesn't need reparametrization.
+        return module
+
+
+def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
+    """Return the proper normalization module. If causal is True, this will ensure the returned
+    module is causal, or return an error if the normalization doesn't support causal evaluation.
+    """
+    assert norm in CONV_NORMALIZATIONS
+    if norm == 'time_group_norm':
+        if causal:
+            raise ValueError("GroupNorm doesn't support causal evaluation.")
+        assert isinstance(module, nn.modules.conv._ConvNd)
+        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
+    else:
+        return nn.Identity()
+
+
+def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
+                                 padding_total: int = 0) -> int:
+    """See `pad_for_conv1d`.
+    """
+    length = x.shape[-1]
+    n_frames = (length - kernel_size + padding_total) / stride + 1
+    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
+    return ideal_length - length
+
+
+def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
+    """Pad for a convolution to make sure that the last window is full.
+    Extra padding is added at the end. This is required to ensure that we can rebuild
+    an output of the same length, as otherwise, even with padding, some time steps
+    might get removed.
+    For instance, with total padding = 4, kernel size = 4, stride = 2:
+        0 0 1 2 3 4 5 0 0   # (0s are padding)
+        1   2   3           # (output frames of a convolution, last 0 is never used)
+        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
+            1 2 3 4         # once you removed padding, we are missing one time step !
+    """
+    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+    return F.pad(x, (0, extra_padding))
+
+
+def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
+    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
+    If this is the case, we insert extra 0 padding to the right before the reflection happen.
+    """
+    length = x.shape[-1]
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    if mode == 'reflect':
+        max_pad = max(padding_left, padding_right)
+        extra_pad = 0
+        if length <= max_pad:
+            extra_pad = max_pad - length + 1
+            x = F.pad(x, (0, extra_pad))
+        padded = F.pad(x, paddings, mode, value)
+        end = padded.shape[-1] - extra_pad
+        return padded[..., :end]
+    else:
+        return F.pad(x, paddings, mode, value)
+
+
+def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
+    """Remove padding from x, handling properly zero padding. Only for 1d!
+    """
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    assert (padding_left + padding_right) <= x.shape[-1]
+    end = x.shape[-1] - padding_right
+    return x[..., padding_left: end]
+
+
+class NormConv1d(nn.Module):
+    """Wrapper around Conv1d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, causal: bool = False, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConv2d(nn.Module):
+    """Wrapper around Conv2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConvTranspose1d(nn.Module):
+    """Wrapper around ConvTranspose1d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, causal: bool = False, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.convtr(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConvTranspose2d(nn.Module):
+    """Wrapper around ConvTranspose2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
+
+    def forward(self, x):
+        x = self.convtr(x)
+        x = self.norm(x)
+        return x
+
+
+class StreamableConv1d(nn.Module):
+    """Conv1d with some builtin handling of asymmetric or causal padding
+    and normalization.
+    """
+    def __init__(self, in_channels: int, out_channels: int,
+                 kernel_size: int, stride: int = 1, dilation: int = 1,
+                 groups: int = 1, bias: bool = True, causal: bool = False,
+                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
+                 pad_mode: str = 'reflect'):
+        super().__init__()
+        # warn user on unusual setup between dilation and stride
+        if stride > 1 and dilation > 1:
+            warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
+                          f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
+        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
+                               dilation=dilation, groups=groups, bias=bias, causal=causal,
+                               norm=norm, norm_kwargs=norm_kwargs)
+        self.causal = causal
+        self.pad_mode = pad_mode
+
+    def forward(self, x):
+        B, C, T = x.shape
+        kernel_size = self.conv.conv.kernel_size[0]
+        stride = self.conv.conv.stride[0]
+        dilation = self.conv.conv.dilation[0]
+        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
+        padding_total = kernel_size - stride
+        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+        if self.causal:
+            # Left padding for causal
+            x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
+        return self.conv(x)
+
+
+class StreamableConvTranspose1d(nn.Module):
+    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
+    and normalization.
+    """
+    def __init__(self, in_channels: int, out_channels: int,
+                 kernel_size: int, stride: int = 1, causal: bool = False,
+                 norm: str = 'none', trim_right_ratio: float = 1.,
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}):
+        super().__init__()
+        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
+                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
+        self.causal = causal
+        self.trim_right_ratio = trim_right_ratio
+        assert self.causal or self.trim_right_ratio == 1., \
+            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
+        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
+
+    def forward(self, x):
+        kernel_size = self.convtr.convtr.kernel_size[0]
+        stride = self.convtr.convtr.stride[0]
+        padding_total = kernel_size - stride
+
+        y = self.convtr(x)
+
+        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
+        # removed at the very end, when keeping only the right length for the output,
+        # as removing it here would require also passing the length at the matching layer
+        # in the encoder.
+        if self.causal:
+            # Trim the padding on the right according to the specified ratio
+            # if trim_right_ratio = 1.0, trim everything from right
+            padding_right = math.ceil(padding_total * self.trim_right_ratio)
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        return y

audiocraft/modules/lstm.py

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch import nn
+
+
+class StreamableLSTM(nn.Module):
+    """LSTM without worrying about the hidden state, nor the layout of the data.
+    Expects input as convolutional layout.
+    """
+    def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
+        super().__init__()
+        self.skip = skip
+        self.lstm = nn.LSTM(dimension, dimension, num_layers)
+
+    def forward(self, x):
+        x = x.permute(2, 0, 1)
+        y, _ = self.lstm(x)
+        if self.skip:
+            y = y + x
+        y = y.permute(1, 2, 0)
+        return y

audiocraft/modules/rope.py

@@ -0,0 +1,124 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import typing as tp
+
+from torch import nn
+import torch
+
+
+class XPos(nn.Module):
+    """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
+    This applies an exponential decay to the RoPE rotation matrix.
+
+    Args:
+        dim (int): Embedding dimension.
+        smoothing (float): Smoothing factor applied to the decay rates.
+        base_scale (int): Base decay rate, given in terms of scaling time.
+        device (torch.device or None): Device on which to initialize the module.
+        dtype (torch.dtype): dtype to use to generate the embedding.
+    """
+    def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
+                 device=None, dtype: torch.dtype = torch.float32):
+        super().__init__()
+        assert dim % 2 == 0
+        assert dtype in [torch.float64, torch.float32]
+        self.dtype = dtype
+        self.base_scale = base_scale
+
+        half_dim = dim // 2
+        adim = torch.arange(half_dim, device=device, dtype=dtype)
+        decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
+        self.register_buffer("decay_rates", decay_rates)
+        self.decay: tp.Optional[torch.Tensor] = None
+
+    def get_decay(self, start: int, end: int):
+        """Create complex decay tensor, cache values for fast computation.
+        """
+        if self.decay is None or end > self.decay.shape[0]:
+            assert isinstance(self.decay_rates, torch.Tensor)  # Satisfy type checker.
+            idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
+            power = idx / self.base_scale
+            scale = self.decay_rates ** power.unsqueeze(-1)
+            self.decay = torch.polar(scale, torch.zeros_like(scale))
+        return self.decay[start:end]  # [T, C/2]
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
+
+    Args:
+        dim (int): Embedding dimension (twice the number of frequencies).
+        max_period (float): Maximum period of the rotation frequencies.
+        xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
+        scale (float): Scale of positional embedding, set to 0 to deactivate.
+        device (torch.device or None): Device on which to initialize the module.
+        dtype (torch.dtype): dtype to use to generate the embedding.
+    """
+    def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
+                 scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
+        super().__init__()
+        assert dim % 2 == 0
+        self.scale = scale
+        assert dtype in [torch.float64, torch.float32]
+        self.dtype = dtype
+
+        adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
+        frequencies = 1.0 / (max_period ** (adim / dim))
+        self.register_buffer("frequencies", frequencies)
+        self.rotation: tp.Optional[torch.Tensor] = None
+
+        self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
+
+    def get_rotation(self, start: int, end: int):
+        """Create complex rotation tensor, cache values for fast computation.
+        """
+        if self.rotation is None or end > self.rotation.shape[0]:
+            assert isinstance(self.frequencies, torch.Tensor)  # Satisfy type checker.
+            idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
+            angles = torch.outer(idx, self.frequencies)
+            self.rotation = torch.polar(torch.ones_like(angles), angles)
+        return self.rotation[start:end]
+
+    def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False):
+        """Apply rope rotation to query or key tensor.
+        """
+        T = x.shape[1]
+        rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2)
+
+        if self.xpos:
+            decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2)
+        else:
+            decay = 1.0
+
+        if invert_decay:
+            decay = decay ** -1
+
+        x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
+        scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
+        x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2)
+
+        return x_out.type_as(x)
+
+    def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0):
+        """ Apply rope rotation to both query and key tensors.
+        Supports streaming mode, in which query and key are not expected to have the same shape.
+        In streaming mode, key will be of legnth [P + C] with P the cached past timesteps, but
+        query will be [C] (typically C == 1).
+
+        Args:
+            query (torch.Tensor): Query to rotate.
+            key (torch.Tensor): Key to rotate.
+            start (int): Start index of the sequence for time offset.
+        """
+        query_timesteps = query.shape[1]
+        key_timesteps = key.shape[1]
+        streaming_offset = key_timesteps - query_timesteps
+
+        query_out = self.rotate(query, start + streaming_offset)
+        key_out = self.rotate(key, start, invert_decay=True)
+
+        return query_out, key_out

audiocraft/modules/seanet.py

@@ -0,0 +1,258 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import typing as tp
+
+import numpy as np
+import torch.nn as nn
+
+from .conv import StreamableConv1d, StreamableConvTranspose1d
+from .lstm import StreamableLSTM
+
+
+class SEANetResnetBlock(nn.Module):
+    """Residual block from SEANet model.
+
+    Args:
+        dim (int): Dimension of the input/output.
+        kernel_sizes (list): List of kernel sizes for the convolutions.
+        dilations (list): List of dilations for the convolutions.
+        activation (str): Activation function.
+        activation_params (dict): Parameters to provide to the activation function.
+        norm (str): Normalization method.
+        norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution.
+        causal (bool): Whether to use fully causal convolution.
+        pad_mode (str): Padding mode for the convolutions.
+        compress (int): Reduced dimensionality in residual branches (from Demucs v3).
+        true_skip (bool): Whether to use true skip connection or a simple
+            (streamable) convolution as the skip connection.
+    """
+    def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1],
+                 activation: str = 'ELU', activation_params: dict = {'alpha': 1.0},
+                 norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False,
+                 pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True):
+        super().__init__()
+        assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations'
+        act = getattr(nn, activation)
+        hidden = dim // compress
+        block = []
+        for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)):
+            in_chs = dim if i == 0 else hidden
+            out_chs = dim if i == len(kernel_sizes) - 1 else hidden
+            block += [
+                act(**activation_params),
+                StreamableConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation,
+                                 norm=norm, norm_kwargs=norm_params,
+                                 causal=causal, pad_mode=pad_mode),
+            ]
+        self.block = nn.Sequential(*block)
+        self.shortcut: nn.Module
+        if true_skip:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = StreamableConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params,
+                                             causal=causal, pad_mode=pad_mode)
+
+    def forward(self, x):
+        return self.shortcut(x) + self.block(x)
+
+
+class SEANetEncoder(nn.Module):
+    """SEANet encoder.
+
+    Args:
+        channels (int): Audio channels.
+        dimension (int): Intermediate representation dimension.
+        n_filters (int): Base width for the model.
+        n_residual_layers (int): nb of residual layers.
+        ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of
+            upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here
+            that must match the decoder order. We use the decoder order as some models may only employ the decoder.
+        activation (str): Activation function.
+        activation_params (dict): Parameters to provide to the activation function.
+        norm (str): Normalization method.
+        norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution.
+        kernel_size (int): Kernel size for the initial convolution.
+        last_kernel_size (int): Kernel size for the initial convolution.
+        residual_kernel_size (int): Kernel size for the residual layers.
+        dilation_base (int): How much to increase the dilation with each layer.
+        causal (bool): Whether to use fully causal convolution.
+        pad_mode (str): Padding mode for the convolutions.
+        true_skip (bool): Whether to use true skip connection or a simple
+            (streamable) convolution as the skip connection in the residual network blocks.
+        compress (int): Reduced dimensionality in residual branches (from Demucs v3).
+        lstm (int): Number of LSTM layers at the end of the encoder.
+        disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm.
+            For the encoder, it corresponds to the N first blocks.
+    """
+    def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3,
+                 ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0},
+                 norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7,
+                 last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False,
+                 pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0,
+                 disable_norm_outer_blocks: int = 0):
+        super().__init__()
+        self.channels = channels
+        self.dimension = dimension
+        self.n_filters = n_filters
+        self.ratios = list(reversed(ratios))
+        del ratios
+        self.n_residual_layers = n_residual_layers
+        self.hop_length = np.prod(self.ratios)
+        self.n_blocks = len(self.ratios) + 2  # first and last conv + residual blocks
+        self.disable_norm_outer_blocks = disable_norm_outer_blocks
+        assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \
+            "Number of blocks for which to disable norm is invalid." \
+            "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0."
+
+        act = getattr(nn, activation)
+        mult = 1
+        model: tp.List[nn.Module] = [
+            StreamableConv1d(channels, mult * n_filters, kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks >= 1 else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+        # Downsample to raw audio scale
+        for i, ratio in enumerate(self.ratios):
+            block_norm = 'none' if self.disable_norm_outer_blocks >= i + 2 else norm
+            # Add residual layers
+            for j in range(n_residual_layers):
+                model += [
+                    SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1],
+                                      dilations=[dilation_base ** j, 1],
+                                      norm=block_norm, norm_params=norm_params,
+                                      activation=activation, activation_params=activation_params,
+                                      causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)]
+
+            # Add downsampling layers
+            model += [
+                act(**activation_params),
+                StreamableConv1d(mult * n_filters, mult * n_filters * 2,
+                                 kernel_size=ratio * 2, stride=ratio,
+                                 norm=block_norm, norm_kwargs=norm_params,
+                                 causal=causal, pad_mode=pad_mode),
+            ]
+            mult *= 2
+
+        if lstm:
+            model += [StreamableLSTM(mult * n_filters, num_layers=lstm)]
+
+        model += [
+            act(**activation_params),
+            StreamableConv1d(mult * n_filters, dimension, last_kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class SEANetDecoder(nn.Module):
+    """SEANet decoder.
+
+    Args:
+        channels (int): Audio channels.
+        dimension (int): Intermediate representation dimension.
+        n_filters (int): Base width for the model.
+        n_residual_layers (int): nb of residual layers.
+        ratios (Sequence[int]): kernel size and stride ratios.
+        activation (str): Activation function.
+        activation_params (dict): Parameters to provide to the activation function.
+        final_activation (str): Final activation function after all convolutions.
+        final_activation_params (dict): Parameters to provide to the activation function.
+        norm (str): Normalization method.
+        norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution.
+        kernel_size (int): Kernel size for the initial convolution.
+        last_kernel_size (int): Kernel size for the initial convolution.
+        residual_kernel_size (int): Kernel size for the residual layers.
+        dilation_base (int): How much to increase the dilation with each layer.
+        causal (bool): Whether to use fully causal convolution.
+        pad_mode (str): Padding mode for the convolutions.
+        true_skip (bool): Whether to use true skip connection or a simple.
+            (streamable) convolution as the skip connection in the residual network blocks.
+        compress (int): Reduced dimensionality in residual branches (from Demucs v3).
+        lstm (int): Number of LSTM layers at the end of the encoder.
+        disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm.
+            For the decoder, it corresponds to the N last blocks.
+        trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup.
+            If equal to 1.0, it means that all the trimming is done at the right.
+    """
+    def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3,
+                 ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0},
+                 final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None,
+                 norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7,
+                 last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False,
+                 pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0,
+                 disable_norm_outer_blocks: int = 0, trim_right_ratio: float = 1.0):
+        super().__init__()
+        self.dimension = dimension
+        self.channels = channels
+        self.n_filters = n_filters
+        self.ratios = ratios
+        del ratios
+        self.n_residual_layers = n_residual_layers
+        self.hop_length = np.prod(self.ratios)
+        self.n_blocks = len(self.ratios) + 2  # first and last conv + residual blocks
+        self.disable_norm_outer_blocks = disable_norm_outer_blocks
+        assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \
+            "Number of blocks for which to disable norm is invalid." \
+            "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0."
+
+        act = getattr(nn, activation)
+        mult = int(2 ** len(self.ratios))
+        model: tp.List[nn.Module] = [
+            StreamableConv1d(dimension, mult * n_filters, kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+
+        if lstm:
+            model += [StreamableLSTM(mult * n_filters, num_layers=lstm)]
+
+        # Upsample to raw audio scale
+        for i, ratio in enumerate(self.ratios):
+            block_norm = 'none' if self.disable_norm_outer_blocks >= self.n_blocks - (i + 1) else norm
+            # Add upsampling layers
+            model += [
+                act(**activation_params),
+                StreamableConvTranspose1d(mult * n_filters, mult * n_filters // 2,
+                                          kernel_size=ratio * 2, stride=ratio,
+                                          norm=block_norm, norm_kwargs=norm_params,
+                                          causal=causal, trim_right_ratio=trim_right_ratio),
+            ]
+            # Add residual layers
+            for j in range(n_residual_layers):
+                model += [
+                    SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1],
+                                      dilations=[dilation_base ** j, 1],
+                                      activation=activation, activation_params=activation_params,
+                                      norm=block_norm, norm_params=norm_params, causal=causal,
+                                      pad_mode=pad_mode, compress=compress, true_skip=true_skip)]
+
+            mult //= 2
+
+        # Add final layers
+        model += [
+            act(**activation_params),
+            StreamableConv1d(n_filters, channels, last_kernel_size,
+                             norm='none' if self.disable_norm_outer_blocks >= 1 else norm,
+                             norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode)
+        ]
+        # Add optional final activation to decoder (eg. tanh)
+        if final_activation is not None:
+            final_act = getattr(nn, final_activation)
+            final_activation_params = final_activation_params or {}
+            model += [
+                final_act(**final_activation_params)
+            ]
+        self.model = nn.Sequential(*model)
+
+    def forward(self, z):
+        y = self.model(z)
+        return y

audiocraft/modules/streaming.py

@@ -0,0 +1,135 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Streaming module API that should be implemented by all Streaming components,
+"""
+
+from contextlib import contextmanager
+import typing as tp
+from torch import nn
+import torch
+
+
+State = tp.Dict[str, torch.Tensor]
+
+
+class StreamingModule(nn.Module):
+    """Common API for streaming components.
+
+    Each streaming component has a streaming state, which is just a dict[str, Tensor].
+    By convention, the first dim of each tensor must be the batch size.
+    Don't use dots in the key names, as this would clash with submodules
+    (like in state_dict).
+
+    If `self._is_streaming` is True, the component should use and remember
+    the proper state inside `self._streaming_state`.
+
+    To set a streaming component in streaming state, use
+
+        with module.streaming():
+            ...
+
+    This will automatically reset the streaming state when exiting the context manager.
+    This also automatically propagates to all streaming children module.
+
+    Some module might also implement the `StreamingModule.flush` method, although
+    this one is trickier, as all parents module must be StreamingModule and implement
+    it as well for it to work properly. See `StreamingSequential` after.
+    """
+    def __init__(self) -> None:
+        super().__init__()
+        self._streaming_state: State = {}
+        self._is_streaming = False
+
+    def _apply_named_streaming(self, fn: tp.Any):
+        for name, module in self.named_modules():
+            if isinstance(module, StreamingModule):
+                fn(name, module)
+
+    def _set_streaming(self, streaming: bool):
+        def _set_streaming(name, module):
+            module._is_streaming = streaming
+        self._apply_named_streaming(_set_streaming)
+
+    @contextmanager
+    def streaming(self):
+        """Context manager to enter streaming mode. Reset streaming state on exit.
+        """
+        self._set_streaming(True)
+        try:
+            yield
+        finally:
+            self._set_streaming(False)
+            self.reset_streaming()
+
+    def reset_streaming(self):
+        """Reset the streaming state.
+        """
+        def _reset(name: str, module: StreamingModule):
+            module._streaming_state.clear()
+
+        self._apply_named_streaming(_reset)
+
+    def get_streaming_state(self) -> State:
+        """Return the streaming state, including that of sub-modules.
+        """
+        state: State = {}
+
+        def _add(name: str, module: StreamingModule):
+            if name:
+                name += "."
+            for key, value in module._streaming_state.items():
+                state[name + key] = value
+
+        self._apply_named_streaming(_add)
+        return state
+
+    def set_streaming_state(self, state: State):
+        """Set the streaming state, including that of sub-modules.
+        """
+        state = dict(state)
+
+        def _set(name: str, module: StreamingModule):
+            if name:
+                name += "."
+            module._streaming_state.clear()
+            for key, value in list(state.items()):
+                # complexity is not ideal here, but probably fine.
+                if key.startswith(name):
+                    local_key = key[len(name):]
+                    if '.' not in local_key:
+                        module._streaming_state[local_key] = value
+                        del state[key]
+
+        self._apply_named_streaming(_set)
+        assert len(state) == 0, list(state.keys())
+
+    def flush(self, x: tp.Optional[torch.Tensor] = None):
+        """Flush any remaining outputs that were waiting for completion.
+        Typically, for convolutions, this will add the final padding
+        and process the last buffer.
+
+        This should take an optional argument `x`, which will be provided
+        if a module before this one in the streaming pipeline has already
+        spitted out a flushed out buffer.
+        """
+        if x is None:
+            return None
+        else:
+            return self(x)
+
+
+class StreamingSequential(StreamingModule, nn.Sequential):
+    """A streaming compatible alternative of `nn.Sequential`.
+    """
+    def flush(self, x: tp.Optional[torch.Tensor] = None):
+        for module in self:
+            if isinstance(module, StreamingModule):
+                x = module.flush(x)
+            elif x is not None:
+                x = module(x)
+        return x

audiocraft/modules/transformer.py

@@ -0,0 +1,704 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Transformer model, with streaming support, xformer attention support
+and easy causal attention with a potentially finite receptive field.
+
+See `StreamingTransformer` for more information.
+
+Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
+"""
+
+import typing as tp
+
+from einops import rearrange
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint as torch_checkpoint
+from xformers import ops
+
+from .rope import RotaryEmbedding
+from .streaming import StreamingModule
+
+
+def _is_profiled() -> bool:
+    # Return true if we are currently running with a xformers profiler activated.
+    try:
+        from xformers.profiler import profiler
+    except ImportError:
+        return False
+    return profiler._Profiler._CURRENT_PROFILER is not None
+
+
+def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
+    """Create normalization module for transformer encoder layer.
+
+    Args:
+        norm_type (str): Normalization method.
+        dim (int): Dimension of the normalized layer.
+        **kwargs (dict): Additional parameters for normalization layer.
+    Returns:
+        nn.Module: Normalization module.
+    """
+    if norm_type == 'layer_norm':
+        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
+    else:
+        raise ValueError(f"Unknown norm type: {norm_type}")
+
+
+def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
+                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
+    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
+
+    Args:
+        positions (torch.Tensor): LongTensor of positions.
+        dim (int): Dimension of the embedding.
+        max_period (float): Maximum period of the cosine/sine functions.
+        dtype (torch.dtype or str): dtype to use to generate the embedding.
+    Returns:
+        torch.Tensor: Sinusoidal positional embedding.
+    """
+    # We aim for BTC format
+    assert dim % 2 == 0
+    half_dim = dim // 2
+    positions = positions.to(dtype)
+    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
+    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
+    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
+    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
+
+
+def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers"""
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+class LayerScale(nn.Module):
+    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
+    This rescales diagonaly the residual outputs close to 0, with a learnt scale.
+
+    Args:
+        channels (int): Number of channels.
+        init (float): Initial scale.
+        channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
+        device (torch.device or None): Device on which to initialize the module.
+        dtype (torch.dtype or None): dtype to use to initialize the module.
+    """
+    def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
+                 device=None, dtype=None):
+        super().__init__()
+        self.channel_last = channel_last
+        self.scale = nn.Parameter(
+            torch.full((channels,), init,
+                       requires_grad=True, device=device, dtype=dtype))
+
+    def forward(self, x: torch.Tensor):
+        if self.channel_last:
+            return self.scale * x
+        else:
+            return self.scale[:, None] * x
+
+
+class StreamingMultiheadAttention(StreamingModule):
+    """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
+
+    Args:
+        embed_dim (int): Dimension to project to.
+        num_heads (int): Number of heads.
+        dropout (float): Dropout level.
+        bias (bool): Use bias in projections.
+        causal (bool): Causal mask applied automatically.
+        past_context (int or None): Receptive field for the causal mask, infinite if None.
+        custom (bool): Use custom MHA implementation, for testing / benchmarking.
+        memory_efficient (bool): Use xformers based memory efficient attention.
+        attention_as_float32 (bool): Perform the attention as float32
+            (especially important with memory_efficient as autocast won't do this automatically).
+        rope (`RotaryEmbedding` or None): Rope embedding to use.
+        cross_attention: Should be true when used as a cross attention.
+            All keys and values must be available at once, streaming is only for the queries.
+            Cannot be used with `causal` or `rope` (as it wouldn't make sens to
+            intepret the time steps in the keys relative to those in the queries).
+        safe_streaming (bool): Bug fix, will go away with xformers update.
+        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
+        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
+            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
+        device (torch.device or None): Sevice on which to initialize.
+        dtype (torch.dtype or None): dtype to use.
+    """
+    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
+                 causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
+                 memory_efficient: bool = False, attention_as_float32: bool = False,
+                 rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
+                 safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
+                 device=None, dtype=None):
+        super().__init__()
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        if past_context is not None:
+            assert causal
+
+        self.embed_dim = embed_dim
+        self.causal = causal
+        self.past_context = past_context
+        self.memory_efficient = memory_efficient
+        self.attention_as_float32 = attention_as_float32
+        self.rope = rope
+        self.cross_attention = cross_attention
+        self.safe_streaming = safe_streaming
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.kv_repeat = kv_repeat
+        if cross_attention:
+            assert not causal, "Causal cannot work with cross attention."
+            assert rope is None, "Rope cannot work with cross attention."
+
+        if memory_efficient:
+            _verify_xformers_memory_efficient_compat()
+
+        self.custom = _is_custom(custom, memory_efficient)
+        if self.custom:
+            out_dim = embed_dim
+            assert num_heads % kv_repeat == 0
+            assert not cross_attention or kv_repeat == 1
+            num_kv = num_heads // kv_repeat
+            kv_dim = (embed_dim // num_heads) * num_kv
+            out_dim += 2 * kv_dim
+            in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
+            # We try to follow the default PyTorch MHA convention, to easily compare results.
+            self.in_proj_weight = in_proj.weight
+            self.in_proj_bias = in_proj.bias
+            if bias:
+                self.in_proj_bias.data.zero_()  # Following Pytorch convention
+            self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
+            if bias:
+                self.out_proj.bias.data.zero_()
+        else:
+            assert not qk_layer_norm
+            assert kv_repeat == 1
+            self.mha = nn.MultiheadAttention(
+                embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
+                **factory_kwargs)
+        self.qk_layer_norm = qk_layer_norm
+        if qk_layer_norm:
+            assert self.custom
+            assert kv_repeat == 1
+            ln_dim = embed_dim
+            self.q_layer_norm = nn.LayerNorm(ln_dim)
+            self.k_layer_norm = nn.LayerNorm(ln_dim)
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        if not self.custom:
+            # Support compat with regular MHA
+            keys = [n for n, _ in self.mha.named_parameters()]
+            for key in keys:
+                if prefix + key in state_dict:
+                    state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
+        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
+
+    def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
+        # Return a causal mask, accounting for potentially stored past keys/values
+        # We actually return a bias for the attention score, as this has the same
+        # convention both in the builtin MHA in Pytorch, and Xformers functions.
+        if self.memory_efficient:
+            from xformers.ops import LowerTriangularMask
+            if current_steps == 1:
+                # If we only have one step, then we do not need a mask.
+                return None
+            elif 'past_keys' in self._streaming_state:
+                raise RuntimeError('Not supported at the moment')
+            else:
+                # Then we can safely use a lower triangular mask
+                return LowerTriangularMask()
+        if self._streaming_state:
+            past_keys = self._streaming_state['past_keys']
+            past_steps = past_keys.shape[1]
+        else:
+            past_steps = 0
+
+        queries_pos = torch.arange(
+            past_steps, current_steps + past_steps, device=device).view(-1, 1)
+        keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
+        delta = queries_pos - keys_pos
+        valid = delta >= 0
+        if self.past_context is not None:
+            valid &= (delta <= self.past_context)
+        return torch.where(
+            valid,
+            torch.zeros([], device=device, dtype=dtype),
+            torch.full([], float('-inf'), device=device, dtype=dtype))
+
+    def _complete_kv(self, k, v):
+        if self.cross_attention:
+            # With cross attention we assume all keys and values
+            # are already available, and streaming is with respect
+            # to the queries only.
+            return k, v
+        # Complete the key/value pair using the streaming state.
+        if self._streaming_state:
+            pk = self._streaming_state['past_keys']
+            nk = torch.cat([pk, k], dim=1)
+            if v is k:
+                nv = nk
+            else:
+                pv = self._streaming_state['past_values']
+                nv = torch.cat([pv, v], dim=1)
+        else:
+            nk = k
+            nv = v
+
+        assert nk.shape[1] == nv.shape[1]
+        offset = 0
+        if self.past_context is not None:
+            offset = max(0, nk.shape[1] - self.past_context)
+        if self._is_streaming:
+            self._streaming_state['past_keys'] = nk[:, offset:]
+            if v is not k:
+                self._streaming_state['past_values'] = nv[:, offset:]
+            if 'offset' in self._streaming_state:
+                self._streaming_state['offset'] += offset
+            else:
+                self._streaming_state['offset'] = torch.tensor(0)
+        return nk, nv
+
+    def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
+        # Apply rope embeddings to query and key tensors.
+        assert self.rope is not None
+        if 'past_keys' in self._streaming_state:
+            past_keys_offset = self._streaming_state['past_keys'].shape[1]
+        else:
+            past_keys_offset = 0
+        if 'offset' in self._streaming_state:
+            past_context_offset = int(self._streaming_state['offset'].item())
+        else:
+            past_context_offset = 0
+        streaming_offset = past_context_offset + past_keys_offset
+        return self.rope.rotate_qk(query, key, start=streaming_offset)
+
+    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
+                key_padding_mask=None, need_weights=False, attn_mask=None,
+                average_attn_weights=True, is_causal=False):
+        assert attn_mask is None
+        assert not is_causal, ("new param added in torch 2.0.1 not supported, "
+                               "use the causal args in the constructor.")
+
+        dtype = query.dtype
+        if self._is_streaming:
+            assert self.causal or self.cross_attention, \
+                "Streaming only available for causal or cross attention"
+
+        if self.causal:
+            # At the moment we specialize only for the self-attention case.
+            assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
+            assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
+            attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
+
+        if self.custom:
+            # custom implementation
+            assert need_weights is False
+            assert key_padding_mask is None
+            if self.cross_attention:
+                # Different queries, keys, values, we have to spit manually the weights
+                # before applying the linear.
+                dim = self.in_proj_weight.shape[0] // 3
+                if self.in_proj_bias is None:
+                    bias_q, bias_k, bias_v = None, None, None
+                else:
+                    bias_q = self.in_proj_bias[:dim]
+                    bias_k = self.in_proj_bias[dim: 2 * dim]
+                    bias_v = self.in_proj_bias[2 * dim:]
+                q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
+                # todo: when streaming, we could actually save k, v and check the shape actually match.
+                k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
+                v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
+                if self.qk_layer_norm is True:
+                    q = self.q_layer_norm(q)
+                    k = self.k_layer_norm(k)
+                # q, k, v = [rearrange(x, "b t (h d) -> (b h) t d", h=self.num_heads) for x in [q, k, v]]
+                q, k, v = [rearrange(x, "b t (h d) -> b t h d", h=self.num_heads) for x in [q, k, v]]
+            else:
+                if not _is_profiled():
+                    # profiling breaks that propertysomehow.
+                    assert query is key, "specialized implementation"
+                    assert value is key, "specialized implementation"
+                projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
+                if self.kv_repeat == 1:
+                    packed = rearrange(projected, "b t (p h d) -> b t p h d", p=3, h=self.num_heads)
+                    q, k, v = ops.unbind(packed, dim=2)
+                else:
+                    embed_dim = self.embed_dim
+                    per_head_dim = (embed_dim // self.num_heads)
+                    kv_heads = self.num_heads // self.kv_repeat
+                    q = projected[:, :, :embed_dim]
+                    start = embed_dim
+                    end = start + per_head_dim * kv_heads
+                    k = projected[:, :, start: end]
+                    v = projected[:, :, end:]
+                    q = rearrange(q, "b t (h d) -> b t h d", h=self.num_heads)
+                    k = rearrange(k, "b t (h d) -> b t h d", h=kv_heads)
+                    v = rearrange(v, "b t (h d) -> b t h d", h=kv_heads)
+
+                if self.qk_layer_norm is True:
+                    assert self.kv_repeat == 1
+                    q, k = [rearrange(x, "b t h d -> b t (h d)") for x in [q, k]]
+                    q = self.q_layer_norm(q)
+                    k = self.k_layer_norm(k)
+                    q, k = [rearrange(x, "b t (h d) -> b t h d", h=self.num_heads) for x in [q, k]]
+                if self.rope:
+                    q, k = self._apply_rope(q, k)
+                k, v = self._complete_kv(k, v)
+                if self.kv_repeat > 1:
+                    k = expand_repeated_kv(k, self.kv_repeat)
+                    v = expand_repeated_kv(v, self.kv_repeat)
+            if self.attention_as_float32:
+                q, k, v = [x.float() for x in [q, k, v]]
+            if self.memory_efficient:
+                p = self.dropout if self.training else 0
+                x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
+            else:
+                # We include the dot product as float32, for consistency
+                # with the other implementations that include that step
+                # as part of the attention. Note that when using `autocast`,
+                # the einsums would be done as bfloat16, but the softmax
+                # would be done as bfloat16, so `attention_as_float32` will
+                # extend a bit the range of operations done in float32,
+                # although this should make no difference.
+                q = q / q.shape[-1] ** 0.5
+                if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
+                    with torch.autocast(device_type=q.device.type, dtype=torch.float32):
+                        pre_w = torch.einsum("bqhc,bkhc->bhqk", q, k)
+                else:
+                    pre_w = torch.einsum("bqhc,bkhc->bhqk", q, k)
+                if attn_mask is not None:
+                    pre_w = pre_w + attn_mask
+                w = torch.softmax(pre_w, dim=-1)
+                w = F.dropout(w, self.dropout, training=self.training).to(v)
+                x = torch.einsum("bhqk,bkhc->bqhc", w, v)
+            x = x.to(dtype)
+            x = rearrange(x, "b t h d -> b t (h d)", h=self.num_heads)
+            x = self.out_proj(x)
+        else:
+            key, value = self._complete_kv(key, value)
+            if self.attention_as_float32:
+                query, key, value = [x.float() for x in [query, key, value]]
+            x, _ = self.mha(
+                query, key, value, key_padding_mask,
+                need_weights, attn_mask, average_attn_weights)
+            x = x.to(dtype)
+
+        return x, None
+
+
+class StreamingTransformerLayer(nn.TransformerEncoderLayer):
+    """TransformerLayer with Streaming / Causal support.
+    This also integrates cross_attention, when passing `cross_attention=True`,
+    rather than having two separate classes like in PyTorch.
+
+    Args:
+        d_model (int): Dimension of the data.
+        num_heads (int): Number of heads.
+        dim_feedforward (int): Intermediate dimension of FF module.
+        dropout (float): Dropout both for MHA and FF.
+        bias_ff (bool): Use bias for FF.
+        bias_attn (bool): Use bias for MHA.
+        causal (bool): Causal mask applied automatically.
+        past_context (int or None): Receptive field for the causal mask, infinite if None.
+        custom (bool): Use custom MHA implementation, for testing / benchmarking.
+        memory_efficient (bool): Use xformers based memory efficient attention.
+        attention_as_float32 (bool): Perform the attention as float32
+            (especially important with memory_efficient as autocast won't do this automatically).
+        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
+        qk_layer_norm_cross (bool): Same for the cross attention.
+        cross_attention (bool): If True, expect to get secondary input for cross-attention.
+            Cross attention will use the default MHA, as it typically won't require
+            special treatment.
+        layer_scale (float or None): If not None, LayerScale will be used with
+            the given value as initial scale.
+        rope (`RotaryEmbedding` or None): Rope embedding to use.
+        attention_dropout (float or None): If not None, separate the value of the dimension dropout
+            in FFN and of the attention dropout.
+        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
+            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
+        device (torch.device or None): Device on which to initialize.
+        dtype (torch.dtype or None): dtype to use.
+        **kwargs: See `nn.TransformerEncoderLayer`.
+    """
+    def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
+                 bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
+                 past_context: tp.Optional[int] = None, custom: bool = False,
+                 memory_efficient: bool = False, attention_as_float32: bool = False,
+                 qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
+                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
+                 rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
+                 kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
+        super().__init__(d_model, num_heads, dim_feedforward, dropout,
+                         device=device, dtype=dtype, batch_first=True, **kwargs)
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        # Redefine self_attn to our streaming multi-head attention
+        attn_kwargs: tp.Dict[str, tp.Any] = {
+            'embed_dim': d_model,
+            'num_heads': num_heads,
+            'dropout': dropout if attention_dropout is None else attention_dropout,
+            'bias': bias_attn,
+            'custom': custom,
+            'memory_efficient': memory_efficient,
+            'attention_as_float32': attention_as_float32,
+        }
+        self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
+            causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
+            kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs)  # type: ignore
+        # Redefine feedforward layers to expose bias parameter
+        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
+        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
+
+        self.layer_scale_1: nn.Module
+        self.layer_scale_2: nn.Module
+        if layer_scale is None:
+            self.layer_scale_1 = nn.Identity()
+            self.layer_scale_2 = nn.Identity()
+        else:
+            self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
+            self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
+
+        self.cross_attention: tp.Optional[nn.Module] = None
+        if cross_attention:
+            self.cross_attention = StreamingMultiheadAttention(
+                cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
+                **attn_kwargs, **factory_kwargs)
+            # Norm and dropout
+            self.dropout_cross = nn.Dropout(dropout)
+            # eps value matching that used in PyTorch reference implementation.
+            self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
+            self.layer_scale_cross: nn.Module
+            if layer_scale is None:
+                self.layer_scale_cross = nn.Identity()
+            else:
+                self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
+        self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
+        self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
+
+    def _cross_attention_block(self, src: torch.Tensor,
+                               cross_attention_src: torch.Tensor) -> torch.Tensor:
+        assert self.cross_attention is not None
+        # queries are from src, keys and values from cross_attention_src.
+        x = self.cross_attention(
+            src, cross_attention_src, cross_attention_src, need_weights=False)[0]
+        return self.dropout_cross(x)  # type: ignore
+
+    def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None,  # type: ignore
+                src_key_padding_mask: tp.Optional[torch.Tensor] = None,
+                cross_attention_src: tp.Optional[torch.Tensor] = None):
+        if self.cross_attention is None:
+            assert cross_attention_src is None
+        else:
+            assert cross_attention_src is not None
+        x = src
+        if self.norm_first:
+            x = x + self.layer_scale_1(
+                self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
+            if cross_attention_src is not None:
+                x = x + self.layer_scale_cross(
+                    self._cross_attention_block(
+                        self.norm_cross(x), cross_attention_src))
+            x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
+        else:
+            x = self.norm1(x + self.layer_scale_1(
+                self._sa_block(x, src_mask, src_key_padding_mask)))
+            if cross_attention_src is not None:
+                x = self.norm_cross(
+                    x + self.layer_scale_cross(
+                        self._cross_attention_block(src, cross_attention_src)))
+            x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
+        return x
+
+
+class StreamingTransformer(StreamingModule):
+    """Transformer with Streaming / Causal support.
+
+    Args:
+        d_model (int): Dimension of the data.
+        num_heads (int): Number of heads.
+        dim_feedforward (int): Intermediate dimension of FF module.
+        dropout (float): Dropout both for MHA and FF.
+        bias_ff (bool): Use bias for FF.
+        bias_attn (bool): Use bias for MHA.
+        causal (bool): Causal mask applied automatically.
+        past_context (int or None): Receptive field for the causal mask, infinite if None.
+        custom (bool): Use custom MHA implementation, for testing / benchmarking.
+        memory_efficient (bool): Use xformers based memory efficient attention.
+        attention_as_float32 (bool): Perform the attention as float32
+            (especially important with memory_efficient as autocast won't do this automatically).
+        cross_attention (bool): If True, expect to get secondary input for cross-attention.
+        layer_scale (float or None): If not None, LayerScale will be used
+            with the given value as initial scale.
+        positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
+        max_period (float): Maximum period of the time embedding.
+        positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
+        xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
+        lr (float or None): learning rate override through the `make_optim_group` API.
+        weight_decay (float or None): Weight_decay override through the `make_optim_group` API.
+        layer_class: (subclass of `StreamingTransformerLayer): class to use
+            to initialize the layers, allowing further customization outside of Audiocraft.
+        checkpointing (str): Checkpointing strategy to reduce memory usage.
+            No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
+            if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
+            minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
+            a policy for opting-out some operations of the checkpointing like
+            linear layers and attention, providing a middle ground between speed and memory.
+        device (torch.device or None): Device on which to initialize.
+        dtype (torch.dtype or None): dtype to use.
+        **kwargs: See `nn.TransformerEncoderLayer`.
+    """
+    def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
+                 dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
+                 causal: bool = False, past_context: tp.Optional[int] = None,
+                 custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
+                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
+                 positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
+                 xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
+                 layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
+                 checkpointing: str = 'none', device=None, dtype=None, **kwargs):
+        super().__init__()
+        assert d_model % num_heads == 0
+
+        self.positional_embedding = positional_embedding
+        self.max_period = max_period
+        self.positional_scale = positional_scale
+        self.weight_decay = weight_decay
+        self.lr = lr
+
+        assert positional_embedding in ['sin', 'rope', 'sin_rope']
+        self.rope: tp.Optional[RotaryEmbedding] = None
+        if self.positional_embedding in ['rope', 'sin_rope']:
+            assert _is_custom(custom, memory_efficient)
+            self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
+                                        xpos=xpos, scale=positional_scale, device=device)
+
+        self.checkpointing = checkpointing
+
+        assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
+        if self.checkpointing.startswith('xformers'):
+            _verify_xformers_internal_compat()
+
+        self.layers = nn.ModuleList()
+        for idx in range(num_layers):
+            self.layers.append(
+                layer_class(
+                    d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
+                    dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
+                    causal=causal, past_context=past_context, custom=custom,
+                    memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
+                    cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
+                    device=device, dtype=dtype, **kwargs))
+
+        if self.checkpointing != 'none':
+            for layer in self.layers:
+                # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
+                # backward hook inside of FSDP...
+                layer._magma_checkpointed = True  # type: ignore
+                assert layer.layer_drop == 0., "Need further checking"  # type: ignore
+
+    def _apply_layer(self, layer, *args, **kwargs):
+        method = self.checkpointing
+        if method == 'none':
+            return layer(*args, **kwargs)
+        elif method == 'torch':
+            return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
+        elif method.startswith('xformers'):
+            from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
+            if method == 'xformers_default':
+                # those operations will be saved, and not recomputed.
+                # According to Francisco we can get smarter policies but this is a good start.
+                allow_list = [
+                    "xformers.efficient_attention_forward_cutlass.default",
+                    "xformers_flash.flash_fwd.default",
+                    "aten.addmm.default",
+                    "aten.mm.default",
+                ]
+            elif method == 'xformers_mm':
+                # those operations will be saved, and not recomputed.
+                # According to Francisco we can get smarter policies but this is a good start.
+                allow_list = [
+                    "aten.addmm.default",
+                    "aten.mm.default",
+                ]
+            else:
+                raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
+            policy_fn = _get_default_policy(allow_list)
+            return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
+        else:
+            raise ValueError(f"Checkpointing method {method} is unknown.")
+
+    def forward(self, x: torch.Tensor, *args, **kwargs):
+        B, T, C = x.shape
+
+        if 'offsets' in self._streaming_state:
+            offsets = self._streaming_state['offsets']
+        else:
+            offsets = torch.zeros(B, dtype=torch.long, device=x.device)
+
+        if self.positional_embedding in ['sin', 'sin_rope']:
+            positions = torch.arange(T, device=x.device).view(1, -1, 1)
+            positions = positions + offsets.view(-1, 1, 1)
+            pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
+            x = x + self.positional_scale * pos_emb
+
+        for layer in self.layers:
+            x = self._apply_layer(layer, x, *args, **kwargs)
+
+        if self._is_streaming:
+            self._streaming_state['offsets'] = offsets + T
+
+        return x
+
+    def make_optim_group(self):
+        group = {"params": list(self.parameters())}
+        if self.lr is not None:
+            group["lr"] = self.lr
+        if self.weight_decay is not None:
+            group["weight_decay"] = self.weight_decay
+        return group
+
+
+# special attention attention related function
+
+def _verify_xformers_memory_efficient_compat():
+    try:
+        from xformers.ops import memory_efficient_attention, LowerTriangularMask  # noqa
+    except ImportError:
+        raise ImportError(
+            "xformers is not installed. Please install it and try again.\n"
+            "To install on AWS and Azure, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
+            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n"
+            "To install on FAIR Cluster, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
+            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n")
+
+
+def _verify_xformers_internal_compat():
+    try:
+        from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy  # noqa
+    except ImportError:
+        raise ImportError(
+            "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
+            "To install on AWS and Azure, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
+            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n"
+            "To install on FAIR Cluster, run \n"
+            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
+            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n")
+
+
+def _is_custom(custom: bool, memory_efficient: bool):
+    return custom or memory_efficient

audiocraft/quantization/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# flake8: noqa
+from .vq import ResidualVectorQuantizer
+from .base import BaseQuantizer, DummyQuantizer, QuantizedResult

audiocraft/quantization/base.py

@@ -0,0 +1,107 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Base class for all quantizers.
+"""
+
+from dataclasses import dataclass, field
+import typing as tp
+
+import torch
+from torch import nn
+
+
+@dataclass
+class QuantizedResult:
+    x: torch.Tensor
+    codes: torch.Tensor
+    bandwidth: torch.Tensor  # bandwidth in kb/s used, per batch item.
+    penalty: tp.Optional[torch.Tensor] = None
+    metrics: dict = field(default_factory=dict)
+
+
+class BaseQuantizer(nn.Module):
+    """Base class for quantizers.
+    """
+
+    def forward(self, x: torch.Tensor, frame_rate: int) -> QuantizedResult:
+        """
+        Given input tensor x, returns first the quantized (or approximately quantized)
+        representation along with quantized codes, bandwidth, and any penalty term for the loss.
+        Finally, this returns a dict of metrics to update logging etc.
+        Frame rate must be passed so that the bandwidth is properly computed.
+        """
+        raise NotImplementedError()
+
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        """Encode a given input tensor with the specified sample rate at the given bandwidth.
+        """
+        raise NotImplementedError()
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        """Decode the given codes to the quantized representation.
+        """
+        raise NotImplementedError()
+
+    @property
+    def total_codebooks(self):
+        """Total number of codebooks.
+        """
+        raise NotImplementedError()
+
+    @property
+    def num_codebooks(self):
+        """Number of active codebooks.
+        """
+        raise NotImplementedError()
+
+    def set_num_codebooks(self, n: int):
+        """Set the number of active codebooks.
+        """
+        raise NotImplementedError()
+
+
+class DummyQuantizer(BaseQuantizer):
+    """Fake quantizer that actually does not perform any quantization.
+    """
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x: torch.Tensor, frame_rate: int):
+        q = x.unsqueeze(1)
+        return QuantizedResult(x, q, torch.tensor(q.numel() * 32 * frame_rate / 1000 / len(x)).to(x))
+
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        """Encode a given input tensor with the specified sample rate at the given bandwidth.
+        In the case of the DummyQuantizer, the codes are actually identical
+        to the input and resulting quantized representation as no quantization is done.
+        """
+        return x.unsqueeze(1)
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        """Decode the given codes to the quantized representation.
+        In the case of the DummyQuantizer, the codes are actually identical
+        to the input and resulting quantized representation as no quantization is done.
+        """
+        return codes.squeeze(1)
+
+    @property
+    def total_codebooks(self):
+        """Total number of codebooks.
+        """
+        return 1
+
+    @property
+    def num_codebooks(self):
+        """Total number of codebooks.
+        """
+        return self.total_codebooks
+
+    def set_num_codebooks(self, n: int):
+        """Set the number of active codebooks.
+        """
+        raise AttributeError("Cannot override the number of codebooks for the dummy quantizer")

audiocraft/quantization/core_vq.py

@@ -0,0 +1,400 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import typing as tp
+
+from einops import rearrange, repeat
+import flashy
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+
+def exists(val: tp.Optional[tp.Any]) -> bool:
+    return val is not None
+
+
+def default(val: tp.Any, d: tp.Any) -> tp.Any:
+    return val if exists(val) else d
+
+
+def l2norm(t):
+    return F.normalize(t, p=2, dim=-1)
+
+
+def ema_inplace(moving_avg, new, decay: float):
+    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
+
+
+def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
+    return (x + epsilon) / (x.sum() + n_categories * epsilon)
+
+
+def uniform_init(*shape: int):
+    t = torch.empty(shape)
+    nn.init.kaiming_uniform_(t)
+    return t
+
+
+def sample_vectors(samples, num: int):
+    num_samples, device = samples.shape[0], samples.device
+
+    if num_samples >= num:
+        indices = torch.randperm(num_samples, device=device)[:num]
+    else:
+        indices = torch.randint(0, num_samples, (num,), device=device)
+
+    return samples[indices]
+
+
+def kmeans(samples, num_clusters: int, num_iters: int = 10):
+    dim, dtype = samples.shape[-1], samples.dtype
+
+    means = sample_vectors(samples, num_clusters)
+
+    for _ in range(num_iters):
+        diffs = rearrange(samples, "n d -> n () d") - rearrange(
+            means, "c d -> () c d"
+        )
+        dists = -(diffs ** 2).sum(dim=-1)
+
+        buckets = dists.max(dim=-1).indices
+        bins = torch.bincount(buckets, minlength=num_clusters)
+        zero_mask = bins == 0
+        bins_min_clamped = bins.masked_fill(zero_mask, 1)
+
+        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
+        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
+        new_means = new_means / bins_min_clamped[..., None]
+
+        means = torch.where(zero_mask[..., None], means, new_means)
+
+    return means, bins
+
+
+def orthgonal_loss_fn(t):
+    # eq (2) from https://arxiv.org/abs/2112.00384
+    n = t.shape[0]
+    normed_codes = l2norm(t)
+    identity = torch.eye(n, device=t.device)
+    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
+    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
+
+
+class EuclideanCodebook(nn.Module):
+    """Codebook with Euclidean distance.
+
+    Args:
+        dim (int): Dimension.
+        codebook_size (int): Codebook size.
+        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
+            If set to true, run the k-means algorithm on the first training batch and use
+            the learned centroids as initialization.
+        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
+        decay (float): Decay for exponential moving average over the codebooks.
+        epsilon (float): Epsilon value for numerical stability.
+        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
+            that have an exponential moving average cluster size less than the specified threshold with
+            randomly selected vector from the current batch.
+    """
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        kmeans_init: int = False,
+        kmeans_iters: int = 10,
+        decay: float = 0.8,
+        epsilon: float = 1e-5,
+        threshold_ema_dead_code: int = 2,
+    ):
+        super().__init__()
+        self.decay = decay
+        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
+        embed = init_fn(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+
+        self.kmeans_iters = kmeans_iters
+        self.epsilon = epsilon
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+
+        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
+        self.register_buffer("cluster_size", torch.zeros(codebook_size))
+        self.register_buffer("embed", embed)
+        self.register_buffer("embed_avg", embed.clone())
+
+    @torch.jit.ignore
+    def init_embed_(self, data):
+        if self.inited:
+            return
+
+        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
+        self.embed.data.copy_(embed)
+        self.embed_avg.data.copy_(embed.clone())
+        self.cluster_size.data.copy_(cluster_size)
+        self.inited.data.copy_(torch.Tensor([True]))
+        # Make sure all buffers across workers are in sync after initialization
+        flashy.distrib.broadcast_tensors(self.buffers())
+
+    def replace_(self, samples, mask):
+        modified_codebook = torch.where(
+            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
+        )
+        self.embed.data.copy_(modified_codebook)
+
+    def expire_codes_(self, batch_samples):
+        if self.threshold_ema_dead_code == 0:
+            return
+
+        expired_codes = self.cluster_size < self.threshold_ema_dead_code
+        if not torch.any(expired_codes):
+            return
+
+        batch_samples = rearrange(batch_samples, "... d -> (...) d")
+        self.replace_(batch_samples, mask=expired_codes)
+        flashy.distrib.broadcast_tensors(self.buffers())
+
+    def preprocess(self, x):
+        x = rearrange(x, "... d -> (...) d")
+        return x
+
+    def quantize(self, x):
+        embed = self.embed.t()
+        dist = -(
+            x.pow(2).sum(1, keepdim=True)
+            - 2 * x @ embed
+            + embed.pow(2).sum(0, keepdim=True)
+        )
+        embed_ind = dist.max(dim=-1).indices
+        return embed_ind
+
+    def postprocess_emb(self, embed_ind, shape):
+        return embed_ind.view(*shape[:-1])
+
+    def dequantize(self, embed_ind):
+        quantize = F.embedding(embed_ind, self.embed)
+        return quantize
+
+    def encode(self, x):
+        shape = x.shape
+        # pre-process
+        x = self.preprocess(x)
+        # quantize
+        embed_ind = self.quantize(x)
+        # post-process
+        embed_ind = self.postprocess_emb(embed_ind, shape)
+        return embed_ind
+
+    def decode(self, embed_ind):
+        quantize = self.dequantize(embed_ind)
+        return quantize
+
+    def forward(self, x):
+        shape, dtype = x.shape, x.dtype
+        x = self.preprocess(x)
+        self.init_embed_(x)
+
+        embed_ind = self.quantize(x)
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
+        embed_ind = self.postprocess_emb(embed_ind, shape)
+        quantize = self.dequantize(embed_ind)
+
+        if self.training:
+            # We do the expiry of code at that point as buffers are in sync
+            # and all the workers will take the same decision.
+            self.expire_codes_(x)
+            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
+            embed_sum = x.t() @ embed_onehot
+            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
+            cluster_size = (
+                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
+                * self.cluster_size.sum()
+            )
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
+            self.embed.data.copy_(embed_normalized)
+
+        return quantize, embed_ind
+
+
+class VectorQuantization(nn.Module):
+    """Vector quantization implementation.
+    Currently supports only euclidean distance.
+
+    Args:
+        dim (int): Dimension
+        codebook_size (int): Codebook size
+        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
+        decay (float): Decay for exponential moving average over the codebooks.
+        epsilon (float): Epsilon value for numerical stability.
+        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
+        kmeans_iters (int): Number of iterations used for kmeans initialization.
+        threshold_ema_dead_code (int):
+        channels_last (bool): Channels are the last dimension in the input tensors.
+        commitment_weight (float): Weight for commitment loss.
+        orthogonal_reg_weight (float): Orthogonal regularization weights.
+        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
+        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider
+            for orthogonal regulariation.
+        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
+            that have an exponential moving average cluster size less than the specified threshold with
+            randomly selected vector from the current batch.
+    """
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        codebook_dim: tp.Optional[int] = None,
+        decay: float = 0.8,
+        epsilon: float = 1e-5,
+        kmeans_init: bool = False,
+        kmeans_iters: int = 10,
+        threshold_ema_dead_code: int = 2,
+        channels_last: bool = False,
+        commitment_weight: float = 1.,
+        orthogonal_reg_weight: float = 0.0,
+        orthogonal_reg_active_codes_only: bool = False,
+        orthogonal_reg_max_codes: tp.Optional[int] = None,
+    ):
+        super().__init__()
+        _codebook_dim: int = default(codebook_dim, dim)
+
+        requires_projection = _codebook_dim != dim
+        self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
+        self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
+
+        self.epsilon = epsilon
+        self.commitment_weight = commitment_weight
+
+        self.orthogonal_reg_weight = orthogonal_reg_weight
+        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
+        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
+
+        self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
+                                           kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
+                                           decay=decay, epsilon=epsilon,
+                                           threshold_ema_dead_code=threshold_ema_dead_code)
+        self.codebook_size = codebook_size
+
+        self.channels_last = channels_last
+
+    @property
+    def codebook(self):
+        return self._codebook.embed
+
+    @property
+    def inited(self):
+        return self._codebook.inited
+
+    def _preprocess(self, x):
+        if not self.channels_last:
+            x = rearrange(x, "b d n -> b n d")
+        return x
+
+    def _postprocess(self, quantize):
+        if not self.channels_last:
+            quantize = rearrange(quantize, "b n d -> b d n")
+        return quantize
+
+    def encode(self, x):
+        x = self._preprocess(x)
+        x = self.project_in(x)
+        embed_in = self._codebook.encode(x)
+        return embed_in
+
+    def decode(self, embed_ind):
+        quantize = self._codebook.decode(embed_ind)
+        quantize = self.project_out(quantize)
+        quantize = self._postprocess(quantize)
+        return quantize
+
+    def forward(self, x):
+        device = x.device
+        x = self._preprocess(x)
+
+        x = self.project_in(x)
+        quantize, embed_ind = self._codebook(x)
+
+        if self.training:
+            quantize = x + (quantize - x).detach()
+
+        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
+
+        if self.training:
+            if self.commitment_weight > 0:
+                commit_loss = F.mse_loss(quantize.detach(), x)
+                loss = loss + commit_loss * self.commitment_weight
+
+            if self.orthogonal_reg_weight > 0:
+                codebook = self.codebook
+
+                if self.orthogonal_reg_active_codes_only:
+                    # only calculate orthogonal loss for the activated codes for this batch
+                    unique_code_ids = torch.unique(embed_ind)
+                    codebook = codebook[unique_code_ids]
+
+                num_codes = codebook.shape[0]
+                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
+                    rand_ids = torch.randperm(num_codes, device=device)[:self.orthogonal_reg_max_codes]
+                    codebook = codebook[rand_ids]
+
+                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
+                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
+
+        quantize = self.project_out(quantize)
+        quantize = self._postprocess(quantize)
+
+        return quantize, embed_ind, loss
+
+
+class ResidualVectorQuantization(nn.Module):
+    """Residual vector quantization implementation.
+
+    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
+    """
+    def __init__(self, *, num_quantizers, **kwargs):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
+        )
+
+    def forward(self, x, n_q: tp.Optional[int] = None):
+        quantized_out = 0.0
+        residual = x
+
+        all_losses = []
+        all_indices = []
+
+        n_q = n_q or len(self.layers)
+
+        for i, layer in enumerate(self.layers[:n_q]):
+            quantized, indices, loss = layer(residual)
+            residual = residual - quantized
+            quantized_out = quantized_out + quantized
+            all_indices.append(indices)
+            all_losses.append(loss)
+
+        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
+        return quantized_out, out_indices, out_losses
+
+    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
+        residual = x
+        all_indices = []
+        n_q = n_q or len(self.layers)
+        for layer in self.layers[:n_q]:
+            indices = layer.encode(residual)
+            quantized = layer.decode(indices)
+            residual = residual - quantized
+            all_indices.append(indices)
+        out_indices = torch.stack(all_indices)
+        return out_indices
+
+    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
+        quantized_out = torch.tensor(0.0, device=q_indices.device)
+        for i, indices in enumerate(q_indices):
+            layer = self.layers[i]
+            quantized = layer.decode(indices)
+            quantized_out = quantized_out + quantized
+        return quantized_out

audiocraft/quantization/vq.py

@@ -0,0 +1,116 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import typing as tp
+
+import torch
+
+from .base import BaseQuantizer, QuantizedResult
+from .core_vq import ResidualVectorQuantization
+
+
+class ResidualVectorQuantizer(BaseQuantizer):
+    """Residual Vector Quantizer.
+
+    Args:
+        dimension (int): Dimension of the codebooks.
+        n_q (int): Number of residual vector quantizers used.
+        q_dropout (bool): Random quantizer drop out at train time.
+        bins (int): Codebook size.
+        decay (float): Decay for exponential moving average over the codebooks.
+        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
+        kmeans_iters (int): Number of iterations used for kmeans initialization.
+        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
+            that have an exponential moving average cluster size less than the specified threshold with
+            randomly selected vector from the current batch.
+        orthogonal_reg_weight (float): Orthogonal regularization weights.
+        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
+        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
+            for orthogonal regulariation.
+    """
+    def __init__(
+        self,
+        dimension: int = 256,
+        n_q: int = 8,
+        q_dropout: bool = False,
+        bins: int = 1024,
+        decay: float = 0.99,
+        kmeans_init: bool = True,
+        kmeans_iters: int = 10,
+        threshold_ema_dead_code: int = 2,
+        orthogonal_reg_weight: float = 0.0,
+        orthogonal_reg_active_codes_only: bool = False,
+        orthogonal_reg_max_codes: tp.Optional[int] = None,
+    ):
+        super().__init__()
+        self.max_n_q = n_q
+        self.n_q = n_q
+        self.q_dropout = q_dropout
+        self.dimension = dimension
+        self.bins = bins
+        self.decay = decay
+        self.kmeans_init = kmeans_init
+        self.kmeans_iters = kmeans_iters
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.orthogonal_reg_weight = orthogonal_reg_weight
+        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
+        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
+        self.vq = ResidualVectorQuantization(
+            dim=self.dimension,
+            codebook_size=self.bins,
+            num_quantizers=self.n_q,
+            decay=self.decay,
+            kmeans_init=self.kmeans_init,
+            kmeans_iters=self.kmeans_iters,
+            threshold_ema_dead_code=self.threshold_ema_dead_code,
+            orthogonal_reg_weight=self.orthogonal_reg_weight,
+            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
+            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
+            channels_last=False
+        )
+
+    def forward(self, x: torch.Tensor, frame_rate: int):
+        n_q = self.n_q
+        if self.training and self.q_dropout:
+            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
+        bw_per_q = math.log2(self.bins) * frame_rate / 1000
+        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
+        codes = codes.transpose(0, 1)
+        # codes is [B, K, T], with T frames, K nb of codebooks.
+        bw = torch.tensor(n_q * bw_per_q).to(x)
+        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
+
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        """Encode a given input tensor with the specified frame rate at the given bandwidth.
+        The RVQ encode method sets the appropriate number of quantizer to use
+        and returns indices for each quantizer.
+        """
+        n_q = self.n_q
+        codes = self.vq.encode(x, n_q=n_q)
+        codes = codes.transpose(0, 1)
+        # codes is [B, K, T], with T frames, K nb of codebooks.
+        return codes
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        """Decode the given codes to the quantized representation.
+        """
+        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
+        codes = codes.transpose(0, 1)
+        quantized = self.vq.decode(codes)
+        return quantized
+
+    @property
+    def total_codebooks(self):
+        return self.max_n_q
+
+    @property
+    def num_codebooks(self):
+        return self.n_q
+
+    def set_num_codebooks(self, n: int):
+        assert n > 0 and n <= self.max_n_q
+        self.n_q = n

audiocraft/utils/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

audiocraft/utils/autocast.py

@@ -0,0 +1,40 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+
+class TorchAutocast:
+    """TorchAutocast utility class.
+    Allows you to enable and disable autocast. This is specially useful
+    when dealing with different architectures and clusters with different
+    levels of support.
+
+    Args:
+        enabled (bool): Whether to enable torch.autocast or not.
+        args: Additional args for torch.autocast.
+        kwargs: Additional kwargs for torch.autocast
+    """
+    def __init__(self, enabled: bool, *args, **kwargs):
+        self.autocast = torch.autocast(*args, **kwargs) if enabled else None
+
+    def __enter__(self):
+        if self.autocast is None:
+            return
+        try:
+            self.autocast.__enter__()
+        except RuntimeError:
+            device = self.autocast.device
+            dtype = self.autocast.fast_dtype
+            raise RuntimeError(
+                f"There was an error autocasting with dtype={dtype} device={device}\n"
+                "If you are on the FAIR Cluster, you might need to use autocast_dtype=float16"
+            )
+
+    def __exit__(self, *args, **kwargs):
+        if self.autocast is None:
+            return
+        self.autocast.__exit__(*args, **kwargs)

audiocraft/utils/export.py

@@ -0,0 +1,56 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Utility to export a training checkpoint to a lightweight release checkpoint.
+"""
+
+from pathlib import Path
+import typing as tp
+
+from omegaconf import OmegaConf, DictConfig
+import torch
+
+
+def _clean_lm_cfg(cfg: DictConfig):
+    OmegaConf.set_struct(cfg, False)
+    # This used to be set automatically in the LM solver, need a more robust solution
+    # for the future.
+    cfg['transformer_lm']['card'] = 2048
+    cfg['transformer_lm']['n_q'] = 4
+    # Experimental params no longer supported.
+    bad_params = ['spectral_norm_attn_iters', 'spectral_norm_ff_iters',
+                  'residual_balancer_attn', 'residual_balancer_ff', 'layer_drop']
+    for name in bad_params:
+        del cfg['transformer_lm'][name]
+    OmegaConf.set_struct(cfg, True)
+    return cfg
+
+
+def export_encodec(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
+    sig = Path(checkpoint_path).parent.name
+    assert len(sig) == 8, "Not a valid Dora signature"
+    pkg = torch.load(checkpoint_path, 'cpu')
+    new_pkg = {
+        'best_state': pkg['ema']['state']['model'],
+        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
+    }
+    out_file = Path(out_folder) / f'{sig}.th'
+    torch.save(new_pkg, out_file)
+    return out_file
+
+
+def export_lm(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
+    sig = Path(checkpoint_path).parent.name
+    assert len(sig) == 8, "Not a valid Dora signature"
+    pkg = torch.load(checkpoint_path, 'cpu')
+    new_pkg = {
+        'best_state': pkg['fsdp_best_state']['model'],
+        'xp.cfg': OmegaConf.to_yaml(_clean_lm_cfg(pkg['xp.cfg']))
+    }
+    out_file = Path(out_folder) / f'{sig}.th'
+    torch.save(new_pkg, out_file)
+    return out_file

audiocraft/utils/notebook.py

@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+try:
+    import IPython.display as ipd  # type: ignore
+except ImportError:
+    # Note in a notebook...
+    pass
+
+
+import torch
+
+
+def display_audio(samples: torch.Tensor, sample_rate: int):
+    """Renders an audio player for the given audio samples.
+
+    Args:
+        samples (torch.Tensor): a Tensor of decoded audio samples
+            with shapes [B, C, T] or [C, T]
+        sample_rate (int): sample rate audio should be displayed with.
+    """
+    assert samples.dim() == 2 or samples.dim() == 3
+
+    samples = samples.detach().cpu()
+    if samples.dim() == 2:
+        samples = samples[None, ...]
+
+    for audio in samples:
+        ipd.display(ipd.Audio(audio, rate=sample_rate))

audiocraft/utils/utils.py

@@ -0,0 +1,234 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from concurrent.futures import ProcessPoolExecutor
+from functools import wraps
+import hashlib
+import logging
+import typing as tp
+
+import flashy
+import flashy.distrib
+import omegaconf
+import torch
+from torch.nn.utils.rnn import pad_sequence
+
+
+logger = logging.getLogger(__name__)
+
+
+def dict_from_config(cfg: omegaconf.DictConfig) -> dict:
+    """Convenience function to map an omegaconf configuration to a dictionary.
+
+    Args:
+        cfg (omegaconf.DictConfig): Original configuration to map to dict.
+    Returns:
+        dict: Config as dictionary object.
+    """
+    dct = omegaconf.OmegaConf.to_container(cfg, resolve=True)
+    assert isinstance(dct, dict)
+    return dct
+
+
+def random_subset(dataset, max_samples: int, seed: int = 42) -> torch.utils.data.Subset:
+    if max_samples >= len(dataset):
+        return dataset
+
+    generator = torch.Generator().manual_seed(seed)
+    perm = torch.randperm(len(dataset), generator=generator)
+    return torch.utils.data.Subset(dataset, perm[:max_samples].tolist())
+
+
+def get_loader(dataset, num_samples: tp.Optional[int], batch_size: int,
+               num_workers: int, seed: int, **kwargs) -> torch.utils.data.DataLoader:
+    """Convenience function to load dataset into a dataloader with optional subset sampling.
+
+    Args:
+        dataset: Dataset to load.
+        num_samples (Optional[int]): Number of samples to limit subset size.
+        batch_size (int): Batch size.
+        num_workers (int): Number of workers for data loading.
+        seed (int): Random seed.
+    """
+    if num_samples is not None:
+        dataset = random_subset(dataset, num_samples, seed)
+
+    dataloader = flashy.distrib.loader(
+        dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        **kwargs
+    )
+    return dataloader
+
+
+def get_dataset_from_loader(dataloader):
+    dataset = dataloader.dataset
+    if isinstance(dataset, torch.utils.data.Subset):
+        return dataset.dataset
+    else:
+        return dataset
+
+
+def multinomial(input: torch.Tensor, num_samples: int, replacement=False, *, generator=None):
+    """torch.multinomial with arbitrary number of dimensions, and number of candidates on the last dimension.
+
+    Args:
+        input (torch.Tensor): The input tensor containing probabilities.
+        num_samples (int): Number of samples to draw.
+        replacement (bool): Whether to draw with replacement or not.
+    Keywords args:
+        generator (torch.Generator): A pseudorandom number generator for sampling.
+    Returns:
+        torch.Tensor: Last dimension contains num_samples indices
+            sampled from the multinomial probability distribution
+            located in the last dimension of tensor input.
+    """
+    input_ = input.reshape(-1, input.shape[-1])
+    output_ = torch.multinomial(input_, num_samples=num_samples, replacement=replacement, generator=generator)
+    output = output_.reshape(*list(input.shape[:-1]), -1)
+    return output
+
+
+def sample_top_k(probs: torch.Tensor, k: int) -> torch.Tensor:
+    """Sample next token from top K values along the last dimension of the input probs tensor.
+
+    Args:
+        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
+        k (int): The k in “top-k”.
+    Returns:
+        torch.Tensor: Sampled tokens.
+    """
+    top_k_value, _ = torch.topk(probs, k, dim=-1)
+    min_value_top_k = top_k_value[..., [-1]]
+    probs *= (probs >= min_value_top_k).float()
+    probs.div_(probs.sum(dim=-1, keepdim=True))
+    next_token = multinomial(probs, num_samples=1)
+    return next_token
+
+
+def sample_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
+    """Sample next token from top P probabilities along the last dimension of the input probs tensor.
+
+    Args:
+        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
+        p (int): The p in “top-p”.
+    Returns:
+        torch.Tensor: Sampled tokens.
+    """
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort *= (~mask).float()
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token
+
+
+class DummyPoolExecutor:
+    """Dummy pool executor to use when we actually have only 1 worker.
+    (e.g. instead of ProcessPoolExecutor).
+    """
+    class DummyResult:
+        def __init__(self, func, *args, **kwargs):
+            self.func = func
+            self.args = args
+            self.kwargs = kwargs
+
+        def result(self):
+            return self.func(*self.args, **self.kwargs)
+
+    def __init__(self, workers, mp_context=None):
+        pass
+
+    def submit(self, func, *args, **kwargs):
+        return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, exc_tb):
+        return
+
+
+def get_pool_executor(num_workers: int, mp_context=None):
+    return ProcessPoolExecutor(num_workers, mp_context) if num_workers > 1 else DummyPoolExecutor(1)
+
+
+def length_to_mask(lengths: torch.Tensor, max_len: tp.Optional[int] = None) -> torch.Tensor:
+    """Utility function to convert a tensor of sequence lengths to a mask (useful when working on padded sequences).
+    For example: [3, 5] => [[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]]
+
+    Args:
+        lengths (torch.Tensor): tensor with lengths
+        max_len (int): can set the max length manually. Defaults to None.
+    Returns:
+        torch.Tensor: mask with 0s where there is pad tokens else 1s
+    """
+    assert len(lengths.shape) == 1, "Length shape should be 1 dimensional."
+    final_length = lengths.max().item() if not max_len else max_len
+    final_length = max(final_length, 1)  # if all seqs are of len zero we don't want a zero-size tensor
+    return torch.arange(final_length)[None, :].to(lengths.device) < lengths[:, None]
+
+
+def hash_trick(word: str, vocab_size: int) -> int:
+    """Hash trick to pair each word with an index
+
+    Args:
+        word (str): word we wish to convert to an index
+        vocab_size (int): size of the vocabulary
+    Returns:
+        int: index of the word in the embedding LUT
+    """
+    hash = int(hashlib.sha256(word.encode("utf-8")).hexdigest(), 16)
+    return hash % vocab_size
+
+
+def with_rank_rng(base_seed: int = 1234):
+    """Decorator for a function so that the function will use a Random Number Generator
+    whose state depend on the GPU rank. The original RNG state is restored upon returning.
+
+    Args:
+        base_seed (int): Random seed.
+    """
+    def _decorator(fun: tp.Callable):
+        @wraps(fun)
+        def _decorated(*args, **kwargs):
+            state = torch.get_rng_state()
+            seed = base_seed ^ flashy.distrib.rank()
+            torch.manual_seed(seed)
+            logger.debug('Rank dependent seed set to %d', seed)
+            try:
+                return fun(*args, **kwargs)
+            finally:
+                torch.set_rng_state(state)
+                logger.debug('RNG state restored.')
+        return _decorated
+    return _decorator
+
+
+def collate(tensors: tp.List[torch.Tensor], dim: int = 0) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+    """Get a list of tensors and collate them to a single tensor. according to the following logic:
+    - `dim` specifies the time dimension which will be stacked and padded.
+    - The output will contain 1 new dimension (dimension index 0) which will be the size of
+    of the original list.
+
+    Args:
+        tensors (tp.List[torch.Tensor]): List of tensors to collate.
+        dim (int): Dimension which will be stacked and padded.
+    Returns:
+        tp.Tuple[torch.Tensor, torch.Tensor]:
+            torch.Tensor: Stacked and padded tensor. The output will contain 1 new dimension
+                (dimension index 0) which will be the size of the original list.
+            torch.Tensor: Tensor containing length of original tensor sizes (without padding).
+    """
+    tensors = [x.transpose(0, dim) for x in tensors]
+    lens = torch.LongTensor([len(x) for x in tensors])
+    padded_tensors = pad_sequence(tensors)
+    padded_tensors = padded_tensors.transpose(0, 1)
+    padded_tensors = padded_tensors.transpose(1, dim + 1)
+    return padded_tensors, lens