2002 Turkish pop, a style of energetic, danceable songs Take artists like Ajda Pekkan, Tarkan, Sertab Erener, Mustafa Sandal as an example and compose a song

.github/actions/audiocraft_build/action.yml

@@ -21,8 +21,6 @@ runs:
       python3 -m venv env
       .  env/bin/activate
       python -m pip install --upgrade pip
-      pip install torch torchvision torchaudio
-      pip install xformers
       pip install -e '.[dev]'
   - name: System Dependencies
     shell: bash

.github/workflows/audiocraft_docs.yml

@@ -23,9 +23,9 @@ jobs:
       - name: Make docs
         run: |
           . env/bin/activate
-          make api_docs
-          git add -f api_docs
-          git commit -m api_docs
+          make docs
+          git add -f docs
+          git commit -m docs
 
       - name: Push branch
         run: |

.github/workflows/audiocraft_tests.yml

@@ -12,11 +12,6 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - uses: ./.github/actions/audiocraft_build
-      - name: Run unit tests
-        run: |
+      - run: |
           . env/bin/activate
           make tests
-      - name: Run integration tests
-        run: |
-          . env/bin/activate
-          make tests_integ

.gitignore

@@ -35,7 +35,7 @@ wheels/
 .coverage
 
 # docs
-/api_docs
+/docs
 
 # dotenv
 .env
@@ -46,13 +46,6 @@ wheels/
 venv/
 ENV/
 
-# egs with manifest files
-egs/*
-!egs/example
-# local datasets
-dataset/*
-!dataset/example
-
 # personal notebooks & scripts
 */local_scripts
 */notes

CHANGELOG.md

@@ -4,37 +4,7 @@ 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/).
 
-## [1.2.0a] - TBD
-
-Adding stereo models.
-
-
-## [1.1.0] - 2023-11-06
-
-Not using torchaudio anymore when writing audio files, relying instead directly on the commandline ffmpeg. Also not using it anymore for reading audio files, for similar reasons.
-
-Fixed DAC support with non default number of codebooks.
-
-Fixed bug when `two_step_cfg` was overriden when calling `generate()`.
-
-Fixed samples being always prompted with audio, rather than having both prompted and unprompted.
-
-**Backward incompatible change:** A `torch.no_grad` around the computation of the conditioning made its way in the public release.
-	The released models were trained without this. Those impact linear layers applied to the output of the T5 or melody conditioners.
-	We removed it, so you might need to retrain models.
-
-**Backward incompatible change:** Fixing wrong sample rate in CLAP (WARNING if you trained model with CLAP before).
-
-**Backward incompatible change:** Renamed VALLEPattern to CoarseFirstPattern, as it was wrongly named. Probably no one
-	retrained a model with this pattern, so hopefully this won't impact you!
-
-
-## [1.0.0] - 2023-09-07
-
-Major revision, added training code for EnCodec, AudioGen, MusicGen, and MultiBandDiffusion.
-Added pretrained model for AudioGen and MultiBandDiffusion.
-
-## [0.0.2] - 2023-08-01
+## [0.0.2a] - TBD
 
 Improved demo, fixed top p (thanks @jnordberg).
 
@@ -45,9 +15,6 @@ More options when launching Gradio app locally (thanks @ashleykleynhans).
 
 Testing out PyTorch 2.0 memory efficient attention.
 
-Added extended generation (infinite length) by slowly moving the windows.
-Note that other implementations exist: https://github.com/camenduru/MusicGen-colab.
-
 ## [0.0.1] - 2023-06-09
 
 Initial release, with model evaluation only.

CONTRIBUTING.md

@@ -1,11 +1,11 @@
-# Contributing to AudioCraft
+# 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.
+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.
 

LICENSE_weights

@@ -1,399 +1,157 @@
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+
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+
+### Section 4 – Sui Generis Database Rights.
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+
+### Section 5 – Disclaimer of Warranties and Limitation of Liability.
+
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+
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+
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+
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+
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MANIFEST.in

@@ -6,10 +6,3 @@ include *.ini
 include requirements.txt
 include audiocraft/py.typed
 include assets/*.mp3
-include datasets/*.mp3
-recursive-include config *.yaml
-recursive-include demos *.py
-recursive-include demos *.ipynb
-recursive-include scripts *.py
-recursive-include model_cards *.md
-recursive-include docs *.md

model_cards/MUSICGEN_MODEL_CARD.md → MODEL_CARD.md

@@ -12,11 +12,11 @@
 
 **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]
+**Citation details** See [our paper][arxiv]
 
-**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
+**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.
+**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:
@@ -26,7 +26,7 @@
 
 **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.
+**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
 
@@ -54,24 +54,15 @@ The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/data
 
 The model was trained on licensed data 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.
 
-## Evaluation results
+## Quantitative analysis
 
-Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through 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), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper.
-
-| Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity |
-|---|---|---|---|---|
-| facebook/musicgen-small  | 4.88 | 1.42 | 0.27 | - |
-| facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - |
-| facebook/musicgen-large  | 5.48 | 1.37 | 0.28 | - |
-| facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 |
-
-More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Results section.
+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:** Vocals have been removed from the data source using corresponding tags, and then 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).
+**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using 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).
 
 **Limitations:**
 
@@ -87,19 +78,4 @@ More information can be found in the paper [Simple and Controllable Music Genera
 
 **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.
 
-## Update: stereo models and large melody.
-
-We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting
-from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using
-the delay pattern. We also release a mono large model with melody conditioning capabilities. The list of new models
-is as follow:
-
-- facebook/musicgen-stereo-small
-- facebook/musicgen-stereo-medium
-- facebook/musicgen-stereo-large
-- facebook/musicgen-stereo-melody
-- facebook/musicgen-melody-large
-- facebook/musicgen-stereo-melody-large
-
-
 [arxiv]: https://arxiv.org/abs/2306.05284

Makefile

@@ -1,15 +1,3 @@
-INTEG=AUDIOCRAFT_DORA_DIR="/tmp/magma_$(USER)" python3 -m dora -v run --clear device=cpu dataset.num_workers=0 optim.epochs=1 \
-	dataset.train.num_samples=10 dataset.valid.num_samples=10 \
-	dataset.evaluate.num_samples=10 dataset.generate.num_samples=2 sample_rate=16000 \
-	logging.level=DEBUG
-INTEG_COMPRESSION = $(INTEG) solver=compression/debug rvq.n_q=2 rvq.bins=48 checkpoint.save_last=true   # SIG is 5091833e
-INTEG_MUSICGEN = $(INTEG) solver=musicgen/debug dset=audio/example compression_model_checkpoint=//sig/5091833e \
-	transformer_lm.n_q=2 transformer_lm.card=48 transformer_lm.dim=16 checkpoint.save_last=false  # Using compression model from 5091833e
-INTEG_AUDIOGEN = $(INTEG) solver=audiogen/debug dset=audio/example compression_model_checkpoint=//sig/5091833e \
-	transformer_lm.n_q=2 transformer_lm.card=48 transformer_lm.dim=16 checkpoint.save_last=false  # Using compression model from 5091833e
-INTEG_MBD = $(INTEG) solver=diffusion/debug dset=audio/example  \
-	checkpoint.save_last=false  # Using compression model from 616d7b3c
-
 default: linter tests
 
 install:
@@ -22,19 +10,12 @@ linter:
 
 tests:
 	coverage run -m pytest tests
-	coverage report
-
-tests_integ:
-	$(INTEG_COMPRESSION)
-	$(INTEG_MBD)
-	$(INTEG_MUSICGEN)
-	$(INTEG_AUDIOGEN)
-
+	coverage report --include 'audiocraft/*'
 
-api_docs:
-	pdoc3 --html -o api_docs -f audiocraft
+docs:
+	pdoc3 --html -o docs -f audiocraft
 
 dist:
 	python setup.py sdist
 
-.PHONY: linter tests api_docs dist
+.PHONY: linter tests docs dist

README.md

@@ -5,27 +5,42 @@ tags:
   - "music generation"
   - "language models"
   - "LLMs"
-app_file: "demos/musicgen_app.py"
+app_file: "app_batched.py"
 emoji: 🎵
-colorFrom: gray
+colorFrom: white
 colorTo: blue
 sdk: gradio
 sdk_version: 3.34.0
 pinned: true
 license: "cc-by-nc-4.0"
-disable_embedding: true
 ---
-# AudioCraft
+# 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. AudioCraft contains inference and training code
-for two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.
+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 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/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?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>
+
+We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.
 
 ## Installation
-AudioCraft requires Python 3.9, PyTorch 2.0.0. To install AudioCraft, you can run the following:
+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.
@@ -34,68 +49,87 @@ 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 (mandatory if you want to train).
+pip install -e .  # or if you cloned the repo locally
 ```
 
-We also recommend having `ffmpeg` installed, either through your system or Anaconda:
-```bash
-sudo apt-get install ffmpeg
-# Or if you are using Anaconda or Miniconda
-conda install "ffmpeg<5" -c conda-forge
-```
+## Usage
+We offer a number of way to interact with MusicGen:
+1. 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).
+2. You can use the gradio demo locally by running `python app.py`.
+3. A demo is also available on the [`facebook/MusicGen`  HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
+4. Finally, you can run the [Gradio demo with a Colab GPU](https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing),
+as adapted from [@camenduru Colab](https://github.com/camenduru/MusicGen-colab).
 
-## Models
+## API
 
-At the moment, AudioCraft contains the training code and inference code for:
-* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model.
-* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model.
-* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec.
-* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion.
+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)
 
-## Training code
+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.
 
-AudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models.
-For a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to
-the [AudioCraft training documentation](./docs/TRAINING.md).
+**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
+```
 
-For reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model
-that provides pointers to configuration, example grids and model/task-specific information and FAQ.
+See after a quick example for using the API.
 
+```python
+import torchaudio
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
 
-## API documentation
+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.
 
-We provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft.
+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", loudness_compressor=True)
+```
 
-## FAQ
 
-#### Is the training code available?
+## Model Card
 
-Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md) and [Multi Band Diffusion](./docs/MBD.md).
+See [the model card page](./MODEL_CARD.md).
 
-#### Where are the models stored?
+## FAQ
 
-Hugging Face stored the model in a specific location, which can be overriden by setting the `AUDIOCRAFT_CACHE_DIR` environment variable for the AudioCraft models.
-In order to change the cache location of the other Hugging Face models, please check out the [Hugging Face Transformers documentation for the cache setup](https://huggingface.co/docs/transformers/installation#cache-setup).
-Finally, if you use a model that relies on Demucs (e.g. `musicgen-melody`) and want to change the download location for Demucs, refer to the [Torch Hub documentation](https://pytorch.org/docs/stable/hub.html#where-are-my-downloaded-models-saved).
+#### Will the training code be released?
 
+Yes. We will soon release the training code for MusicGen and EnCodec.
 
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The models weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
 
+#### I need help on Windows
+
+@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
 
-## Citation
 
-For the general framework of AudioCraft, please cite the following.
+## 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},
+      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},
 }
 ```
 
-When referring to a specific model, please cite as mentioned in the model specific README, e.g
-[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc.
+## 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 +1,216 @@
+"""
+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 argparse
+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.get('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",
+            loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
+        waveform_video = gr.make_waveform(file.name)
+    return waveform_video
+
+
+def ui(**kwargs):
+    with gr.Blocks() as interface:
+        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")
+                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.
+            """
+        )
+
+        # Show the interface
+        launch_kwargs = {}
+        username = kwargs.get('username')
+        password = kwargs.get('password')
+        server_port = kwargs.get('server_port', 0)
+        inbrowser = kwargs.get('inbrowser', False)
+        share = kwargs.get('share', False)
+        server_name = kwargs.get('listen')
+
+        launch_kwargs['server_name'] = server_name
+
+        if username and password:
+            launch_kwargs['auth'] = (username, password)
+        if server_port > 0:
+            launch_kwargs['server_port'] = server_port
+        if inbrowser:
+            launch_kwargs['inbrowser'] = inbrowser
+        if share:
+            launch_kwargs['share'] = share
+
+        interface.queue().launch(**launch_kwargs, max_threads=1)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--listen',
+        type=str,
+        default='0.0.0.0',
+        help='IP to listen on for connections to Gradio',
+    )
+    parser.add_argument(
+        '--username', type=str, default='', help='Username for authentication'
+    )
+    parser.add_argument(
+        '--password', type=str, default='', help='Password for authentication'
+    )
+    parser.add_argument(
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
+    )
+    parser.add_argument(
+        '--share', action='store_true', help='Share the gradio UI'
+    )
+
+    args = parser.parse_args()
+
+    ui(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        share=args.share,
+        listen=args.listen
+    )

app_batched.py

@@ -0,0 +1,222 @@
+"""
+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
+from concurrent.futures import ProcessPoolExecutor
+import subprocess as sp
+from tempfile import NamedTemporaryFile
+import time
+import warnings
+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
+
+_old_call = sp.call
+
+
+def _call_nostderr(*args, **kwargs):
+    # Avoid ffmpeg vomitting on the logs.
+    kwargs['stderr'] = sp.DEVNULL
+    kwargs['stdout'] = sp.DEVNULL
+    _old_call(*args, **kwargs)
+
+
+sp.call = _call_nostderr
+pool = ProcessPoolExecutor(3)
+pool.__enter__()
+
+
+def make_waveform(*args, **kwargs):
+    be = time.time()
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore')
+        out = gr.make_waveform(*args, **kwargs)
+        print("Make a video took", time.time() - be)
+        return out
+
+
+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
+    max_text_length = 512
+    texts = [text[:max_text_length] for text in texts]
+    MODEL.set_generation_params(duration=duration)
+
+    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
+    be = time.time()
+    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",
+                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
+            out_files.append(pool.submit(make_waveform, file.name))
+    res = [[out_file.result() for out_file in out_files]]
+    print("batch finished", len(texts), time.time() - be)
+    return res
+
+
+def ui(**kwargs):
+    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=8)
+        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.
+        """)
+
+        # Show the interface
+        launch_kwargs = {}
+        username = kwargs.get('username')
+        password = kwargs.get('password')
+        server_port = kwargs.get('server_port', 0)
+        inbrowser = kwargs.get('inbrowser', False)
+        share = kwargs.get('share', False)
+        server_name = kwargs.get('listen')
+
+        launch_kwargs['server_name'] = server_name
+
+        if username and password:
+            launch_kwargs['auth'] = (username, password)
+        if server_port > 0:
+            launch_kwargs['server_port'] = server_port
+        if inbrowser:
+            launch_kwargs['inbrowser'] = inbrowser
+        if share:
+            launch_kwargs['share'] = share
+        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--listen',
+        type=str,
+        default='0.0.0.0',
+        help='IP to listen on for connections to Gradio',
+    )
+    parser.add_argument(
+        '--username', type=str, default='', help='Username for authentication'
+    )
+    parser.add_argument(
+        '--password', type=str, default='', help='Password for authentication'
+    )
+    parser.add_argument(
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
+    )
+    parser.add_argument(
+        '--share', action='store_true', help='Share the gradio UI'
+    )
+
+    args = parser.parse_args()
+
+    ui(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        share=args.share,
+        listen=args.listen
+    )

audiocraft/__init__.py

@@ -3,24 +3,8 @@
 #
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
-"""
-AudioCraft is a general framework for training audio generative models.
-At the moment we provide the training code for:
-
-- [MusicGen](https://arxiv.org/abs/2306.05284), a state-of-the-art
-    text-to-music and melody+text autoregressive generative model.
-    For the solver, see `audiocraft.solvers.musicgen.MusicGenSolver`, and for the model,
-    `audiocraft.models.musicgen.MusicGen`.
-- [AudioGen](https://arxiv.org/abs/2209.15352), a state-of-the-art
-    text-to-general-audio generative model.
-- [EnCodec](https://arxiv.org/abs/2210.13438), efficient and high fidelity
-    neural audio codec which provides an excellent tokenizer for autoregressive language models.
-    See `audiocraft.solvers.compression.CompressionSolver`, and `audiocraft.models.encodec.EncodecModel`.
-- [MultiBandDiffusion](TODO), alternative diffusion-based decoder compatible with EnCodec that
-    improves the perceived quality and reduces the artifacts coming from adversarial decoders.
-"""
 
 # flake8: noqa
 from . import data, modules, models
 
-__version__ = '1.1.0'
+__version__ = '0.0.2a1'

audiocraft/adversarial/__init__.py

@@ -1,22 +0,0 @@
-# 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.
-"""Adversarial losses and discriminator architectures."""
-
-# flake8: noqa
-from .discriminators import (
-    MultiPeriodDiscriminator,
-    MultiScaleDiscriminator,
-    MultiScaleSTFTDiscriminator
-)
-from .losses import (
-    AdversarialLoss,
-    AdvLossType,
-    get_adv_criterion,
-    get_fake_criterion,
-    get_real_criterion,
-    FeatLossType,
-    FeatureMatchingLoss
-)

audiocraft/adversarial/discriminators/__init__.py

@@ -1,10 +0,0 @@
-# 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 .mpd import MultiPeriodDiscriminator
-from .msd import MultiScaleDiscriminator
-from .msstftd import MultiScaleSTFTDiscriminator

audiocraft/adversarial/discriminators/base.py

@@ -1,34 +0,0 @@
-# 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
-
-import torch
-import torch.nn as nn
-
-
-FeatureMapType = tp.List[torch.Tensor]
-LogitsType = torch.Tensor
-MultiDiscriminatorOutputType = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]]
-
-
-class MultiDiscriminator(ABC, nn.Module):
-    """Base implementation for discriminators composed of sub-discriminators acting at different scales.
-    """
-    def __init__(self):
-        super().__init__()
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
-        ...
-
-    @property
-    @abstractmethod
-    def num_discriminators(self) -> int:
-        """Number of discriminators.
-        """
-        ...

audiocraft/adversarial/discriminators/mpd.py

@@ -1,106 +0,0 @@
-# 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 torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ...modules import NormConv2d
-from .base import MultiDiscriminator, MultiDiscriminatorOutputType
-
-
-def get_padding(kernel_size: int, dilation: int = 1) -> int:
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-class PeriodDiscriminator(nn.Module):
-    """Period sub-discriminator.
-
-    Args:
-        period (int): Period between samples of audio.
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        n_layers (int): Number of convolutional layers.
-        kernel_sizes (list of int): Kernel sizes for convolutions.
-        stride (int): Stride for convolutions.
-        filters (int): Initial number of filters in convolutions.
-        filters_scale (int): Multiplier of number of filters as we increase depth.
-        max_filters (int): Maximum number of filters.
-        norm (str): Normalization method.
-        activation (str): Activation function.
-        activation_params (dict): Parameters to provide to the activation function.
-    """
-    def __init__(self, period: int, in_channels: int = 1, out_channels: int = 1,
-                 n_layers: int = 5, kernel_sizes: tp.List[int] = [5, 3], stride: int = 3,
-                 filters: int = 8, filters_scale: int = 4, max_filters: int = 1024,
-                 norm: str = 'weight_norm', activation: str = 'LeakyReLU',
-                 activation_params: dict = {'negative_slope': 0.2}):
-        super().__init__()
-        self.period = period
-        self.n_layers = n_layers
-        self.activation = getattr(torch.nn, activation)(**activation_params)
-        self.convs = nn.ModuleList()
-        in_chs = in_channels
-        for i in range(self.n_layers):
-            out_chs = min(filters * (filters_scale ** (i + 1)), max_filters)
-            eff_stride = 1 if i == self.n_layers - 1 else stride
-            self.convs.append(NormConv2d(in_chs, out_chs, kernel_size=(kernel_sizes[0], 1), stride=(eff_stride, 1),
-                                         padding=((kernel_sizes[0] - 1) // 2, 0), norm=norm))
-            in_chs = out_chs
-        self.conv_post = NormConv2d(in_chs, out_channels, kernel_size=(kernel_sizes[1], 1), stride=1,
-                                    padding=((kernel_sizes[1] - 1) // 2, 0), norm=norm)
-
-    def forward(self, x: torch.Tensor):
-        fmap = []
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), 'reflect')
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for conv in self.convs:
-            x = conv(x)
-            x = self.activation(x)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        # x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(MultiDiscriminator):
-    """Multi-Period (MPD) Discriminator.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        periods (Sequence[int]): Periods between samples of audio for the sub-discriminators.
-        **kwargs: Additional args for `PeriodDiscriminator`
-    """
-    def __init__(self, in_channels: int = 1, out_channels: int = 1,
-                 periods: tp.Sequence[int] = [2, 3, 5, 7, 11], **kwargs):
-        super().__init__()
-        self.discriminators = nn.ModuleList([
-            PeriodDiscriminator(p, in_channels, out_channels, **kwargs) for p in periods
-        ])
-
-    @property
-    def num_discriminators(self):
-        return len(self.discriminators)
-
-    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
-        logits = []
-        fmaps = []
-        for disc in self.discriminators:
-            logit, fmap = disc(x)
-            logits.append(logit)
-            fmaps.append(fmap)
-        return logits, fmaps

audiocraft/adversarial/discriminators/msd.py

@@ -1,126 +0,0 @@
-# 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
-import torch.nn as nn
-
-from ...modules import NormConv1d
-from .base import MultiDiscriminator, MultiDiscriminatorOutputType
-
-
-class ScaleDiscriminator(nn.Module):
-    """Waveform sub-discriminator.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        kernel_sizes (Sequence[int]): Kernel sizes for first and last convolutions.
-        filters (int): Number of initial filters for convolutions.
-        max_filters (int): Maximum number of filters.
-        downsample_scales (Sequence[int]): Scale for downsampling implemented as strided convolutions.
-        inner_kernel_sizes (Sequence[int] or None): Kernel sizes for inner convolutions.
-        groups (Sequence[int] or None): Groups for inner convolutions.
-        strides (Sequence[int] or None): Strides for inner convolutions.
-        paddings (Sequence[int] or None): Paddings for inner convolutions.
-        norm (str): Normalization method.
-        activation (str): Activation function.
-        activation_params (dict): Parameters to provide to the activation function.
-        pad (str): Padding for initial convolution.
-        pad_params (dict): Parameters to provide to the padding module.
-    """
-    def __init__(self, in_channels=1, out_channels=1, kernel_sizes: tp.Sequence[int] = [5, 3],
-                 filters: int = 16, max_filters: int = 1024, downsample_scales: tp.Sequence[int] = [4, 4, 4, 4],
-                 inner_kernel_sizes: tp.Optional[tp.Sequence[int]] = None, groups: tp.Optional[tp.Sequence[int]] = None,
-                 strides: tp.Optional[tp.Sequence[int]] = None, paddings: tp.Optional[tp.Sequence[int]] = None,
-                 norm: str = 'weight_norm', activation: str = 'LeakyReLU',
-                 activation_params: dict = {'negative_slope': 0.2}, pad: str = 'ReflectionPad1d',
-                 pad_params: dict = {}):
-        super().__init__()
-        assert len(kernel_sizes) == 2
-        assert kernel_sizes[0] % 2 == 1
-        assert kernel_sizes[1] % 2 == 1
-        assert (inner_kernel_sizes is None or len(inner_kernel_sizes) == len(downsample_scales))
-        assert (groups is None or len(groups) == len(downsample_scales))
-        assert (strides is None or len(strides) == len(downsample_scales))
-        assert (paddings is None or len(paddings) == len(downsample_scales))
-        self.activation = getattr(torch.nn, activation)(**activation_params)
-        self.convs = nn.ModuleList()
-        self.convs.append(
-            nn.Sequential(
-                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
-                NormConv1d(in_channels, filters, kernel_size=np.prod(kernel_sizes), stride=1, norm=norm)
-            )
-        )
-
-        in_chs = filters
-        for i, downsample_scale in enumerate(downsample_scales):
-            out_chs = min(in_chs * downsample_scale, max_filters)
-            default_kernel_size = downsample_scale * 10 + 1
-            default_stride = downsample_scale
-            default_padding = (default_kernel_size - 1) // 2
-            default_groups = in_chs // 4
-            self.convs.append(
-                NormConv1d(in_chs, out_chs,
-                           kernel_size=inner_kernel_sizes[i] if inner_kernel_sizes else default_kernel_size,
-                           stride=strides[i] if strides else default_stride,
-                           groups=groups[i] if groups else default_groups,
-                           padding=paddings[i] if paddings else default_padding,
-                           norm=norm))
-            in_chs = out_chs
-
-        out_chs = min(in_chs * 2, max_filters)
-        self.convs.append(NormConv1d(in_chs, out_chs, kernel_size=kernel_sizes[0], stride=1,
-                                     padding=(kernel_sizes[0] - 1) // 2, norm=norm))
-        self.conv_post = NormConv1d(out_chs, out_channels, kernel_size=kernel_sizes[1], stride=1,
-                                    padding=(kernel_sizes[1] - 1) // 2, norm=norm)
-
-    def forward(self, x: torch.Tensor):
-        fmap = []
-        for layer in self.convs:
-            x = layer(x)
-            x = self.activation(x)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        # x = torch.flatten(x, 1, -1)
-        return x, fmap
-
-
-class MultiScaleDiscriminator(MultiDiscriminator):
-    """Multi-Scale (MSD) Discriminator,
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        downsample_factor (int): Downsampling factor between the different scales.
-        scale_norms (Sequence[str]): Normalization for each sub-discriminator.
-        **kwargs: Additional args for ScaleDiscriminator.
-    """
-    def __init__(self, in_channels: int = 1, out_channels: int = 1, downsample_factor: int = 2,
-                 scale_norms: tp.Sequence[str] = ['weight_norm', 'weight_norm', 'weight_norm'], **kwargs):
-        super().__init__()
-        self.discriminators = nn.ModuleList([
-            ScaleDiscriminator(in_channels, out_channels, norm=norm, **kwargs) for norm in scale_norms
-        ])
-        self.downsample = nn.AvgPool1d(downsample_factor * 2, downsample_factor, padding=downsample_factor)
-
-    @property
-    def num_discriminators(self):
-        return len(self.discriminators)
-
-    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
-        logits = []
-        fmaps = []
-        for i, disc in enumerate(self.discriminators):
-            if i != 0:
-                self.downsample(x)
-            logit, fmap = disc(x)
-            logits.append(logit)
-            fmaps.append(fmap)
-        return logits, fmaps

audiocraft/adversarial/discriminators/msstftd.py

@@ -1,134 +0,0 @@
-# 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 torchaudio
-import torch
-from torch import nn
-from einops import rearrange
-
-from ...modules import NormConv2d
-from .base import MultiDiscriminator, MultiDiscriminatorOutputType
-
-
-def get_2d_padding(kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1)):
-    return (((kernel_size[0] - 1) * dilation[0]) // 2, ((kernel_size[1] - 1) * dilation[1]) // 2)
-
-
-class DiscriminatorSTFT(nn.Module):
-    """STFT sub-discriminator.
-
-    Args:
-        filters (int): Number of filters in convolutions.
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        n_fft (int): Size of FFT for each scale.
-        hop_length (int): Length of hop between STFT windows for each scale.
-        kernel_size (tuple of int): Inner Conv2d kernel sizes.
-        stride (tuple of int): Inner Conv2d strides.
-        dilations (list of int): Inner Conv2d dilation on the time dimension.
-        win_length (int): Window size for each scale.
-        normalized (bool): Whether to normalize by magnitude after stft.
-        norm (str): Normalization method.
-        activation (str): Activation function.
-        activation_params (dict): Parameters to provide to the activation function.
-        growth (int): Growth factor for the filters.
-    """
-    def __init__(self, filters: int, in_channels: int = 1, out_channels: int = 1,
-                 n_fft: int = 1024, hop_length: int = 256, win_length: int = 1024, max_filters: int = 1024,
-                 filters_scale: int = 1, kernel_size: tp.Tuple[int, int] = (3, 9), dilations: tp.List = [1, 2, 4],
-                 stride: tp.Tuple[int, int] = (1, 2), normalized: bool = True, norm: str = 'weight_norm',
-                 activation: str = 'LeakyReLU', activation_params: dict = {'negative_slope': 0.2}):
-        super().__init__()
-        assert len(kernel_size) == 2
-        assert len(stride) == 2
-        self.filters = filters
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.n_fft = n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.normalized = normalized
-        self.activation = getattr(torch.nn, activation)(**activation_params)
-        self.spec_transform = torchaudio.transforms.Spectrogram(
-            n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window_fn=torch.hann_window,
-            normalized=self.normalized, center=False, pad_mode=None, power=None)
-        spec_channels = 2 * self.in_channels
-        self.convs = nn.ModuleList()
-        self.convs.append(
-            NormConv2d(spec_channels, self.filters, kernel_size=kernel_size, padding=get_2d_padding(kernel_size))
-        )
-        in_chs = min(filters_scale * self.filters, max_filters)
-        for i, dilation in enumerate(dilations):
-            out_chs = min((filters_scale ** (i + 1)) * self.filters, max_filters)
-            self.convs.append(NormConv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride,
-                                         dilation=(dilation, 1), padding=get_2d_padding(kernel_size, (dilation, 1)),
-                                         norm=norm))
-            in_chs = out_chs
-        out_chs = min((filters_scale ** (len(dilations) + 1)) * self.filters, max_filters)
-        self.convs.append(NormConv2d(in_chs, out_chs, kernel_size=(kernel_size[0], kernel_size[0]),
-                                     padding=get_2d_padding((kernel_size[0], kernel_size[0])),
-                                     norm=norm))
-        self.conv_post = NormConv2d(out_chs, self.out_channels,
-                                    kernel_size=(kernel_size[0], kernel_size[0]),
-                                    padding=get_2d_padding((kernel_size[0], kernel_size[0])),
-                                    norm=norm)
-
-    def forward(self, x: torch.Tensor):
-        fmap = []
-        z = self.spec_transform(x)  # [B, 2, Freq, Frames, 2]
-        z = torch.cat([z.real, z.imag], dim=1)
-        z = rearrange(z, 'b c w t -> b c t w')
-        for i, layer in enumerate(self.convs):
-            z = layer(z)
-            z = self.activation(z)
-            fmap.append(z)
-        z = self.conv_post(z)
-        return z, fmap
-
-
-class MultiScaleSTFTDiscriminator(MultiDiscriminator):
-    """Multi-Scale STFT (MS-STFT) discriminator.
-
-    Args:
-        filters (int): Number of filters in convolutions.
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        sep_channels (bool): Separate channels to distinct samples for stereo support.
-        n_ffts (Sequence[int]): Size of FFT for each scale.
-        hop_lengths (Sequence[int]): Length of hop between STFT windows for each scale.
-        win_lengths (Sequence[int]): Window size for each scale.
-        **kwargs: Additional args for STFTDiscriminator.
-    """
-    def __init__(self, filters: int, in_channels: int = 1, out_channels: int = 1, sep_channels: bool = False,
-                 n_ffts: tp.List[int] = [1024, 2048, 512], hop_lengths: tp.List[int] = [256, 512, 128],
-                 win_lengths: tp.List[int] = [1024, 2048, 512], **kwargs):
-        super().__init__()
-        assert len(n_ffts) == len(hop_lengths) == len(win_lengths)
-        self.sep_channels = sep_channels
-        self.discriminators = nn.ModuleList([
-            DiscriminatorSTFT(filters, in_channels=in_channels, out_channels=out_channels,
-                              n_fft=n_ffts[i], win_length=win_lengths[i], hop_length=hop_lengths[i], **kwargs)
-            for i in range(len(n_ffts))
-        ])
-
-    @property
-    def num_discriminators(self):
-        return len(self.discriminators)
-
-    def _separate_channels(self, x: torch.Tensor) -> torch.Tensor:
-        B, C, T = x.shape
-        return x.view(-1, 1, T)
-
-    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
-        logits = []
-        fmaps = []
-        for disc in self.discriminators:
-            logit, fmap = disc(x)
-            logits.append(logit)
-            fmaps.append(fmap)
-        return logits, fmaps

audiocraft/adversarial/losses.py

@@ -1,228 +0,0 @@
-# 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 module to handle adversarial losses without requiring to mess up the main training loop.
-"""
-
-import typing as tp
-
-import flashy
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-ADVERSARIAL_LOSSES = ['mse', 'hinge', 'hinge2']
-
-
-AdvLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor], torch.Tensor]]
-FeatLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor, torch.Tensor], torch.Tensor]]
-
-
-class AdversarialLoss(nn.Module):
-    """Adversary training wrapper.
-
-    Args:
-        adversary (nn.Module): The adversary module will be used to estimate the logits given the fake and real samples.
-            We assume here the adversary output is ``Tuple[List[torch.Tensor], List[List[torch.Tensor]]]``
-            where the first item is a list of logits and the second item is a list of feature maps.
-        optimizer (torch.optim.Optimizer): Optimizer used for training the given module.
-        loss (AdvLossType): Loss function for generator training.
-        loss_real (AdvLossType): Loss function for adversarial training on logits from real samples.
-        loss_fake (AdvLossType): Loss function for adversarial training on logits from fake samples.
-        loss_feat (FeatLossType): Feature matching loss function for generator training.
-        normalize (bool): Whether to normalize by number of sub-discriminators.
-
-    Example of usage:
-        adv_loss = AdversarialLoss(adversaries, optimizer, loss, loss_real, loss_fake)
-        for real in loader:
-            noise = torch.randn(...)
-            fake = model(noise)
-            adv_loss.train_adv(fake, real)
-            loss, _ = adv_loss(fake, real)
-            loss.backward()
-    """
-    def __init__(self,
-                 adversary: nn.Module,
-                 optimizer: torch.optim.Optimizer,
-                 loss: AdvLossType,
-                 loss_real: AdvLossType,
-                 loss_fake: AdvLossType,
-                 loss_feat: tp.Optional[FeatLossType] = None,
-                 normalize: bool = True):
-        super().__init__()
-        self.adversary: nn.Module = adversary
-        flashy.distrib.broadcast_model(self.adversary)
-        self.optimizer = optimizer
-        self.loss = loss
-        self.loss_real = loss_real
-        self.loss_fake = loss_fake
-        self.loss_feat = loss_feat
-        self.normalize = normalize
-
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        # Add the optimizer state dict inside our own.
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'optimizer'] = self.optimizer.state_dict()
-        return destination
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        # Load optimizer state.
-        self.optimizer.load_state_dict(state_dict.pop(prefix + 'optimizer'))
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def get_adversary_pred(self, x):
-        """Run adversary model, validating expected output format."""
-        logits, fmaps = self.adversary(x)
-        assert isinstance(logits, list) and all([isinstance(t, torch.Tensor) for t in logits]), \
-            f'Expecting a list of tensors as logits but {type(logits)} found.'
-        assert isinstance(fmaps, list), f'Expecting a list of features maps but {type(fmaps)} found.'
-        for fmap in fmaps:
-            assert isinstance(fmap, list) and all([isinstance(f, torch.Tensor) for f in fmap]), \
-                f'Expecting a list of tensors as feature maps but {type(fmap)} found.'
-        return logits, fmaps
-
-    def train_adv(self, fake: torch.Tensor, real: torch.Tensor) -> torch.Tensor:
-        """Train the adversary with the given fake and real example.
-
-        We assume the adversary output is the following format: Tuple[List[torch.Tensor], List[List[torch.Tensor]]].
-        The first item being the logits and second item being a list of feature maps for each sub-discriminator.
-
-        This will automatically synchronize gradients (with `flashy.distrib.eager_sync_model`)
-        and call the optimizer.
-        """
-        loss = torch.tensor(0., device=fake.device)
-        all_logits_fake_is_fake, _ = self.get_adversary_pred(fake.detach())
-        all_logits_real_is_fake, _ = self.get_adversary_pred(real.detach())
-        n_sub_adversaries = len(all_logits_fake_is_fake)
-        for logit_fake_is_fake, logit_real_is_fake in zip(all_logits_fake_is_fake, all_logits_real_is_fake):
-            loss += self.loss_fake(logit_fake_is_fake) + self.loss_real(logit_real_is_fake)
-
-        if self.normalize:
-            loss /= n_sub_adversaries
-
-        self.optimizer.zero_grad()
-        with flashy.distrib.eager_sync_model(self.adversary):
-            loss.backward()
-        self.optimizer.step()
-
-        return loss
-
-    def forward(self, fake: torch.Tensor, real: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-        """Return the loss for the generator, i.e. trying to fool the adversary,
-        and feature matching loss if provided.
-        """
-        adv = torch.tensor(0., device=fake.device)
-        feat = torch.tensor(0., device=fake.device)
-        with flashy.utils.readonly(self.adversary):
-            all_logits_fake_is_fake, all_fmap_fake = self.get_adversary_pred(fake)
-            all_logits_real_is_fake, all_fmap_real = self.get_adversary_pred(real)
-            n_sub_adversaries = len(all_logits_fake_is_fake)
-            for logit_fake_is_fake in all_logits_fake_is_fake:
-                adv += self.loss(logit_fake_is_fake)
-            if self.loss_feat:
-                for fmap_fake, fmap_real in zip(all_fmap_fake, all_fmap_real):
-                    feat += self.loss_feat(fmap_fake, fmap_real)
-
-        if self.normalize:
-            adv /= n_sub_adversaries
-            feat /= n_sub_adversaries
-
-        return adv, feat
-
-
-def get_adv_criterion(loss_type: str) -> tp.Callable:
-    assert loss_type in ADVERSARIAL_LOSSES
-    if loss_type == 'mse':
-        return mse_loss
-    elif loss_type == 'hinge':
-        return hinge_loss
-    elif loss_type == 'hinge2':
-        return hinge2_loss
-    raise ValueError('Unsupported loss')
-
-
-def get_fake_criterion(loss_type: str) -> tp.Callable:
-    assert loss_type in ADVERSARIAL_LOSSES
-    if loss_type == 'mse':
-        return mse_fake_loss
-    elif loss_type in ['hinge', 'hinge2']:
-        return hinge_fake_loss
-    raise ValueError('Unsupported loss')
-
-
-def get_real_criterion(loss_type: str) -> tp.Callable:
-    assert loss_type in ADVERSARIAL_LOSSES
-    if loss_type == 'mse':
-        return mse_real_loss
-    elif loss_type in ['hinge', 'hinge2']:
-        return hinge_real_loss
-    raise ValueError('Unsupported loss')
-
-
-def mse_real_loss(x: torch.Tensor) -> torch.Tensor:
-    return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x))
-
-
-def mse_fake_loss(x: torch.Tensor) -> torch.Tensor:
-    return F.mse_loss(x, torch.tensor(0., device=x.device).expand_as(x))
-
-
-def hinge_real_loss(x: torch.Tensor) -> torch.Tensor:
-    return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-def hinge_fake_loss(x: torch.Tensor) -> torch.Tensor:
-    return -torch.mean(torch.min(-x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-def mse_loss(x: torch.Tensor) -> torch.Tensor:
-    if x.numel() == 0:
-        return torch.tensor([0.0], device=x.device)
-    return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x))
-
-
-def hinge_loss(x: torch.Tensor) -> torch.Tensor:
-    if x.numel() == 0:
-        return torch.tensor([0.0], device=x.device)
-    return -x.mean()
-
-
-def hinge2_loss(x: torch.Tensor) -> torch.Tensor:
-    if x.numel() == 0:
-        return torch.tensor([0.0])
-    return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-class FeatureMatchingLoss(nn.Module):
-    """Feature matching loss for adversarial training.
-
-    Args:
-        loss (nn.Module): Loss to use for feature matching (default=torch.nn.L1).
-        normalize (bool): Whether to normalize the loss.
-            by number of feature maps.
-    """
-    def __init__(self, loss: nn.Module = torch.nn.L1Loss(), normalize: bool = True):
-        super().__init__()
-        self.loss = loss
-        self.normalize = normalize
-
-    def forward(self, fmap_fake: tp.List[torch.Tensor], fmap_real: tp.List[torch.Tensor]) -> torch.Tensor:
-        assert len(fmap_fake) == len(fmap_real) and len(fmap_fake) > 0
-        feat_loss = torch.tensor(0., device=fmap_fake[0].device)
-        feat_scale = torch.tensor(0., device=fmap_fake[0].device)
-        n_fmaps = 0
-        for (feat_fake, feat_real) in zip(fmap_fake, fmap_real):
-            assert feat_fake.shape == feat_real.shape
-            n_fmaps += 1
-            feat_loss += self.loss(feat_fake, feat_real)
-            feat_scale += torch.mean(torch.abs(feat_real))
-
-        if self.normalize:
-            feat_loss /= n_fmaps
-
-        return feat_loss

audiocraft/data/__init__.py

@@ -3,8 +3,6 @@
 #
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
-"""Audio loading and writing support. Datasets for raw audio
-or also including some metadata."""
 
 # flake8: noqa
-from . import audio, audio_dataset, info_audio_dataset, music_dataset, sound_dataset
+from . import audio, audio_dataset

audiocraft/data/audio.py

@@ -18,11 +18,11 @@ import numpy as np
 import soundfile
 import torch
 from torch.nn import functional as F
+import torchaudio as ta
 
 import av
-import subprocess as sp
 
-from .audio_utils import f32_pcm, normalize_audio
+from .audio_utils import f32_pcm, i16_pcm, normalize_audio
 
 
 _av_initialized = False
@@ -78,7 +78,7 @@ def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: floa
         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 of torch.Tensor, int: Tuple containing audio data and sample rate
+        Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate
     """
     _init_av()
     with av.open(str(filepath)) as af:
@@ -123,7 +123,7 @@ def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
         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 of torch.Tensor, int: Tuple containing audio data and sample rate.
+        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
@@ -136,6 +136,12 @@ def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
         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:
@@ -144,22 +150,10 @@ def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
     return wav, sr
 
 
-def _piping_to_ffmpeg(out_path: tp.Union[str, Path], wav: torch.Tensor, sample_rate: int, flags: tp.List[str]):
-    # ffmpeg is always installed and torchaudio is a bit unstable lately, so let's bypass it entirely.
-    assert wav.dim() == 2, wav.shape
-    command = [
-        'ffmpeg',
-        '-loglevel', 'error',
-        '-y', '-f', 'f32le', '-ar', str(sample_rate), '-ac', str(wav.shape[0]),
-        '-i', '-'] + flags + [str(out_path)]
-    input_ = f32_pcm(wav).t().detach().cpu().numpy().tobytes()
-    sp.run(command, input=input_, check=True)
-
-
 def audio_write(stem_name: tp.Union[str, Path],
                 wav: torch.Tensor, sample_rate: int,
-                format: str = 'wav', mp3_rate: int = 320, ogg_rate: tp.Optional[int] = None,
-                normalize: bool = True, strategy: str = 'peak', peak_clip_headroom_db: float = 1,
+                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,
                 loudness_compressor: bool = False,
                 log_clipping: bool = True, make_parent_dir: bool = True,
@@ -168,11 +162,8 @@ def audio_write(stem_name: tp.Union[str, Path],
 
     Args:
         stem_name (str or Path): Filename without extension which will be added automatically.
-        wav (torch.Tensor): Audio data to save.
-        sample_rate (int): Sample rate of audio data.
-        format (str): Either "wav", "mp3", "ogg", or "flac".
+        format (str): Either "wav" or "mp3".
         mp3_rate (int): kbps when using mp3s.
-        ogg_rate (int): kbps when using ogg/vorbis. If not provided, let ffmpeg decide for itself.
         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.
@@ -184,7 +175,7 @@ def audio_write(stem_name: tp.Union[str, Path],
             than the `peak_clip` one to avoid further clipping.
         loudness_headroom_db (float): Target loudness for loudness normalization.
         loudness_compressor (bool): Uses tanh for soft clipping when strategy is 'loudness'.
-         when strategy is 'loudness' log_clipping (bool): If True, basic logging on stderr when clipping still
+         when strategy is 'loudness'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:
@@ -197,23 +188,16 @@ def audio_write(stem_name: tp.Union[str, Path],
         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, loudness_compressor,
-                          log_clipping=log_clipping, sample_rate=sample_rate,
-                          stem_name=str(stem_name))
+                          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'
-        flags = ['-f', 'mp3', '-c:a', 'libmp3lame', '-b:a', f'{mp3_rate}k']
+        kwargs.update({"compression": mp3_rate})
     elif format == 'wav':
+        wav = i16_pcm(wav)
         suffix = '.wav'
-        flags = ['-f', 'wav', '-c:a', 'pcm_s16le']
-    elif format == 'ogg':
-        suffix = '.ogg'
-        flags = ['-f', 'ogg', '-c:a', 'libvorbis']
-        if ogg_rate is not None:
-            flags += ['-b:a', f'{ogg_rate}k']
-    elif format == 'flac':
-        suffix = '.flac'
-        flags = ['-f', 'flac']
+        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:
@@ -222,7 +206,7 @@ def audio_write(stem_name: tp.Union[str, Path],
     if make_parent_dir:
         path.parent.mkdir(exist_ok=True, parents=True)
     try:
-        _piping_to_ffmpeg(path, wav, sample_rate, flags)
+        ta.save(path, wav, sample_rate, **kwargs)
     except Exception:
         if path.exists():
             # we do not want to leave half written files around.

audiocraft/data/audio_dataset.py

@@ -3,16 +3,12 @@
 #
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
-"""AudioDataset support. In order to handle a larger number of files
-without having to scan again the folders, we precompute some metadata
-(filename, sample rate, duration), and use that to efficiently sample audio segments.
-"""
+
 import argparse
 import copy
 from concurrent.futures import ThreadPoolExecutor, Future
 from dataclasses import dataclass, fields
 from contextlib import ExitStack
-from functools import lru_cache
 import gzip
 import json
 import logging
@@ -85,12 +81,9 @@ class AudioMeta(BaseInfo):
 class SegmentInfo(BaseInfo):
     meta: AudioMeta
     seek_time: float
-    # The following values are given once the audio is processed, e.g.
-    # at the target sample rate and target number of channels.
-    n_frames: int      # actual number of frames without padding
+    n_frames: int  # actual number of frames without padding
     total_frames: int  # total number of frames, padding included
-    sample_rate: int   # actual sample rate
-    channels: int      # number of audio channels.
+    sample_rate: int  # actual sample rate
 
 
 DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a']
@@ -121,8 +114,8 @@ def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta:
 
     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.
+        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.
     """
@@ -158,7 +151,7 @@ def find_audio_files(path: tp.Union[Path, str],
         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 of AudioMeta: List of audio file path and its metadata.
+        List[AudioMeta]: List of audio file path and its metadata.
     """
     audio_files = []
     futures: tp.List[Future] = []
@@ -210,7 +203,7 @@ def load_audio_meta(path: tp.Union[str, Path],
         resolve (bool): Whether to resolve the path from AudioMeta (default=True).
         fast (bool): activates some tricks to make things faster.
     Returns:
-        list of AudioMeta: List of audio file path and its total duration.
+        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
@@ -257,14 +250,9 @@ class AudioDataset:
     allows to return a tuple containing the torch Tensor and additional metadata on the segment and the
     original audio meta.
 
-    Note that you can call `start_epoch(epoch)` in order to get
-    a deterministic "randomization" for `shuffle=True`.
-    For a given epoch and dataset index, this will always return the same extract.
-    You can get back some diversity by setting the `shuffle_seed` param.
-
     Args:
-        meta (list of AudioMeta): List of audio files metadata.
-        segment_duration (float, optional): Optional segment duration of audio to load.
+        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.
@@ -278,19 +266,10 @@ class AudioDataset:
             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 (float, optional): Minimum audio file duration, in seconds, if provided
+        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 (float, optional): Maximal audio file duration in seconds, if provided
+        max_audio_duration (tp.Optional[float], optional): Maximal audio file duration in seconds, if provided
             audio longer than this will be filtered out.
-        shuffle_seed (int): can be used to further randomize
-        load_wav (bool): if False, skip loading the wav but returns a tensor of 0
-            with the expected segment_duration (which must be provided if load_wav is False).
-        permutation_on_files (bool): only if `sample_on_weight` and `sample_on_duration`
-            are False. Will ensure a permutation on files when going through the dataset.
-            In that case the epoch number must be provided in order for the model
-            to continue the permutation across epochs. In that case, it is assumed
-            that `num_samples = total_batch_size * num_updates_per_epoch`, with
-            `total_batch_size` the overall batch size accounting for all gpus.
     """
     def __init__(self,
                  meta: tp.List[AudioMeta],
@@ -306,14 +285,16 @@ class AudioDataset:
                  max_read_retry: int = 10,
                  return_info: bool = False,
                  min_audio_duration: tp.Optional[float] = None,
-                 max_audio_duration: tp.Optional[float] = None,
-                 shuffle_seed: int = 0,
-                 load_wav: bool = True,
-                 permutation_on_files: bool = False,
+                 max_audio_duration: tp.Optional[float] = None
                  ):
-        assert len(meta) > 0, "No audio meta provided to AudioDataset. Please check loading of audio meta."
+        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
@@ -336,25 +317,13 @@ class AudioDataset:
         self.sampling_probabilities = self._get_sampling_probabilities()
         self.max_read_retry = max_read_retry
         self.return_info = return_info
-        self.shuffle_seed = shuffle_seed
-        self.current_epoch: tp.Optional[int] = None
-        self.load_wav = load_wav
-        if not load_wav:
-            assert segment_duration is not None
-        self.permutation_on_files = permutation_on_files
-        if permutation_on_files:
-            assert not self.sample_on_duration
-            assert not self.sample_on_weight
-            assert self.shuffle
-
-    def start_epoch(self, epoch: int):
-        self.current_epoch = epoch
 
     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`."""
+        """Return the sampling probabilities for each file inside `self.meta`.
+        """
         scores: tp.List[float] = []
         for file_meta in self.meta:
             score = 1.
@@ -368,32 +337,12 @@ class AudioDataset:
             probabilities /= probabilities.sum()
         return probabilities
 
-    @staticmethod
-    @lru_cache(16)
-    def _get_file_permutation(num_files: int, permutation_index: int, base_seed: int):
-        # Used to keep the most recent files permutation in memory implicitely.
-        # will work unless someone is using a lot of Datasets in parallel.
-        rng = torch.Generator()
-        rng.manual_seed(base_seed + permutation_index)
-        return torch.randperm(num_files, generator=rng)
-
-    def sample_file(self, index: int, rng: torch.Generator) -> AudioMeta:
-        """Sample a given file from `self.meta`. Can be overridden in subclasses.
+    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.
-        You can further make use of the index accessed.
         """
-        if self.permutation_on_files:
-            assert self.current_epoch is not None
-            total_index = self.current_epoch * len(self) + index
-            permutation_index = total_index // len(self.meta)
-            relative_index = total_index % len(self.meta)
-            permutation = AudioDataset._get_file_permutation(
-                len(self.meta), permutation_index, self.shuffle_seed)
-            file_index = permutation[relative_index]
-            return self.meta[file_index]
-
         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:
@@ -401,15 +350,6 @@ class AudioDataset:
 
         return self.meta[file_index]
 
-    def _audio_read(self, path: str, seek_time: float = 0, duration: float = -1):
-        # Override this method in subclass if needed.
-        if self.load_wav:
-            return audio_read(path, seek_time, duration, pad=False)
-        else:
-            assert self.segment_duration is not None
-            n_frames = int(self.sample_rate * self.segment_duration)
-            return torch.zeros(self.channels, n_frames), self.sample_rate
-
     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]
@@ -417,22 +357,18 @@ class AudioDataset:
             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, channels=out.shape[0])
+                                       sample_rate=self.sample_rate)
         else:
             rng = torch.Generator()
             if self.shuffle:
-                # We use index, plus extra randomness, either totally random if we don't know the epoch.
-                # otherwise we make use of the epoch number and optional shuffle_seed.
-                if self.current_epoch is None:
-                    rng.manual_seed(index + self.num_samples * random.randint(0, 2**24))
-                else:
-                    rng.manual_seed(index + self.num_samples * (self.current_epoch + self.shuffle_seed))
+                # 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(index, rng)
+                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)
@@ -445,7 +381,7 @@ class AudioDataset:
                     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, channels=out.shape[0])
+                                               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:
@@ -487,7 +423,7 @@ class AudioDataset:
             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 length of the signal with padding, so we update here as we pad.
+                    # 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])
 
@@ -500,7 +436,9 @@ class AudioDataset:
             return torch.stack(samples)
 
     def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]:
-        """Filters out audio files with audio durations that will not allow to sample examples from them."""
+        """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.

audiocraft/data/audio_utils.py

@@ -3,8 +3,7 @@
 #
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
-"""Various utilities for audio convertion (pcm format, sample rate and channels),
-and volume normalization."""
+
 import sys
 import typing as tp
 
@@ -48,7 +47,8 @@ def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor
 
 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."""
+    """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
@@ -66,7 +66,7 @@ def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db
         loudness_compressor (bool): Uses tanh for soft clipping.
         energy_floor (float): anything below that RMS level will not be rescaled.
     Returns:
-        torch.Tensor: Loudness normalized output data.
+        output (torch.Tensor): Loudness normalized output data.
     """
     energy = wav.pow(2).mean().sqrt().item()
     if energy < energy_floor:
@@ -117,7 +117,7 @@ def normalize_audio(wav: torch.Tensor, normalize: bool = True,
         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 (str, optional): Stem name for clipping logging.
+        stem_name (Optional[str]): Stem name for clipping logging.
     Returns:
         torch.Tensor: Normalized audio.
     """
@@ -150,19 +150,17 @@ def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
     """
     if wav.dtype.is_floating_point:
         return wav
-    elif wav.dtype == torch.int16:
+    else:
+        assert wav.dtype == torch.int16
         return wav.float() / 2**15
-    elif wav.dtype == torch.int32:
-        return wav.float() / 2**31
-    raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
 
 
 def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
     """Convert audio to int 16 bits PCM format.
 
-    ..Warning:: There exist many formula for doing this conversion. None are perfect
-    due to the asymmetry of the int16 range. One either have possible clipping, DC offset,
-    or inconsistencies with f32_pcm. If the given wav doesn't have enough headroom,
+    ..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:

audiocraft/data/info_audio_dataset.py

@@ -1,110 +0,0 @@
-# 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 classes for the datasets that also provide non-audio metadata,
-e.g. description, text transcription etc.
-"""
-from dataclasses import dataclass
-import logging
-import math
-import re
-import typing as tp
-
-import torch
-
-from .audio_dataset import AudioDataset, AudioMeta
-from ..environment import AudioCraftEnvironment
-from ..modules.conditioners import SegmentWithAttributes, ConditioningAttributes
-
-
-logger = logging.getLogger(__name__)
-
-
-def _clusterify_meta(meta: AudioMeta) -> AudioMeta:
-    """Monkey-patch meta to match cluster specificities."""
-    meta.path = AudioCraftEnvironment.apply_dataset_mappers(meta.path)
-    if meta.info_path is not None:
-        meta.info_path.zip_path = AudioCraftEnvironment.apply_dataset_mappers(meta.info_path.zip_path)
-    return meta
-
-
-def clusterify_all_meta(meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]:
-    """Monkey-patch all meta to match cluster specificities."""
-    return [_clusterify_meta(m) for m in meta]
-
-
-@dataclass
-class AudioInfo(SegmentWithAttributes):
-    """Dummy SegmentInfo with empty attributes.
-
-    The InfoAudioDataset is expected to return metadata that inherits
-    from SegmentWithAttributes class and can return conditioning attributes.
-
-    This basically guarantees all datasets will be compatible with current
-    solver that contain conditioners requiring this.
-    """
-    audio_tokens: tp.Optional[torch.Tensor] = None  # populated when using cached batch for training a LM.
-
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        return ConditioningAttributes()
-
-
-class InfoAudioDataset(AudioDataset):
-    """AudioDataset that always returns metadata as SegmentWithAttributes along with the audio waveform.
-
-    See `audiocraft.data.audio_dataset.AudioDataset` for initialization arguments.
-    """
-    def __init__(self, meta: tp.List[AudioMeta], **kwargs):
-        super().__init__(clusterify_all_meta(meta), **kwargs)
-
-    def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentWithAttributes]]:
-        if not self.return_info:
-            wav = super().__getitem__(index)
-            assert isinstance(wav, torch.Tensor)
-            return wav
-        wav, meta = super().__getitem__(index)
-        return wav, AudioInfo(**meta.to_dict())
-
-
-def get_keyword_or_keyword_list(value: tp.Optional[str]) -> tp.Union[tp.Optional[str], tp.Optional[tp.List[str]]]:
-    """Preprocess a single keyword or possible a list of keywords."""
-    if isinstance(value, list):
-        return get_keyword_list(value)
-    else:
-        return get_keyword(value)
-
-
-def get_string(value: tp.Optional[str]) -> tp.Optional[str]:
-    """Preprocess a single keyword."""
-    if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None':
-        return None
-    else:
-        return value.strip()
-
-
-def get_keyword(value: tp.Optional[str]) -> tp.Optional[str]:
-    """Preprocess a single keyword."""
-    if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None':
-        return None
-    else:
-        return value.strip().lower()
-
-
-def get_keyword_list(values: tp.Union[str, tp.List[str]]) -> tp.Optional[tp.List[str]]:
-    """Preprocess a list of keywords."""
-    if isinstance(values, str):
-        values = [v.strip() for v in re.split(r'[,\s]', values)]
-    elif isinstance(values, float) and math.isnan(values):
-        values = []
-    if not isinstance(values, list):
-        logger.debug(f"Unexpected keyword list {values}")
-        values = [str(values)]
-
-    kws = [get_keyword(v) for v in values]
-    kw_list = [k for k in kws if k is not None]
-    if len(kw_list) == 0:
-        return None
-    else:
-        return kw_list

audiocraft/data/music_dataset.py

@@ -1,270 +0,0 @@
-# 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.
-"""Dataset of music tracks with rich metadata.
-"""
-from dataclasses import dataclass, field, fields, replace
-import gzip
-import json
-import logging
-from pathlib import Path
-import random
-import typing as tp
-
-import torch
-
-from .info_audio_dataset import (
-    InfoAudioDataset,
-    AudioInfo,
-    get_keyword_list,
-    get_keyword,
-    get_string
-)
-from ..modules.conditioners import (
-    ConditioningAttributes,
-    JointEmbedCondition,
-    WavCondition,
-)
-from ..utils.utils import warn_once
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class MusicInfo(AudioInfo):
-    """Segment info augmented with music metadata.
-    """
-    # music-specific metadata
-    title: tp.Optional[str] = None
-    artist: tp.Optional[str] = None  # anonymized artist id, used to ensure no overlap between splits
-    key: tp.Optional[str] = None
-    bpm: tp.Optional[float] = None
-    genre: tp.Optional[str] = None
-    moods: tp.Optional[list] = None
-    keywords: tp.Optional[list] = None
-    description: tp.Optional[str] = None
-    name: tp.Optional[str] = None
-    instrument: tp.Optional[str] = None
-    # original wav accompanying the metadata
-    self_wav: tp.Optional[WavCondition] = None
-    # dict mapping attributes names to tuple of wav, text and metadata
-    joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
-
-    @property
-    def has_music_meta(self) -> bool:
-        return self.name is not None
-
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        out = ConditioningAttributes()
-        for _field in fields(self):
-            key, value = _field.name, getattr(self, _field.name)
-            if key == 'self_wav':
-                out.wav[key] = value
-            elif key == 'joint_embed':
-                for embed_attribute, embed_cond in value.items():
-                    out.joint_embed[embed_attribute] = embed_cond
-            else:
-                if isinstance(value, list):
-                    value = ' '.join(value)
-                out.text[key] = value
-        return out
-
-    @staticmethod
-    def attribute_getter(attribute):
-        if attribute == 'bpm':
-            preprocess_func = get_bpm
-        elif attribute == 'key':
-            preprocess_func = get_musical_key
-        elif attribute in ['moods', 'keywords']:
-            preprocess_func = get_keyword_list
-        elif attribute in ['genre', 'name', 'instrument']:
-            preprocess_func = get_keyword
-        elif attribute in ['title', 'artist', 'description']:
-            preprocess_func = get_string
-        else:
-            preprocess_func = None
-        return preprocess_func
-
-    @classmethod
-    def from_dict(cls, dictionary: dict, fields_required: bool = False):
-        _dictionary: tp.Dict[str, tp.Any] = {}
-
-        # allow a subset of attributes to not be loaded from the dictionary
-        # these attributes may be populated later
-        post_init_attributes = ['self_wav', 'joint_embed']
-        optional_fields = ['keywords']
-
-        for _field in fields(cls):
-            if _field.name in post_init_attributes:
-                continue
-            elif _field.name not in dictionary:
-                if fields_required and _field.name not in optional_fields:
-                    raise KeyError(f"Unexpected missing key: {_field.name}")
-            else:
-                preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
-                value = dictionary[_field.name]
-                if preprocess_func:
-                    value = preprocess_func(value)
-                _dictionary[_field.name] = value
-        return cls(**_dictionary)
-
-
-def augment_music_info_description(music_info: MusicInfo, merge_text_p: float = 0.,
-                                   drop_desc_p: float = 0., drop_other_p: float = 0.) -> MusicInfo:
-    """Augment MusicInfo description with additional metadata fields and potential dropout.
-    Additional textual attributes are added given probability 'merge_text_conditions_p' and
-    the original textual description is dropped from the augmented description given probability drop_desc_p.
-
-    Args:
-        music_info (MusicInfo): The music metadata to augment.
-        merge_text_p (float): Probability of merging additional metadata to the description.
-            If provided value is 0, then no merging is performed.
-        drop_desc_p (float): Probability of dropping the original description on text merge.
-            if provided value is 0, then no drop out is performed.
-        drop_other_p (float): Probability of dropping the other fields used for text augmentation.
-    Returns:
-        MusicInfo: The MusicInfo with augmented textual description.
-    """
-    def is_valid_field(field_name: str, field_value: tp.Any) -> bool:
-        valid_field_name = field_name in ['key', 'bpm', 'genre', 'moods', 'instrument', 'keywords']
-        valid_field_value = field_value is not None and isinstance(field_value, (int, float, str, list))
-        keep_field = random.uniform(0, 1) < drop_other_p
-        return valid_field_name and valid_field_value and keep_field
-
-    def process_value(v: tp.Any) -> str:
-        if isinstance(v, (int, float, str)):
-            return str(v)
-        if isinstance(v, list):
-            return ", ".join(v)
-        else:
-            raise ValueError(f"Unknown type for text value! ({type(v), v})")
-
-    description = music_info.description
-
-    metadata_text = ""
-    if random.uniform(0, 1) < merge_text_p:
-        meta_pairs = [f'{_field.name}: {process_value(getattr(music_info, _field.name))}'
-                      for _field in fields(music_info) if is_valid_field(_field.name, getattr(music_info, _field.name))]
-        random.shuffle(meta_pairs)
-        metadata_text = ". ".join(meta_pairs)
-        description = description if not random.uniform(0, 1) < drop_desc_p else None
-        logger.debug(f"Applying text augmentation on MMI info. description: {description}, metadata: {metadata_text}")
-
-    if description is None:
-        description = metadata_text if len(metadata_text) > 1 else None
-    else:
-        description = ". ".join([description.rstrip('.'), metadata_text])
-    description = description.strip() if description else None
-
-    music_info = replace(music_info)
-    music_info.description = description
-    return music_info
-
-
-class Paraphraser:
-    def __init__(self, paraphrase_source: tp.Union[str, Path], paraphrase_p: float = 0.):
-        self.paraphrase_p = paraphrase_p
-        open_fn = gzip.open if str(paraphrase_source).lower().endswith('.gz') else open
-        with open_fn(paraphrase_source, 'rb') as f:  # type: ignore
-            self.paraphrase_source = json.loads(f.read())
-        logger.info(f"loaded paraphrasing source from: {paraphrase_source}")
-
-    def sample_paraphrase(self, audio_path: str, description: str):
-        if random.random() >= self.paraphrase_p:
-            return description
-        info_path = Path(audio_path).with_suffix('.json')
-        if info_path not in self.paraphrase_source:
-            warn_once(logger, f"{info_path} not in paraphrase source!")
-            return description
-        new_desc = random.choice(self.paraphrase_source[info_path])
-        logger.debug(f"{description} -> {new_desc}")
-        return new_desc
-
-
-class MusicDataset(InfoAudioDataset):
-    """Music dataset is an AudioDataset with music-related metadata.
-
-    Args:
-        info_fields_required (bool): Whether to enforce having required fields.
-        merge_text_p (float): Probability of merging additional metadata to the description.
-        drop_desc_p (float): Probability of dropping the original description on text merge.
-        drop_other_p (float): Probability of dropping the other fields used for text augmentation.
-        joint_embed_attributes (list[str]): A list of attributes for which joint embedding metadata is returned.
-        paraphrase_source (str, optional): Path to the .json or .json.gz file containing the
-            paraphrases for the description. The json should be a dict with keys are the
-            original info path (e.g. track_path.json) and each value is a list of possible
-            paraphrased.
-        paraphrase_p (float): probability of taking a paraphrase.
-
-    See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
-    """
-    def __init__(self, *args, info_fields_required: bool = True,
-                 merge_text_p: float = 0., drop_desc_p: float = 0., drop_other_p: float = 0.,
-                 joint_embed_attributes: tp.List[str] = [],
-                 paraphrase_source: tp.Optional[str] = None, paraphrase_p: float = 0,
-                 **kwargs):
-        kwargs['return_info'] = True  # We require the info for each song of the dataset.
-        super().__init__(*args, **kwargs)
-        self.info_fields_required = info_fields_required
-        self.merge_text_p = merge_text_p
-        self.drop_desc_p = drop_desc_p
-        self.drop_other_p = drop_other_p
-        self.joint_embed_attributes = joint_embed_attributes
-        self.paraphraser = None
-        if paraphrase_source is not None:
-            self.paraphraser = Paraphraser(paraphrase_source, paraphrase_p)
-
-    def __getitem__(self, index):
-        wav, info = super().__getitem__(index)
-        info_data = info.to_dict()
-        music_info_path = Path(info.meta.path).with_suffix('.json')
-
-        if Path(music_info_path).exists():
-            with open(music_info_path, 'r') as json_file:
-                music_data = json.load(json_file)
-                music_data.update(info_data)
-                music_info = MusicInfo.from_dict(music_data, fields_required=self.info_fields_required)
-            if self.paraphraser is not None:
-                music_info.description = self.paraphraser.sample(music_info.meta.path, music_info.description)
-            if self.merge_text_p:
-                music_info = augment_music_info_description(
-                    music_info, self.merge_text_p, self.drop_desc_p, self.drop_other_p)
-        else:
-            music_info = MusicInfo.from_dict(info_data, fields_required=False)
-
-        music_info.self_wav = WavCondition(
-            wav=wav[None], length=torch.tensor([info.n_frames]),
-            sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
-        for att in self.joint_embed_attributes:
-            att_value = getattr(music_info, att)
-            joint_embed_cond = JointEmbedCondition(
-                wav[None], [att_value], torch.tensor([info.n_frames]),
-                sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-            music_info.joint_embed[att] = joint_embed_cond
-
-        return wav, music_info
-
-
-def get_musical_key(value: tp.Optional[str]) -> tp.Optional[str]:
-    """Preprocess key keywords, discarding them if there are multiple key defined."""
-    if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None':
-        return None
-    elif ',' in value:
-        # For now, we discard when multiple keys are defined separated with comas
-        return None
-    else:
-        return value.strip().lower()
-
-
-def get_bpm(value: tp.Optional[str]) -> tp.Optional[float]:
-    """Preprocess to a float."""
-    if value is None:
-        return None
-    try:
-        return float(value)
-    except ValueError:
-        return None

audiocraft/data/sound_dataset.py

@@ -1,330 +0,0 @@
-# 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.
-"""Dataset of audio with a simple description.
-"""
-
-from dataclasses import dataclass, fields, replace
-import json
-from pathlib import Path
-import random
-import typing as tp
-
-import numpy as np
-import torch
-
-from .info_audio_dataset import (
-    InfoAudioDataset,
-    get_keyword_or_keyword_list
-)
-from ..modules.conditioners import (
-    ConditioningAttributes,
-    SegmentWithAttributes,
-    WavCondition,
-)
-
-
-EPS = torch.finfo(torch.float32).eps
-TARGET_LEVEL_LOWER = -35
-TARGET_LEVEL_UPPER = -15
-
-
-@dataclass
-class SoundInfo(SegmentWithAttributes):
-    """Segment info augmented with Sound metadata.
-    """
-    description: tp.Optional[str] = None
-    self_wav: tp.Optional[torch.Tensor] = None
-
-    @property
-    def has_sound_meta(self) -> bool:
-        return self.description is not None
-
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        out = ConditioningAttributes()
-
-        for _field in fields(self):
-            key, value = _field.name, getattr(self, _field.name)
-            if key == 'self_wav':
-                out.wav[key] = value
-            else:
-                out.text[key] = value
-        return out
-
-    @staticmethod
-    def attribute_getter(attribute):
-        if attribute == 'description':
-            preprocess_func = get_keyword_or_keyword_list
-        else:
-            preprocess_func = None
-        return preprocess_func
-
-    @classmethod
-    def from_dict(cls, dictionary: dict, fields_required: bool = False):
-        _dictionary: tp.Dict[str, tp.Any] = {}
-
-        # allow a subset of attributes to not be loaded from the dictionary
-        # these attributes may be populated later
-        post_init_attributes = ['self_wav']
-
-        for _field in fields(cls):
-            if _field.name in post_init_attributes:
-                continue
-            elif _field.name not in dictionary:
-                if fields_required:
-                    raise KeyError(f"Unexpected missing key: {_field.name}")
-            else:
-                preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
-                value = dictionary[_field.name]
-                if preprocess_func:
-                    value = preprocess_func(value)
-                _dictionary[_field.name] = value
-        return cls(**_dictionary)
-
-
-class SoundDataset(InfoAudioDataset):
-    """Sound audio dataset: Audio dataset with environmental sound-specific metadata.
-
-    Args:
-        info_fields_required (bool): Whether all the mandatory metadata fields should be in the loaded metadata.
-        external_metadata_source (tp.Optional[str]): Folder containing JSON metadata for the corresponding dataset.
-            The metadata files contained in this folder are expected to match the stem of the audio file with
-            a json extension.
-        aug_p (float): Probability of performing audio mixing augmentation on the batch.
-        mix_p (float): Proportion of batch items that are mixed together when applying audio mixing augmentation.
-        mix_snr_low (int): Lowerbound for SNR value sampled for mixing augmentation.
-        mix_snr_high (int): Upperbound for SNR value sampled for mixing augmentation.
-        mix_min_overlap (float): Minimum overlap between audio files when performing mixing augmentation.
-        kwargs: Additional arguments for AudioDataset.
-
-    See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
-    """
-    def __init__(
-        self,
-        *args,
-        info_fields_required: bool = True,
-        external_metadata_source: tp.Optional[str] = None,
-        aug_p: float = 0.,
-        mix_p: float = 0.,
-        mix_snr_low: int = -5,
-        mix_snr_high: int = 5,
-        mix_min_overlap: float = 0.5,
-        **kwargs
-    ):
-        kwargs['return_info'] = True  # We require the info for each song of the dataset.
-        super().__init__(*args, **kwargs)
-        self.info_fields_required = info_fields_required
-        self.external_metadata_source = external_metadata_source
-        self.aug_p = aug_p
-        self.mix_p = mix_p
-        if self.aug_p > 0:
-            assert self.mix_p > 0, "Expecting some mixing proportion mix_p if aug_p > 0"
-            assert self.channels == 1, "SoundDataset with audio mixing considers only monophonic audio"
-        self.mix_snr_low = mix_snr_low
-        self.mix_snr_high = mix_snr_high
-        self.mix_min_overlap = mix_min_overlap
-
-    def _get_info_path(self, path: tp.Union[str, Path]) -> Path:
-        """Get path of JSON with metadata (description, etc.).
-        If there exists a JSON with the same name as 'path.name', then it will be used.
-        Else, such JSON will be searched for in an external json source folder if it exists.
-        """
-        info_path = Path(path).with_suffix('.json')
-        if Path(info_path).exists():
-            return info_path
-        elif self.external_metadata_source and (Path(self.external_metadata_source) / info_path.name).exists():
-            return Path(self.external_metadata_source) / info_path.name
-        else:
-            raise Exception(f"Unable to find a metadata JSON for path: {path}")
-
-    def __getitem__(self, index):
-        wav, info = super().__getitem__(index)
-        info_data = info.to_dict()
-        info_path = self._get_info_path(info.meta.path)
-        if Path(info_path).exists():
-            with open(info_path, 'r') as json_file:
-                sound_data = json.load(json_file)
-                sound_data.update(info_data)
-                sound_info = SoundInfo.from_dict(sound_data, fields_required=self.info_fields_required)
-                # if there are multiple descriptions, sample one randomly
-                if isinstance(sound_info.description, list):
-                    sound_info.description = random.choice(sound_info.description)
-        else:
-            sound_info = SoundInfo.from_dict(info_data, fields_required=False)
-
-        sound_info.self_wav = WavCondition(
-            wav=wav[None], length=torch.tensor([info.n_frames]),
-            sample_rate=[sound_info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
-        return wav, sound_info
-
-    def collater(self, samples):
-        # when training, audio mixing is performed in the collate function
-        wav, sound_info = super().collater(samples)  # SoundDataset always returns infos
-        if self.aug_p > 0:
-            wav, sound_info = mix_samples(wav, sound_info, self.aug_p, self.mix_p,
-                                          snr_low=self.mix_snr_low, snr_high=self.mix_snr_high,
-                                          min_overlap=self.mix_min_overlap)
-        return wav, sound_info
-
-
-def rms_f(x: torch.Tensor) -> torch.Tensor:
-    return (x ** 2).mean(1).pow(0.5)
-
-
-def normalize(audio: torch.Tensor, target_level: int = -25) -> torch.Tensor:
-    """Normalize the signal to the target level."""
-    rms = rms_f(audio)
-    scalar = 10 ** (target_level / 20) / (rms + EPS)
-    audio = audio * scalar.unsqueeze(1)
-    return audio
-
-
-def is_clipped(audio: torch.Tensor, clipping_threshold: float = 0.99) -> torch.Tensor:
-    return (abs(audio) > clipping_threshold).any(1)
-
-
-def mix_pair(src: torch.Tensor, dst: torch.Tensor, min_overlap: float) -> torch.Tensor:
-    start = random.randint(0, int(src.shape[1] * (1 - min_overlap)))
-    remainder = src.shape[1] - start
-    if dst.shape[1] > remainder:
-        src[:, start:] = src[:, start:] + dst[:, :remainder]
-    else:
-        src[:, start:start+dst.shape[1]] = src[:, start:start+dst.shape[1]] + dst
-    return src
-
-
-def snr_mixer(clean: torch.Tensor, noise: torch.Tensor, snr: int, min_overlap: float,
-              target_level: int = -25, clipping_threshold: float = 0.99) -> torch.Tensor:
-    """Function to mix clean speech and noise at various SNR levels.
-
-    Args:
-        clean (torch.Tensor): Clean audio source to mix, of shape [B, T].
-        noise (torch.Tensor): Noise audio source to mix, of shape [B, T].
-        snr (int): SNR level when mixing.
-        min_overlap (float): Minimum overlap between the two mixed sources.
-        target_level (int): Gain level in dB.
-        clipping_threshold (float): Threshold for clipping the audio.
-    Returns:
-        torch.Tensor: The mixed audio, of shape [B, T].
-    """
-    if clean.shape[1] > noise.shape[1]:
-        noise = torch.nn.functional.pad(noise, (0, clean.shape[1] - noise.shape[1]))
-    else:
-        noise = noise[:, :clean.shape[1]]
-
-    # normalizing to -25 dB FS
-    clean = clean / (clean.max(1)[0].abs().unsqueeze(1) + EPS)
-    clean = normalize(clean, target_level)
-    rmsclean = rms_f(clean)
-
-    noise = noise / (noise.max(1)[0].abs().unsqueeze(1) + EPS)
-    noise = normalize(noise, target_level)
-    rmsnoise = rms_f(noise)
-
-    # set the noise level for a given SNR
-    noisescalar = (rmsclean / (10 ** (snr / 20)) / (rmsnoise + EPS)).unsqueeze(1)
-    noisenewlevel = noise * noisescalar
-
-    # mix noise and clean speech
-    noisyspeech = mix_pair(clean, noisenewlevel, min_overlap)
-
-    # randomly select RMS value between -15 dBFS and -35 dBFS and normalize noisyspeech with that value
-    # there is a chance of clipping that might happen with very less probability, which is not a major issue.
-    noisy_rms_level = np.random.randint(TARGET_LEVEL_LOWER, TARGET_LEVEL_UPPER)
-    rmsnoisy = rms_f(noisyspeech)
-    scalarnoisy = (10 ** (noisy_rms_level / 20) / (rmsnoisy + EPS)).unsqueeze(1)
-    noisyspeech = noisyspeech * scalarnoisy
-    clean = clean * scalarnoisy
-    noisenewlevel = noisenewlevel * scalarnoisy
-
-    # final check to see if there are any amplitudes exceeding +/- 1. If so, normalize all the signals accordingly
-    clipped = is_clipped(noisyspeech)
-    if clipped.any():
-        noisyspeech_maxamplevel = noisyspeech[clipped].max(1)[0].abs().unsqueeze(1) / (clipping_threshold - EPS)
-        noisyspeech[clipped] = noisyspeech[clipped] / noisyspeech_maxamplevel
-
-    return noisyspeech
-
-
-def snr_mix(src: torch.Tensor, dst: torch.Tensor, snr_low: int, snr_high: int, min_overlap: float):
-    if snr_low == snr_high:
-        snr = snr_low
-    else:
-        snr = np.random.randint(snr_low, snr_high)
-    mix = snr_mixer(src, dst, snr, min_overlap)
-    return mix
-
-
-def mix_text(src_text: str, dst_text: str):
-    """Mix text from different sources by concatenating them."""
-    if src_text == dst_text:
-        return src_text
-    return src_text + " " + dst_text
-
-
-def mix_samples(wavs: torch.Tensor, infos: tp.List[SoundInfo], aug_p: float, mix_p: float,
-                snr_low: int, snr_high: int, min_overlap: float):
-    """Mix samples within a batch, summing the waveforms and concatenating the text infos.
-
-    Args:
-        wavs (torch.Tensor): Audio tensors of shape [B, C, T].
-        infos (list[SoundInfo]): List of SoundInfo items corresponding to the audio.
-        aug_p (float): Augmentation probability.
-        mix_p (float): Proportion of items in the batch to mix (and merge) together.
-        snr_low (int): Lowerbound for sampling SNR.
-        snr_high (int): Upperbound for sampling SNR.
-        min_overlap (float): Minimum overlap between mixed samples.
-    Returns:
-        tuple[torch.Tensor, list[SoundInfo]]: A tuple containing the mixed wavs
-            and mixed SoundInfo for the given batch.
-    """
-    # no mixing to perform within the batch
-    if mix_p == 0:
-        return wavs, infos
-
-    if random.uniform(0, 1) < aug_p:
-        # perform all augmentations on waveforms as [B, T]
-        # randomly picking pairs of audio to mix
-        assert wavs.size(1) == 1, f"Mix samples requires monophonic audio but C={wavs.size(1)}"
-        wavs = wavs.mean(dim=1, keepdim=False)
-        B, T = wavs.shape
-        k = int(mix_p * B)
-        mixed_sources_idx = torch.randperm(B)[:k]
-        mixed_targets_idx = torch.randperm(B)[:k]
-        aug_wavs = snr_mix(
-            wavs[mixed_sources_idx],
-            wavs[mixed_targets_idx],
-            snr_low,
-            snr_high,
-            min_overlap,
-        )
-        # mixing textual descriptions in metadata
-        descriptions = [info.description for info in infos]
-        aug_infos = []
-        for i, j in zip(mixed_sources_idx, mixed_targets_idx):
-            text = mix_text(descriptions[i], descriptions[j])
-            m = replace(infos[i])
-            m.description = text
-            aug_infos.append(m)
-
-        # back to [B, C, T]
-        aug_wavs = aug_wavs.unsqueeze(1)
-        assert aug_wavs.shape[0] > 0, "Samples mixing returned empty batch."
-        assert aug_wavs.dim() == 3, f"Returned wav should be [B, C, T] but dim = {aug_wavs.dim()}"
-        assert aug_wavs.shape[0] == len(aug_infos), "Mismatch between number of wavs and infos in the batch"
-
-        return aug_wavs, aug_infos  # [B, C, T]
-    else:
-        # randomly pick samples in the batch to match
-        # the batch size when performing audio mixing
-        B, C, T = wavs.shape
-        k = int(mix_p * B)
-        wav_idx = torch.randperm(B)[:k]
-        wavs = wavs[wav_idx]
-        infos = [infos[i] for i in wav_idx]
-        assert wavs.shape[0] == len(infos), "Mismatch between number of wavs and infos in the batch"
-
-        return wavs, infos  # [B, C, T]

audiocraft/data/zip.py

@@ -3,8 +3,6 @@
 #
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
-"""Utility for reading some info from inside a zip file.
-"""
 
 import typing
 import zipfile
@@ -20,13 +18,13 @@ MODE = Literal['r', 'w', 'x', 'a']
 
 @dataclass(order=True)
 class PathInZip:
-    """Hold a path of file within a zip file.
+    """Class for holding a path of file within a zip file.
 
     Args:
-        path (str): The convention is <path_to_zip>:<relative_path_inside_zip>.
+        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".
+            Then we expect path = "/some/location/foo.zip:/data/file1.json"
     """
 
     INFO_PATH_SEP = ':'
@@ -57,7 +55,7 @@ def set_zip_cache_size(max_size: int):
     """Sets the maximal LRU caching for zip file opening.
 
     Args:
-        max_size (int): the maximal LRU cache.
+        max_size: the maximal LRU cache.
     """
     global _cached_open_zip
     _cached_open_zip = lru_cache(max_size)(_open_zip)
@@ -67,8 +65,8 @@ 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 (PathInZip): A PathInZip object representing the file to return a file-like object of.
-        mode (str): The mode in which to open the file with.
+        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.
     """

audiocraft/environment.py

@@ -1,176 +0,0 @@
-# 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.
-
-"""
-Provides cluster and tools configuration across clusters (slurm, dora, utilities).
-"""
-
-import logging
-import os
-from pathlib import Path
-import re
-import typing as tp
-
-import omegaconf
-
-from .utils.cluster import _guess_cluster_type
-
-
-logger = logging.getLogger(__name__)
-
-
-class AudioCraftEnvironment:
-    """Environment configuration for teams and clusters.
-
-    AudioCraftEnvironment picks compute cluster settings (slurm, dora) from the current running environment
-    or declared variable and the loaded team configuration. Additionally, the AudioCraftEnvironment
-    provides pointers to a reference folder resolved automatically across clusters that is shared across team members,
-    allowing to share sigs or other files to run jobs. Finally, it provides dataset mappers to automatically
-    map dataset file paths to new locations across clusters, allowing to use the same manifest of files across cluters.
-
-    The cluster type is identified automatically and base configuration file is read from config/teams.yaml.
-    Use the following environment variables to specify the cluster, team or configuration:
-
-        AUDIOCRAFT_CLUSTER (optional): Cluster type to enforce. Useful if the cluster type
-            cannot be inferred automatically.
-        AUDIOCRAFT_CONFIG (optional): Path to yaml config holding the teams configuration.
-            If not set, configuration is read from config/teams.yaml.
-        AUDIOCRAFT_TEAM (optional): Name of the team. Recommended to set to your own team.
-            Cluster configuration are shared across teams to match compute allocation,
-            specify your cluster configuration in the configuration file under a key mapping
-            your team name.
-    """
-    _instance = None
-    DEFAULT_TEAM = "default"
-
-    def __init__(self) -> None:
-        """Loads configuration."""
-        self.team: str = os.getenv("AUDIOCRAFT_TEAM", self.DEFAULT_TEAM)
-        cluster_type = _guess_cluster_type()
-        cluster = os.getenv(
-            "AUDIOCRAFT_CLUSTER", cluster_type.value
-        )
-        logger.info("Detecting cluster type %s", cluster_type)
-
-        self.cluster: str = cluster
-
-        config_path = os.getenv(
-            "AUDIOCRAFT_CONFIG",
-            Path(__file__)
-            .parent.parent.joinpath("config/teams", self.team)
-            .with_suffix(".yaml"),
-        )
-        self.config = omegaconf.OmegaConf.load(config_path)
-        self._dataset_mappers = []
-        cluster_config = self._get_cluster_config()
-        if "dataset_mappers" in cluster_config:
-            for pattern, repl in cluster_config["dataset_mappers"].items():
-                regex = re.compile(pattern)
-                self._dataset_mappers.append((regex, repl))
-
-    def _get_cluster_config(self) -> omegaconf.DictConfig:
-        assert isinstance(self.config, omegaconf.DictConfig)
-        return self.config[self.cluster]
-
-    @classmethod
-    def instance(cls):
-        if cls._instance is None:
-            cls._instance = cls()
-        return cls._instance
-
-    @classmethod
-    def reset(cls):
-        """Clears the environment and forces a reload on next invocation."""
-        cls._instance = None
-
-    @classmethod
-    def get_team(cls) -> str:
-        """Gets the selected team as dictated by the AUDIOCRAFT_TEAM env var.
-        If not defined, defaults to "labs".
-        """
-        return cls.instance().team
-
-    @classmethod
-    def get_cluster(cls) -> str:
-        """Gets the detected cluster.
-        This value can be overridden by the AUDIOCRAFT_CLUSTER env var.
-        """
-        return cls.instance().cluster
-
-    @classmethod
-    def get_dora_dir(cls) -> Path:
-        """Gets the path to the dora directory for the current team and cluster.
-        Value is overridden by the AUDIOCRAFT_DORA_DIR env var.
-        """
-        cluster_config = cls.instance()._get_cluster_config()
-        dora_dir = os.getenv("AUDIOCRAFT_DORA_DIR", cluster_config["dora_dir"])
-        logger.warning(f"Dora directory: {dora_dir}")
-        return Path(dora_dir)
-
-    @classmethod
-    def get_reference_dir(cls) -> Path:
-        """Gets the path to the reference directory for the current team and cluster.
-        Value is overridden by the AUDIOCRAFT_REFERENCE_DIR env var.
-        """
-        cluster_config = cls.instance()._get_cluster_config()
-        return Path(os.getenv("AUDIOCRAFT_REFERENCE_DIR", cluster_config["reference_dir"]))
-
-    @classmethod
-    def get_slurm_exclude(cls) -> tp.Optional[str]:
-        """Get the list of nodes to exclude for that cluster."""
-        cluster_config = cls.instance()._get_cluster_config()
-        return cluster_config.get("slurm_exclude")
-
-    @classmethod
-    def get_slurm_partitions(cls, partition_types: tp.Optional[tp.List[str]] = None) -> str:
-        """Gets the requested partitions for the current team and cluster as a comma-separated string.
-
-        Args:
-            partition_types (list[str], optional): partition types to retrieve. Values must be
-                from ['global', 'team']. If not provided, the global partition is returned.
-        """
-        if not partition_types:
-            partition_types = ["global"]
-
-        cluster_config = cls.instance()._get_cluster_config()
-        partitions = [
-            cluster_config["partitions"][partition_type]
-            for partition_type in partition_types
-        ]
-        return ",".join(partitions)
-
-    @classmethod
-    def resolve_reference_path(cls, path: tp.Union[str, Path]) -> Path:
-        """Converts reference placeholder in path with configured reference dir to resolve paths.
-
-        Args:
-            path (str or Path): Path to resolve.
-        Returns:
-            Path: Resolved path.
-        """
-        path = str(path)
-
-        if path.startswith("//reference"):
-            reference_dir = cls.get_reference_dir()
-            logger.warn(f"Reference directory: {reference_dir}")
-            assert (
-                reference_dir.exists() and reference_dir.is_dir()
-            ), f"Reference directory does not exist: {reference_dir}."
-            path = re.sub("^//reference", str(reference_dir), path)
-
-        return Path(path)
-
-    @classmethod
-    def apply_dataset_mappers(cls, path: str) -> str:
-        """Applies dataset mapping regex rules as defined in the configuration.
-        If no rules are defined, the path is returned as-is.
-        """
-        instance = cls.instance()
-
-        for pattern, repl in instance._dataset_mappers:
-            path = pattern.sub(repl, path)
-
-        return path

audiocraft/grids/__init__.py

@@ -1,6 +0,0 @@
-# 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.
-"""Dora Grids."""

audiocraft/grids/_base_explorers.py

@@ -1,80 +0,0 @@
-# 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 time
-import typing as tp
-from dora import Explorer
-import treetable as tt
-
-
-def get_sheep_ping(sheep) -> tp.Optional[str]:
-    """Return the amount of time since the Sheep made some update
-    to its log. Returns a str using the relevant time unit."""
-    ping = None
-    if sheep.log is not None and sheep.log.exists():
-        delta = time.time() - sheep.log.stat().st_mtime
-        if delta > 3600 * 24:
-            ping = f'{delta / (3600 * 24):.1f}d'
-        elif delta > 3600:
-            ping = f'{delta / (3600):.1f}h'
-        elif delta > 60:
-            ping = f'{delta / 60:.1f}m'
-        else:
-            ping = f'{delta:.1f}s'
-    return ping
-
-
-class BaseExplorer(ABC, Explorer):
-    """Base explorer for AudioCraft grids.
-
-    All task specific solvers are expected to implement the `get_grid_metrics`
-    method to specify logic about metrics to display for a given task.
-
-    If additional stages are used, the child explorer must define how to handle
-    these new stages in the `process_history` and `process_sheep` methods.
-    """
-    def stages(self):
-        return ["train", "valid", "evaluate"]
-
-    def get_grid_meta(self):
-        """Returns the list of Meta information to display for each XP/job.
-        """
-        return [
-            tt.leaf("index", align=">"),
-            tt.leaf("name", wrap=140),
-            tt.leaf("state"),
-            tt.leaf("sig", align=">"),
-            tt.leaf("sid", align="<"),
-        ]
-
-    @abstractmethod
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table.
-        """
-        ...
-
-    def process_sheep(self, sheep, history):
-        train = {
-            "epoch": len(history),
-        }
-        parts = {"train": train}
-        for metrics in history:
-            for key, sub in metrics.items():
-                part = parts.get(key, {})
-                if 'duration' in sub:
-                    # Convert to minutes for readability.
-                    sub['duration'] = sub['duration'] / 60.
-                part.update(sub)
-                parts[key] = part
-        ping = get_sheep_ping(sheep)
-        if ping is not None:
-            for name in self.stages():
-                if name not in parts:
-                    parts[name] = {}
-                # Add the ping to each part for convenience.
-                parts[name]['ping'] = ping
-        return parts

audiocraft/grids/audiogen/__init__.py

@@ -1,6 +0,0 @@
-# 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.
-"""AudioGen grids."""

audiocraft/grids/audiogen/audiogen_base_16khz.py

@@ -1,23 +0,0 @@
-# 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 ..musicgen._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=64, partition=partitions)
-    launcher.bind_(solver='audiogen/audiogen_base_16khz')
-    # replace this by the desired environmental sound dataset
-    launcher.bind_(dset='internal/sounds_16khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-
-    launcher.bind_(fsdp)
-    launcher(medium)

audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py

@@ -1,68 +0,0 @@
-# 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.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
-    opts = {
-        'dset': 'audio/audiocaps_16khz',
-        'solver/audiogen/evaluation': 'objective_eval',
-        'execute_only': 'evaluate',
-        '+dataset.evaluate.batch_size': batch_size,
-        '+metrics.fad.tf.batch_size': 32,
-    }
-    # binary for FAD computation: replace this path with your own path
-    metrics_opts = {
-        'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
-    }
-    opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
-    opt2 = {'transformer_lm.two_step_cfg': True}
-
-    sub = launcher.bind(opts)
-    sub.bind_(metrics_opts)
-
-    # base objective metrics
-    sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=4, partition=partitions)
-
-    if 'REGEN' not in os.environ:
-        folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
-        with launcher.job_array():
-            for sig in folder.iterdir():
-                if not sig.is_symlink():
-                    continue
-                xp = train.main.get_xp_from_sig(sig.name)
-                launcher(xp.argv)
-        return
-
-    audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
-    audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
-    audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
-    audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
-    eval(audiogen_base_medium, batch_size=128)

audiocraft/grids/compression/__init__.py

@@ -1,6 +0,0 @@
-# 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.
-"""EnCodec grids."""

audiocraft/grids/compression/_explorers.py

@@ -1,55 +0,0 @@
-# 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 treetable as tt
-
-from .._base_explorers import BaseExplorer
-
-
-class CompressionExplorer(BaseExplorer):
-    eval_metrics = ["sisnr", "visqol"]
-
-    def stages(self):
-        return ["train", "valid", "evaluate"]
-
-    def get_grid_meta(self):
-        """Returns the list of Meta information to display for each XP/job.
-        """
-        return [
-            tt.leaf("index", align=">"),
-            tt.leaf("name", wrap=140),
-            tt.leaf("state"),
-            tt.leaf("sig", align=">"),
-        ]
-
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table.
-        """
-        return [
-            tt.group(
-                "train",
-                [
-                    tt.leaf("epoch"),
-                    tt.leaf("bandwidth", ".2f"),
-                    tt.leaf("adv", ".4f"),
-                    tt.leaf("d_loss", ".4f"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "valid",
-                [
-                    tt.leaf("bandwidth", ".2f"),
-                    tt.leaf("adv", ".4f"),
-                    tt.leaf("msspec", ".4f"),
-                    tt.leaf("sisnr", ".2f"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "evaluate", [tt.leaf(name, ".3f") for name in self.eval_metrics], align=">"
-            ),
-        ]

audiocraft/grids/compression/debug.py

@@ -1,31 +0,0 @@
-# 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.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=2, partition=partitions)
-    launcher.bind_(solver='compression/debug')
-
-    with launcher.job_array():
-        # base debug task using config from solver=compression/debug
-        launcher()
-        # we can override parameters in the grid to launch additional xps
-        launcher({'rvq.bins': 2048, 'rvq.n_q': 4})

audiocraft/grids/compression/encodec_audiogen_16khz.py

@@ -1,29 +0,0 @@
-# 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.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid shows how to train the new AudioGen EnCodec model at 16 kHz.
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=8, partition=partitions)
-    # use configuration for AudioGen's EnCodec model trained on monophonic audio sampled at 16 kHz
-    # AudioGen's EnCodec is trained with a total stride of 320 leading to a frame rate of 50 hz
-    launcher.bind_(solver='compression/encodec_audiogen_16khz')
-    # replace this by the desired sound dataset
-    launcher.bind_(dset='internal/sounds_16khz')
-    # launch xp
-    launcher()

audiocraft/grids/compression/encodec_base_24khz.py

@@ -1,28 +0,0 @@
-# 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.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid shows how to train a base causal EnCodec model at 24 kHz.
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=8, partition=partitions)
-    # base causal EnCodec trained on monophonic audio sampled at 24 kHz
-    launcher.bind_(solver='compression/encodec_base_24khz')
-    # replace this by the desired dataset
-    launcher.bind_(dset='audio/example')
-    # launch xp
-    launcher()

audiocraft/grids/compression/encodec_musicgen_32khz.py

@@ -1,34 +0,0 @@
-# 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.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid shows how to train a MusicGen EnCodec model at 32 kHz.
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=8, partition=partitions)
-    # use configuration for MusicGen's EnCodec model trained on monophonic audio sampled at 32 kHz
-    # MusicGen's EnCodec is trained with a total stride of 640 leading to a frame rate of 50 hz
-    launcher.bind_(solver='compression/encodec_musicgen_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-    # launch xp
-    launcher()
-    launcher({
-        'metrics.visqol.bin': '/data/home/jadecopet/local/usr/opt/visqol',
-        'label': 'visqol',
-        'evaluate.metrics.visqol': True
-    })

audiocraft/grids/diffusion/4_bands_base_32khz.py

@@ -1,27 +0,0 @@
-# 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.
-
-"""
-Training of the 4 diffusion models described in
-"From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion"
-(paper link).
-"""
-
-from ._explorers import DiffusionExplorer
-
-
-@DiffusionExplorer
-def explorer(launcher):
-    launcher.slurm_(gpus=4, partition='learnfair')
-
-    launcher.bind_({'solver': 'diffusion/default',
-                    'dset': 'internal/music_10k_32khz'})
-
-    with launcher.job_array():
-        launcher({'filter.use': True, 'filter.idx_band': 0, "processor.use": False, 'processor.power_std': 0.4})
-        launcher({'filter.use': True, 'filter.idx_band': 1, "processor.use": False, 'processor.power_std': 0.4})
-        launcher({'filter.use': True, 'filter.idx_band': 2, "processor.use": True, 'processor.power_std': 0.4})
-        launcher({'filter.use': True, 'filter.idx_band': 3, "processor.use": True, 'processor.power_std': 0.75})

audiocraft/grids/diffusion/__init__.py

@@ -1,6 +0,0 @@
-# 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.
-"""Diffusion grids."""

audiocraft/grids/diffusion/_explorers.py

@@ -1,66 +0,0 @@
-# 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 treetable as tt
-
-from .._base_explorers import BaseExplorer
-
-
-class DiffusionExplorer(BaseExplorer):
-    eval_metrics = ["sisnr", "visqol"]
-
-    def stages(self):
-        return ["train", "valid", "valid_ema", "evaluate", "evaluate_ema"]
-
-    def get_grid_meta(self):
-        """Returns the list of Meta information to display for each XP/job.
-        """
-        return [
-            tt.leaf("index", align=">"),
-            tt.leaf("name", wrap=140),
-            tt.leaf("state"),
-            tt.leaf("sig", align=">"),
-        ]
-
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table.
-        """
-        return [
-            tt.group(
-                "train",
-                [
-                    tt.leaf("epoch"),
-                    tt.leaf("loss", ".3%"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "valid",
-                [
-                    tt.leaf("loss", ".3%"),
-                    # tt.leaf("loss_0", ".3%"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "valid_ema",
-                [
-                    tt.leaf("loss", ".3%"),
-                    # tt.leaf("loss_0", ".3%"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "evaluate", [tt.leaf("rvm", ".4f"), tt.leaf("rvm_0", ".4f"),
-                             tt.leaf("rvm_1", ".4f"), tt.leaf("rvm_2", ".4f"),
-                             tt.leaf("rvm_3", ".4f"), ], align=">"
-            ),
-            tt.group(
-                "evaluate_ema", [tt.leaf("rvm", ".4f"), tt.leaf("rvm_0", ".4f"),
-                                 tt.leaf("rvm_1", ".4f"), tt.leaf("rvm_2", ".4f"),
-                                 tt.leaf("rvm_3", ".4f")], align=">"
-            ),
-        ]

audiocraft/grids/musicgen/__init__.py

@@ -1,6 +0,0 @@
-# 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.
-"""MusicGen grids."""

audiocraft/grids/musicgen/_explorers.py

@@ -1,93 +0,0 @@
-# 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 treetable as tt
-
-from .._base_explorers import BaseExplorer
-
-
-class LMExplorer(BaseExplorer):
-    eval_metrics: tp.List[str] = []
-
-    def stages(self) -> tp.List[str]:
-        return ['train', 'valid']
-
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table."""
-        return [
-            tt.group(
-                'train',
-                [
-                    tt.leaf('epoch'),
-                    tt.leaf('duration', '.1f'),  # duration in minutes
-                    tt.leaf('ping'),
-                    tt.leaf('ce', '.4f'),  # cross entropy
-                    tt.leaf("ppl", '.3f'),  # perplexity
-                ],
-                align='>',
-            ),
-            tt.group(
-                'valid',
-                [
-                    tt.leaf('ce', '.4f'),
-                    tt.leaf('ppl', '.3f'),
-                    tt.leaf('best_ppl', '.3f'),
-                ],
-                align='>',
-            ),
-        ]
-
-    def process_sheep(self, sheep, history):
-        parts = super().process_sheep(sheep, history)
-
-        track_by = {'ppl': 'lower'}  # values should be in ['lower', 'higher']
-        best_metrics = {k: (1 if v == 'lower' else -1) * float('inf') for k, v in track_by.items()}
-
-        def comparator(mode, a, b):
-            return a < b if mode == 'lower' else a > b
-
-        for metrics in history:
-            for key, sub in metrics.items():
-                for metric in track_by:
-                    # for the validation set, keep track of best metrics (ppl in this example)
-                    # this is so we can conveniently compare metrics between runs in the grid
-                    if key == 'valid' and metric in sub and comparator(
-                        track_by[metric], sub[metric], best_metrics[metric]
-                    ):
-                        best_metrics[metric] = sub[metric]
-
-        if 'valid' in parts:
-            parts['valid'].update({f'best_{k}': v for k, v in best_metrics.items()})
-        return parts
-
-
-class GenerationEvalExplorer(BaseExplorer):
-    eval_metrics: tp.List[str] = []
-
-    def stages(self) -> tp.List[str]:
-        return ['evaluate']
-
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table."""
-        return [
-            tt.group(
-                'evaluate',
-                [
-                    tt.leaf('epoch', '.3f'),
-                    tt.leaf('duration', '.1f'),
-                    tt.leaf('ping'),
-                    tt.leaf('ce', '.4f'),
-                    tt.leaf('ppl', '.3f'),
-                    tt.leaf('fad', '.3f'),
-                    tt.leaf('kld', '.3f'),
-                    tt.leaf('text_consistency', '.3f'),
-                    tt.leaf('chroma_cosine', '.3f'),
-                ],
-                align='>',
-            ),
-        ]

audiocraft/grids/musicgen/musicgen_base_32khz.py

@@ -1,43 +0,0 @@
-# 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 ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-    large = {'model/lm/model_scale': 'large'}
-
-    cfg_low = {'classifier_free_guidance.training_dropout': 0.2}
-    wd_low = {'conditioners.description.t5.word_dropout': 0.2}
-
-    adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
-
-    launcher.bind_(fsdp)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub()
-
-    launcher.slurm_(gpus=64).bind_(label='64gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(medium, adam)
-
-    launcher.slurm_(gpus=96).bind_(label='96gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3})

audiocraft/grids/musicgen/musicgen_base_cached_32khz.py

@@ -1,67 +0,0 @@
-# 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 ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-    large = {'model/lm/model_scale': 'large'}
-
-    cfg_low = {'classifier_free_guidance.training_dropout': 0.2}
-    wd_low = {'conditioners.description.t5.word_dropout': 0.2}
-
-    adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
-
-    # BEGINNING OF CACHE WRITING JOBS.
-    cache_write = {
-        'cache.path': '/fsx-codegen/defossez/cache/interleave_stereo_nv_32k',
-        'cache.write': True,
-        'generate.every': 500,
-        'evaluate.every': 500,
-        'logging.log_updates': 50,
-    }
-
-    cache_sub = launcher.bind({'model/lm/model_scale': 'xsmall', 'conditioner': 'none'})
-    cache_sub.bind_({'deadlock.use': True})
-    cache_sub.slurm_(gpus=8)
-    with launcher.job_array():
-        num_shards = 10  # total number of jobs running in parallel.
-        for shard in range(0, num_shards):
-            launcher(cache_write, {'cache.write_num_shards': num_shards, 'cache.write_shard': shard})
-
-    # REMOVE THE FOLLOWING RETURN STATEMENT ONCE THE ABOVE JOBS ARE DONE,
-    # OR SUFFICIENTLY AHEAD.
-    return
-
-    cache = {
-        'cache.path': '/fsx-codegen/defossez/cache/interleave_stereo_nv_32k',
-    }
-    launcher.bind_(fsdp, cache)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub()
-
-    launcher.slurm_(gpus=64).bind_(label='64gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(medium, adam)
-
-    launcher.slurm_(gpus=96).bind_(label='96gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3})