Stereo demo update (#60)

- updated demo (a16e65ea0e58cfa993cbd11f06f2ded8eafb559c)


Co-authored-by: Alexandre Défossez <[email protected]>

.github/actions/audiocraft_build/action.yml

@@ -21,6 +21,8 @@ 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 docs
-          git add -f docs
-          git commit -m docs
+          make api_docs
+          git add -f api_docs
+          git commit -m api_docs
 
       - name: Push branch
         run: |

.github/workflows/audiocraft_tests.yml

@@ -12,6 +12,11 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - uses: ./.github/actions/audiocraft_build
-      - run: |
+      - name: Run unit tests
+        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
-/docs
+/api_docs
 
 # dotenv
 .env
@@ -46,6 +46,13 @@ wheels/
 venv/
 ENV/
 
+# egs with manifest files
+egs/*
+!egs/example
+# local datasets
+dataset/*
+!dataset/example
+
 # personal notebooks & scripts
 */local_scripts
 */notes

CHANGELOG.md

@@ -4,7 +4,37 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
-## [0.0.2a] - TBD
+## [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
 
 Improved demo, fixed top p (thanks @jnordberg).
 

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

@@ -1,157 +1,399 @@
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MANIFEST.in

@@ -6,3 +6,10 @@ 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

Makefile

@@ -1,3 +1,15 @@
+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:
@@ -10,12 +22,19 @@ linter:
 
 tests:
 	coverage run -m pytest tests
-	coverage report --include 'audiocraft/*'
+	coverage report
+
+tests_integ:
+	$(INTEG_COMPRESSION)
+	$(INTEG_MBD)
+	$(INTEG_MUSICGEN)
+	$(INTEG_AUDIOGEN)
+
 
-docs:
-	pdoc3 --html -o docs -f audiocraft
+api_docs:
+	pdoc3 --html -o api_docs -f audiocraft
 
 dist:
 	python setup.py sdist
 
-.PHONY: linter tests docs dist
+.PHONY: linter tests api_docs dist

README.md

@@ -5,7 +5,7 @@ tags:
   - "music generation"
   - "language models"
   - "LLMs"
-app_file: "app.py"
+app_file: "demos/musicgen_app.py"
 emoji: 🎵
 colorFrom: gray
 colorTo: blue
@@ -14,33 +14,17 @@ sdk_version: 3.34.0
 pinned: true
 license: "cc-by-nc-4.0"
 ---
-# 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. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
+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.
 
-## 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, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
+AudioCraft requires Python 3.9, PyTorch 2.0.0. To install AudioCraft, you can run the following:
 
 ```shell
 # Best to make sure you have torch installed first, in particular before installing xformers.
@@ -49,92 +33,68 @@ 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
+pip install -e .  # or if you cloned the repo locally (mandatory if you want to train).
 ```
 
-## Usage
-We offer a number of way to interact with MusicGen:
-1. 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).
-2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing).
-3. You can use the gradio demo locally by running `python app.py`.
-4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU).
-5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly
-  updated with contributions from @camenduru and the community.
-
-## API
-
-We provide a simple API and 4 pre-trained models. The pre trained models are:
-- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
-- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
-- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
-- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
-
-We observe the best trade-off between quality and compute with the `medium` or `melody` model.
-In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
-GPUs will be able to generate short sequences, or longer sequences with the `small` model.
-
-**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
-You can install it with:
-```
-apt-get install ffmpeg
+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
 ```
 
-See after a quick example for using the API.
+## Models
 
-```python
-import torchaudio
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
+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.
 
-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.
+## Training code
 
-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)
+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).
 
-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)
-```
+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.
 
 
-## Model Card
+## API documentation
 
-See [the model card page](./MODEL_CARD.md).
+We provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft.
 
-## FAQ
 
-#### Will the training code be released?
+## FAQ
 
-Yes. We will soon release the training code for MusicGen and EnCodec.
+#### Is the training code available?
 
+Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md) and [Multi Band Diffusion](./docs/MBD.md).
 
-#### I need help on Windows
+#### Where are the models stored?
 
-@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
+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).
 
-#### I need help for running the demo on Colab
 
-Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo).
+## 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).
 
 
 ## Citation
+
+For the general framework of AudioCraft, please cite the following.
 ```
 @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},
 }
 ```
 
-## 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/
+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.

audiocraft/__init__.py

@@ -3,8 +3,24 @@
 #
 # 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__ = '0.0.2a2'
+__version__ = '1.1.0'

audiocraft/adversarial/__init__.py

@@ -0,0 +1,22 @@
+# 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

@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# flake8: noqa
+from .mpd import MultiPeriodDiscriminator
+from .msd import MultiScaleDiscriminator
+from .msstftd import MultiScaleSTFTDiscriminator

audiocraft/adversarial/discriminators/base.py

@@ -0,0 +1,34 @@
+# 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

@@ -0,0 +1,106 @@
+# 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

@@ -0,0 +1,126 @@
+# 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

@@ -0,0 +1,134 @@
+# 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

@@ -0,0 +1,228 @@
+# 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

@@ -3,6 +3,8 @@
 #
 # 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
+from . import audio, audio_dataset, info_audio_dataset, music_dataset, sound_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, i16_pcm, normalize_audio
+from .audio_utils import f32_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[torch.Tensor, int]: Tuple containing audio data and sample rate
+        tuple of 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[torch.Tensor, int]: Tuple containing audio data and sample rate.
+        tuple of 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
@@ -136,12 +136,6 @@ 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:
@@ -150,10 +144,22 @@ 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, normalize: bool = True,
-                strategy: str = 'peak', peak_clip_headroom_db: float = 1,
+                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,
                 rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
                 loudness_compressor: bool = False,
                 log_clipping: bool = True, make_parent_dir: bool = True,
@@ -162,8 +168,11 @@ def audio_write(stem_name: tp.Union[str, Path],
 
     Args:
         stem_name (str or Path): Filename without extension which will be added automatically.
-        format (str): Either "wav" or "mp3".
+        wav (torch.Tensor): Audio data to save.
+        sample_rate (int): Sample rate of audio data.
+        format (str): Either "wav", "mp3", "ogg", or "flac".
         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.
@@ -175,7 +184,7 @@ 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:
@@ -188,16 +197,23 @@ 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, log_clipping=log_clipping,
-                          sample_rate=sample_rate, stem_name=str(stem_name))
-    kwargs: dict = {}
+                          rms_headroom_db, loudness_headroom_db, loudness_compressor,
+                          log_clipping=log_clipping, sample_rate=sample_rate,
+                          stem_name=str(stem_name))
     if format == 'mp3':
         suffix = '.mp3'
-        kwargs.update({"compression": mp3_rate})
+        flags = ['-f', 'mp3', '-c:a', 'libmp3lame', '-b:a', f'{mp3_rate}k']
     elif format == 'wav':
-        wav = i16_pcm(wav)
         suffix = '.wav'
-        kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16})
+        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']
     else:
         raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.")
     if not add_suffix:
@@ -206,7 +222,7 @@ def audio_write(stem_name: tp.Union[str, Path],
     if make_parent_dir:
         path.parent.mkdir(exist_ok=True, parents=True)
     try:
-        ta.save(path, wav, sample_rate, **kwargs)
+        _piping_to_ffmpeg(path, wav, sample_rate, flags)
     except Exception:
         if path.exists():
             # we do not want to leave half written files around.

audiocraft/data/audio_dataset.py

@@ -3,12 +3,16 @@
 #
 # 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
@@ -81,9 +85,12 @@ class AudioMeta(BaseInfo):
 class SegmentInfo(BaseInfo):
     meta: AudioMeta
     seek_time: float
-    n_frames: int  # actual number of frames without padding
+    # 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
     total_frames: int  # total number of frames, padding included
-    sample_rate: int  # actual sample rate
+    sample_rate: int   # actual sample rate
+    channels: int      # number of audio channels.
 
 
 DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a']
@@ -114,8 +121,8 @@ 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.
     """
@@ -151,7 +158,7 @@ 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[AudioMeta]: List of audio file path and its metadata.
+        list of AudioMeta: List of audio file path and its metadata.
     """
     audio_files = []
     futures: tp.List[Future] = []
@@ -203,7 +210,7 @@ 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[AudioMeta]: List of audio file path and its total duration.
+        list of 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
@@ -250,9 +257,14 @@ 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 (tp.List[AudioMeta]): List of audio files metadata.
-        segment_duration (float): Optional segment duration of audio to load.
+        meta (list of AudioMeta): List of audio files metadata.
+        segment_duration (float, optional): 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.
@@ -266,10 +278,19 @@ 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 (tp.Optional[float], optional): Minimum audio file duration, in seconds, if provided
+        min_audio_duration (float, optional): Minimum audio file duration, in seconds, if provided
             audio shorter than this will be filtered out.
-        max_audio_duration (tp.Optional[float], optional): Maximal audio file duration in seconds, if provided
+        max_audio_duration (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],
@@ -285,16 +306,14 @@ 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
+                 max_audio_duration: tp.Optional[float] = None,
+                 shuffle_seed: int = 0,
+                 load_wav: bool = True,
+                 permutation_on_files: bool = False,
                  ):
-        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
@@ -317,13 +336,25 @@ 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.
@@ -337,12 +368,32 @@ class AudioDataset:
             probabilities /= probabilities.sum()
         return probabilities
 
-    def sample_file(self, rng: torch.Generator) -> AudioMeta:
-        """Sample a given file from `self.meta`. Can be overriden in subclasses.
+    @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.
         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:
@@ -350,6 +401,15 @@ 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]
@@ -357,18 +417,22 @@ 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)
+                                       sample_rate=self.sample_rate, channels=out.shape[0])
         else:
             rng = torch.Generator()
             if self.shuffle:
-                # We use index, plus extra randomness
-                rng.manual_seed(index + self.num_samples * random.randint(0, 2**24))
+                # 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))
             else:
                 # We only use index
                 rng.manual_seed(index)
 
             for retry in range(self.max_read_retry):
-                file_meta = self.sample_file(rng)
+                file_meta = self.sample_file(index, 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)
@@ -381,7 +445,7 @@ 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)
+                                               sample_rate=self.sample_rate, channels=out.shape[0])
                 except Exception as exc:
                     logger.warning("Error opening file %s: %r", file_meta.path, exc)
                     if retry == self.max_read_retry - 1:
@@ -423,7 +487,7 @@ 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 legth of the signal with padding, so we update here as we pad.
+                    # Determines the total length 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])
 
@@ -436,9 +500,7 @@ class AudioDataset:
             return torch.stack(samples)
 
     def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]:
-        """Filters out audio files with short durations.
-        Removes from meta files that have durations that will not allow to samples examples from them.
-        """
+        """Filters out audio files with audio durations that will not allow to sample examples from them."""
         orig_len = len(meta)
 
         # Filter data that is too short.

audiocraft/data/audio_utils.py

@@ -3,7 +3,8 @@
 #
 # 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
 
@@ -47,8 +48,7 @@ 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:
-        output (torch.Tensor): Loudness normalized output data.
+        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 (Optional[str]): Stem name for clipping logging.
+        stem_name (str, optional): Stem name for clipping logging.
     Returns:
         torch.Tensor: Normalized audio.
     """
@@ -150,17 +150,19 @@ def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
     """
     if wav.dtype.is_floating_point:
         return wav
-    else:
-        assert wav.dtype == torch.int16
+    elif 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 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,
+    ..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,
     it is possible that `i16_pcm(f32_pcm)) != Identity`.
     """
     if wav.dtype.is_floating_point:

audiocraft/data/info_audio_dataset.py

@@ -0,0 +1,110 @@
+# 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

@@ -0,0 +1,270 @@
+# 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

@@ -0,0 +1,330 @@
+# 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

@@ -3,6 +3,8 @@
 #
 # 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
@@ -18,13 +20,13 @@ MODE = Literal['r', 'w', 'x', 'a']
 
 @dataclass(order=True)
 class PathInZip:
-    """Class for holding a path of file within a zip file.
+    """Hold a path of file within a zip file.
 
     Args:
-        path: The convention is <path_to_zip>:<relative_path_inside_zip>
+        path (str): 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 = ':'
@@ -55,7 +57,7 @@ def set_zip_cache_size(max_size: int):
     """Sets the maximal LRU caching for zip file opening.
 
     Args:
-        max_size: the maximal LRU cache.
+        max_size (int): the maximal LRU cache.
     """
     global _cached_open_zip
     _cached_open_zip = lru_cache(max_size)(_open_zip)
@@ -65,8 +67,8 @@ 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: A PathInZip object representing the file to return a file-like object of.
-        mode: The mode in which to open the file with.
+        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.
     Returns:
         A file-like object for PathInZip.
     """

audiocraft/environment.py

@@ -0,0 +1,176 @@
+# 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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,80 @@
+# 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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,23 @@
+# 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

@@ -0,0 +1,68 @@
+# 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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,55 @@
+# 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

@@ -0,0 +1,31 @@
+# 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

@@ -0,0 +1,29 @@
+# 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

@@ -0,0 +1,28 @@
+# 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

@@ -0,0 +1,34 @@
+# 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

@@ -0,0 +1,27 @@
+# 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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,66 @@
+# 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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,93 @@
+# 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

@@ -0,0 +1,43 @@
+# 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

@@ -0,0 +1,67 @@
+# 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})

audiocraft/grids/musicgen/musicgen_clapemb_32khz.py

@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+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')
+    launcher.bind_(conditioner='clapemb2music')
+
+    fsdp = {'autocast': False, 'fsdp.use': True}
+    cache_path = {'conditioners.description.clap.cache_path':
+                  '/fsx-audio-craft-llm/jadecopet/experiments/audiocraft/caches/clap_embed_music'}
+    text_wav_training_opt = {'conditioners.description.clap.text_p': 0.5}
+
+    launcher.bind_(fsdp)
+
+    launcher.slurm_(gpus=32).bind_(label='32gpus')
+    with launcher.job_array():
+        launcher()
+        launcher(text_wav_training_opt)
+        launcher(cache_path)
+        launcher(cache_path, text_wav_training_opt)

audiocraft/grids/musicgen/musicgen_melody_32khz.py

@@ -0,0 +1,65 @@
+# 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_melody_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}
+
+    cache_path = {'conditioners.self_wav.chroma_stem.cache_path':
+                  '/fsx-audio-craft-llm/jadecopet/experiments/audiocraft/caches/chroma_stem'}
+
+    # CACHE GENERATION JOBS
+    n_cache_gen_jobs = 4
+    gen_sub = launcher.slurm(gpus=1)
+    gen_sub.bind_(
+        cache_path, {
+            # the cache is always computed over the whole file, so duration doesn't matter here.
+            'dataset.segment_duration': 2.,
+            'dataset.batch_size': 8,
+            'dataset.train.permutation_on_files': True,  # try to not repeat files.
+            'optim.epochs': 10,
+            'model/lm/model_scale': 'xsmall',
+
+        })
+    with gen_sub.job_array():
+        for gen_job in range(n_cache_gen_jobs):
+            gen_sub({'dataset.train.shuffle_seed': gen_job})
+
+    # ACTUAL TRAINING JOBS.
+    launcher.bind_(fsdp)
+
+    launcher.slurm_(gpus=32).bind_(label='32gpus')
+    with launcher.job_array():
+        sub = launcher.bind()
+        sub()
+        sub(cache_path)
+
+    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_pretrained_32khz_eval.py

@@ -0,0 +1,99 @@
+# 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 MusicGen 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 musicgen.musicgen_pretrained_32khz_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 ._explorers import GenerationEvalExplorer
+from ...environment import AudioCraftEnvironment
+from ... import train
+
+
+def eval(launcher, batch_size: int = 32, eval_melody: bool = False):
+    opts = {
+        'dset': 'audio/musiccaps_32khz',
+        'solver/musicgen/evaluation': 'objective_eval',
+        'execute_only': 'evaluate',
+        '+dataset.evaluate.batch_size': batch_size,
+        '+metrics.fad.tf.batch_size': 16,
+    }
+    # chroma-specific evaluation
+    chroma_opts = {
+        'dset': 'internal/music_400k_32khz',
+        'dataset.evaluate.segment_duration': 30,
+        'dataset.evaluate.num_samples': 1000,
+        'evaluate.metrics.chroma_cosine': True,
+        'evaluate.metrics.fad': False,
+        'evaluate.metrics.kld': False,
+        'evaluate.metrics.text_consistency': False,
+    }
+    # 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)
+
+    if eval_melody:
+        # chroma-specific metrics
+        sub(opt1, opt2, chroma_opts)
+
+
+@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
+
+    with launcher.job_array():
+        musicgen_base = launcher.bind(solver="musicgen/musicgen_base_32khz")
+        musicgen_base.bind_({'autocast': False, 'fsdp.use': True})
+
+        # base musicgen models
+        musicgen_base_small = musicgen_base.bind({'continue_from': '//pretrained/facebook/musicgen-small'})
+        eval(musicgen_base_small, batch_size=128)
+
+        musicgen_base_medium = musicgen_base.bind({'continue_from': '//pretrained/facebook/musicgen-medium'})
+        musicgen_base_medium.bind_({'model/lm/model_scale': 'medium'})
+        eval(musicgen_base_medium, batch_size=128)
+
+        musicgen_base_large = musicgen_base.bind({'continue_from': '//pretrained/facebook/musicgen-large'})
+        musicgen_base_large.bind_({'model/lm/model_scale': 'large'})
+        eval(musicgen_base_large, batch_size=128)
+
+        # melody musicgen model
+        musicgen_melody = launcher.bind(solver="musicgen/musicgen_melody_32khz")
+        musicgen_melody.bind_({'autocast': False, 'fsdp.use': True})
+
+        musicgen_melody_medium = musicgen_melody.bind({'continue_from': '//pretrained/facebook/musicgen-melody'})
+        musicgen_melody_medium.bind_({'model/lm/model_scale': 'medium'})
+        eval(musicgen_melody_medium, batch_size=128, eval_melody=True)