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

.github/actions/audiocraft_build/action.yml

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

.github/workflows/audiocraft_docs.yml

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

.github/workflows/audiocraft_tests.yml

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

.gitignore

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

CHANGELOG.md

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

CONTRIBUTING.md

@@ -1,11 +1,11 @@
-# Contributing to AudioCraft
+# Contributing to Audiocraft
 
 We want to make contributing to this project as easy and transparent as
 possible.
 
 ## Pull Requests
 
-AudioCraft is the implementation of a research paper.
+Audiocraft is the implementation of a research paper.
 Therefore, we do not plan on accepting many pull requests for new features.
 We certainly welcome them for bug fixes.
 

LICENSE_weights

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

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

model_cards/MUSICGEN_MODEL_CARD.md → MODEL_CARD.md

@@ -12,11 +12,11 @@
 
 **Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][arxiv].
 
-**Citation details:** See [our paper][arxiv]
+**Citation details** See [our paper][arxiv]
 
-**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
+**License** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
 
-**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [GitHub repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
+**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
 
 ## Intended use
 **Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
@@ -26,7 +26,7 @@
 
 **Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
 
-**Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
+**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
 
 ## Metrics
 
@@ -54,24 +54,15 @@ The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/data
 
 The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound),  [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
 
-## Evaluation results
+## Quantitative analysis
 
-Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper.
-
-| Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity |
-|---|---|---|---|---|
-| facebook/musicgen-small  | 4.88 | 1.42 | 0.27 | - |
-| facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - |
-| facebook/musicgen-large  | 5.48 | 1.37 | 0.28 | - |
-| facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 |
-
-More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Results section.
+More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section.
 
 ## Limitations and biases
 
 **Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
 
-**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
+**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
 
 **Limitations:**
 
@@ -87,19 +78,4 @@ More information can be found in the paper [Simple and Controllable Music Genera
 
 **Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
 
-## Update: stereo models and large melody.
-
-We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting
-from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using
-the delay pattern. We also release a mono large model with melody conditioning capabilities. The list of new models
-is as follow:
-
-- facebook/musicgen-stereo-small
-- facebook/musicgen-stereo-medium
-- facebook/musicgen-stereo-large
-- facebook/musicgen-stereo-melody
-- facebook/musicgen-melody-large
-- facebook/musicgen-stereo-melody-large
-
-
 [arxiv]: https://arxiv.org/abs/2306.05284

Makefile

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

README.md

@@ -5,27 +5,42 @@ tags:
   - "music generation"
   - "language models"
   - "LLMs"
-app_file: "demos/musicgen_app.py"
+app_file: "app_batched.py"
 emoji: 🎵
-colorFrom: gray
+colorFrom: white
 colorTo: blue
 sdk: gradio
 sdk_version: 3.34.0
 pinned: true
 license: "cc-by-nc-4.0"
-disable_embedding: true
 ---
-# AudioCraft
+# Audiocraft
 ![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
 ![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
 ![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)
 
-AudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code
-for two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.
+Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
 
+## MusicGen
+
+Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
+Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates
+all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
+them in parallel, thus having only 50 auto-regressive steps per second of audio.
+Check out our [sample page][musicgen_samples] or test the available demo!
+
+<a target="_blank" href="https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
+  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
+</a>
+<br>
+
+We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.
 
 ## Installation
-AudioCraft requires Python 3.9, PyTorch 2.0.0. To install AudioCraft, you can run the following:
+Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
 
 ```shell
 # Best to make sure you have torch installed first, in particular before installing xformers.
@@ -34,68 +49,87 @@ pip install 'torch>=2.0'
 # Then proceed to one of the following
 pip install -U audiocraft  # stable release
 pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge
-pip install -e .  # or if you cloned the repo locally (mandatory if you want to train).
+pip install -e .  # or if you cloned the repo locally
 ```
 
-We also recommend having `ffmpeg` installed, either through your system or Anaconda:
-```bash
-sudo apt-get install ffmpeg
-# Or if you are using Anaconda or Miniconda
-conda install "ffmpeg<5" -c conda-forge
-```
+## Usage
+We offer a number of way to interact with MusicGen:
+1. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally, or use the provided [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing).
+2. You can use the gradio demo locally by running `python app.py`.
+3. A demo is also available on the [`facebook/MusicGen`  HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
+4. Finally, you can run the [Gradio demo with a Colab GPU](https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing),
+as adapted from [@camenduru Colab](https://github.com/camenduru/MusicGen-colab).
 
-## Models
+## API
 
-At the moment, AudioCraft contains the training code and inference code for:
-* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model.
-* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model.
-* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec.
-* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion.
+We provide a simple API and 4 pre-trained models. The pre trained models are:
+- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
+- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
+- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
+- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
 
-## Training code
+We observe the best trade-off between quality and compute with the `medium` or `melody` model.
+In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
+GPUs will be able to generate short sequences, or longer sequences with the `small` model.
 
-AudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models.
-For a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to
-the [AudioCraft training documentation](./docs/TRAINING.md).
+**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
+You can install it with:
+```
+apt-get install ffmpeg
+```
 
-For reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model
-that provides pointers to configuration, example grids and model/task-specific information and FAQ.
+See after a quick example for using the API.
 
+```python
+import torchaudio
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
 
-## API documentation
+model = MusicGen.get_pretrained('melody')
+model.set_generation_params(duration=8)  # generate 8 seconds.
+wav = model.generate_unconditional(4)    # generates 4 unconditional audio samples
+descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
+wav = model.generate(descriptions)  # generates 3 samples.
 
-We provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft.
+melody, sr = torchaudio.load('./assets/bach.mp3')
+# generates using the melody from the given audio and the provided descriptions.
+wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)
 
+for idx, one_wav in enumerate(wav):
+    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
+    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
+```
 
-## FAQ
 
-#### Is the training code available?
+## Model Card
 
-Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md) and [Multi Band Diffusion](./docs/MBD.md).
+See [the model card page](./MODEL_CARD.md).
 
-#### Where are the models stored?
+## FAQ
 
-Hugging Face stored the model in a specific location, which can be overriden by setting the `AUDIOCRAFT_CACHE_DIR` environment variable for the AudioCraft models.
-In order to change the cache location of the other Hugging Face models, please check out the [Hugging Face Transformers documentation for the cache setup](https://huggingface.co/docs/transformers/installation#cache-setup).
-Finally, if you use a model that relies on Demucs (e.g. `musicgen-melody`) and want to change the download location for Demucs, refer to the [Torch Hub documentation](https://pytorch.org/docs/stable/hub.html#where-are-my-downloaded-models-saved).
+#### Will the training code be released?
 
+Yes. We will soon release the training code for MusicGen and EnCodec.
 
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The models weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
 
+#### I need help on Windows
+
+@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
 
-## Citation
 
-For the general framework of AudioCraft, please cite the following.
+## Citation
 ```
 @article{copet2023simple,
-    title={Simple and Controllable Music Generation},
-    author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
-    year={2023},
-    journal={arXiv preprint arXiv:2306.05284},
+      title={Simple and Controllable Music Generation},
+      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
+      year={2023},
+      journal={arXiv preprint arXiv:2306.05284},
 }
 ```
 
-When referring to a specific model, please cite as mentioned in the model specific README, e.g
-[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc.
+## License
+* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
+* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
+
+[arxiv]: https://arxiv.org/abs/2306.05284
+[musicgen_samples]: https://ai.honu.io/papers/musicgen/

app.py

@@ -0,0 +1,216 @@
+"""
+Copyright (c) Meta Platforms, Inc. and affiliates.
+All rights reserved.
+
+This source code is licensed under the license found in the
+LICENSE file in the root directory of this source tree.
+"""
+
+from tempfile import NamedTemporaryFile
+import argparse
+import torch
+import gradio as gr
+import os
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
+
+MODEL = None
+IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ.get('SPACE_ID', '')
+
+
+def load_model(version):
+    print("Loading model", version)
+    return MusicGen.get_pretrained(version)
+
+
+def predict(model, text, melody, duration, topk, topp, temperature, cfg_coef):
+    global MODEL
+    topk = int(topk)
+    if MODEL is None or MODEL.name != model:
+        MODEL = load_model(model)
+
+    if duration > MODEL.lm.cfg.dataset.segment_duration:
+        raise gr.Error("MusicGen currently supports durations of up to 30 seconds!")
+    MODEL.set_generation_params(
+        use_sampling=True,
+        top_k=topk,
+        top_p=topp,
+        temperature=temperature,
+        cfg_coef=cfg_coef,
+        duration=duration,
+    )
+
+    if melody:
+        sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
+        print(melody.shape)
+        if melody.dim() == 2:
+            melody = melody[None]
+        melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
+        output = MODEL.generate_with_chroma(
+            descriptions=[text],
+            melody_wavs=melody,
+            melody_sample_rate=sr,
+            progress=False
+        )
+    else:
+        output = MODEL.generate(descriptions=[text], progress=False)
+
+    output = output.detach().cpu().float()[0]
+    with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
+        audio_write(
+            file.name, output, MODEL.sample_rate, strategy="loudness",
+            loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
+        waveform_video = gr.make_waveform(file.name)
+    return waveform_video
+
+
+def ui(**kwargs):
+    with gr.Blocks() as interface:
+        gr.Markdown(
+            """
+            # MusicGen
+            This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
+            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
+            """
+        )
+        if IS_SHARED_SPACE:
+            gr.Markdown("""
+                ⚠ This Space doesn't work in this shared UI ⚠
+
+                <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
+                <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+                to use it privately, or use the <a href="https://huggingface.co/spaces/facebook/MusicGen">public demo</a>
+                """)
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    text = gr.Text(label="Input Text", interactive=True)
+                    melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                with gr.Row():
+                    model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
+                with gr.Row():
+                    duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
+                with gr.Row():
+                    topk = gr.Number(label="Top-k", value=250, interactive=True)
+                    topp = gr.Number(label="Top-p", value=0, interactive=True)
+                    temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
+                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
+            with gr.Column():
+                output = gr.Video(label="Generated Music")
+        submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
+        gr.Examples(
+            fn=predict,
+            examples=[
+                [
+                    "An 80s driving pop song with heavy drums and synth pads in the background",
+                    "./assets/bach.mp3",
+                    "melody"
+                ],
+                [
+                    "A cheerful country song with acoustic guitars",
+                    "./assets/bolero_ravel.mp3",
+                    "melody"
+                ],
+                [
+                    "90s rock song with electric guitar and heavy drums",
+                    None,
+                    "medium"
+                ],
+                [
+                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
+                    "./assets/bach.mp3",
+                    "melody"
+                ],
+                [
+                    "lofi slow bpm electro chill with organic samples",
+                    None,
+                    "medium",
+                ],
+            ],
+            inputs=[text, melody, model],
+            outputs=[output]
+        )
+        gr.Markdown(
+            """
+            ### More details
+
+            The model will generate a short music extract based on the description you provided.
+            You can generate up to 30 seconds of audio.
+
+            We present 4 model variations:
+            1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
+            2. Small -- a 300M transformer decoder conditioned on text only.
+            3. Medium -- a 1.5B transformer decoder conditioned on text only.
+            4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
+
+            When using `melody`, ou can optionaly provide a reference audio from
+            which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
+
+            You can also use your own GPU or a Google Colab by following the instructions on our repo.
+            See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
+            for more details.
+            """
+        )
+
+        # Show the interface
+        launch_kwargs = {}
+        username = kwargs.get('username')
+        password = kwargs.get('password')
+        server_port = kwargs.get('server_port', 0)
+        inbrowser = kwargs.get('inbrowser', False)
+        share = kwargs.get('share', False)
+        server_name = kwargs.get('listen')
+
+        launch_kwargs['server_name'] = server_name
+
+        if username and password:
+            launch_kwargs['auth'] = (username, password)
+        if server_port > 0:
+            launch_kwargs['server_port'] = server_port
+        if inbrowser:
+            launch_kwargs['inbrowser'] = inbrowser
+        if share:
+            launch_kwargs['share'] = share
+
+        interface.queue().launch(**launch_kwargs, max_threads=1)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--listen',
+        type=str,
+        default='0.0.0.0',
+        help='IP to listen on for connections to Gradio',
+    )
+    parser.add_argument(
+        '--username', type=str, default='', help='Username for authentication'
+    )
+    parser.add_argument(
+        '--password', type=str, default='', help='Password for authentication'
+    )
+    parser.add_argument(
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
+    )
+    parser.add_argument(
+        '--share', action='store_true', help='Share the gradio UI'
+    )
+
+    args = parser.parse_args()
+
+    ui(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        share=args.share,
+        listen=args.listen
+    )

app_batched.py

@@ -0,0 +1,222 @@
+"""
+Copyright (c) Meta Platforms, Inc. and affiliates.
+All rights reserved.
+
+This source code is licensed under the license found in the
+LICENSE file in the root directory of this source tree.
+"""
+
+import argparse
+from concurrent.futures import ProcessPoolExecutor
+import subprocess as sp
+from tempfile import NamedTemporaryFile
+import time
+import warnings
+import torch
+import gradio as gr
+from audiocraft.data.audio_utils import convert_audio
+from audiocraft.data.audio import audio_write
+from audiocraft.models import MusicGen
+
+
+MODEL = None
+
+_old_call = sp.call
+
+
+def _call_nostderr(*args, **kwargs):
+    # Avoid ffmpeg vomitting on the logs.
+    kwargs['stderr'] = sp.DEVNULL
+    kwargs['stdout'] = sp.DEVNULL
+    _old_call(*args, **kwargs)
+
+
+sp.call = _call_nostderr
+pool = ProcessPoolExecutor(3)
+pool.__enter__()
+
+
+def make_waveform(*args, **kwargs):
+    be = time.time()
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore')
+        out = gr.make_waveform(*args, **kwargs)
+        print("Make a video took", time.time() - be)
+        return out
+
+
+def load_model():
+    print("Loading model")
+    return MusicGen.get_pretrained("melody")
+
+
+def predict(texts, melodies):
+    global MODEL
+    if MODEL is None:
+        MODEL = load_model()
+
+    duration = 12
+    max_text_length = 512
+    texts = [text[:max_text_length] for text in texts]
+    MODEL.set_generation_params(duration=duration)
+
+    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
+    be = time.time()
+    processed_melodies = []
+    target_sr = 32000
+    target_ac = 1
+    for melody in melodies:
+        if melody is None:
+            processed_melodies.append(None)
+        else:
+            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
+            if melody.dim() == 1:
+                melody = melody[None]
+            melody = melody[..., :int(sr * duration)]
+            melody = convert_audio(melody, sr, target_sr, target_ac)
+            processed_melodies.append(melody)
+
+    outputs = MODEL.generate_with_chroma(
+        descriptions=texts,
+        melody_wavs=processed_melodies,
+        melody_sample_rate=target_sr,
+        progress=False
+    )
+
+    outputs = outputs.detach().cpu().float()
+    out_files = []
+    for output in outputs:
+        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
+            audio_write(
+                file.name, output, MODEL.sample_rate, strategy="loudness",
+                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
+            out_files.append(pool.submit(make_waveform, file.name))
+    res = [[out_file.result() for out_file in out_files]]
+    print("batch finished", len(texts), time.time() - be)
+    return res
+
+
+def ui(**kwargs):
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # MusicGen
+
+            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
+            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
+            <br/>
+            <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
+            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+            for longer sequences, more control and no queue.</p>
+            """
+        )
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
+                    melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
+                with gr.Row():
+                    submit = gr.Button("Generate")
+            with gr.Column():
+                output = gr.Video(label="Generated Music")
+        submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8)
+        gr.Examples(
+            fn=predict,
+            examples=[
+                [
+                    "An 80s driving pop song with heavy drums and synth pads in the background",
+                    "./assets/bach.mp3",
+                ],
+                [
+                    "A cheerful country song with acoustic guitars",
+                    "./assets/bolero_ravel.mp3",
+                ],
+                [
+                    "90s rock song with electric guitar and heavy drums",
+                    None,
+                ],
+                [
+                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
+                    "./assets/bach.mp3",
+                ],
+                [
+                    "lofi slow bpm electro chill with organic samples",
+                    None,
+                ],
+            ],
+            inputs=[text, melody],
+            outputs=[output]
+        )
+        gr.Markdown("""
+        ### More details
+
+        The model will generate 12 seconds of audio based on the description you provided.
+        You can optionaly provide a reference audio from which a broad melody will be extracted.
+        The model will then try to follow both the description and melody provided.
+        All samples are generated with the `melody` model.
+
+        You can also use your own GPU or a Google Colab by following the instructions on our repo.
+
+        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
+        for more details.
+        """)
+
+        # Show the interface
+        launch_kwargs = {}
+        username = kwargs.get('username')
+        password = kwargs.get('password')
+        server_port = kwargs.get('server_port', 0)
+        inbrowser = kwargs.get('inbrowser', False)
+        share = kwargs.get('share', False)
+        server_name = kwargs.get('listen')
+
+        launch_kwargs['server_name'] = server_name
+
+        if username and password:
+            launch_kwargs['auth'] = (username, password)
+        if server_port > 0:
+            launch_kwargs['server_port'] = server_port
+        if inbrowser:
+            launch_kwargs['inbrowser'] = inbrowser
+        if share:
+            launch_kwargs['share'] = share
+        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--listen',
+        type=str,
+        default='0.0.0.0',
+        help='IP to listen on for connections to Gradio',
+    )
+    parser.add_argument(
+        '--username', type=str, default='', help='Username for authentication'
+    )
+    parser.add_argument(
+        '--password', type=str, default='', help='Password for authentication'
+    )
+    parser.add_argument(
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
+    )
+    parser.add_argument(
+        '--share', action='store_true', help='Share the gradio UI'
+    )
+
+    args = parser.parse_args()
+
+    ui(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        share=args.share,
+        listen=args.listen
+    )

audiocraft/__init__.py

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

audiocraft/adversarial/__init__.py

@@ -1,22 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Adversarial losses and discriminator architectures."""
-
-# flake8: noqa
-from .discriminators import (
-    MultiPeriodDiscriminator,
-    MultiScaleDiscriminator,
-    MultiScaleSTFTDiscriminator
-)
-from .losses import (
-    AdversarialLoss,
-    AdvLossType,
-    get_adv_criterion,
-    get_fake_criterion,
-    get_real_criterion,
-    FeatLossType,
-    FeatureMatchingLoss