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.gitignore

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+__pycache__
+.mypy_cache

.pre-commit-config.yaml

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+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+
+# Formats code correctly
+-   repo: https://github.com/psf/black
+    rev: 21.12b0
+    hooks:
+    -   id: black
+        args: [
+            '--experimental-string-processing'
+        ]
+
+# Sorts imports
+-   repo: https://github.com/pycqa/isort
+    rev: 5.10.1
+    hooks:
+    -   id: isort
+        name: isort (python)
+        args: ["--profile", "black"]
+
+# Checks unused imports, like lengths, etc
+-   repo: https://gitlab.com/pycqa/flake8
+    rev: 4.0.0
+    hooks:
+    -   id: flake8
+        args: [
+            '--per-file-ignores=__init__.py:F401',
+            '--max-line-length=88',
+            '--ignore=E203,W503'
+        ]
+
+# Checks types
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: 'v0.971'
+    hooks:
+    -   id: mypy
+        additional_dependencies: [data-science-types>=0.2, torch>=1.6]

LICENSE

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+MIT License
+
+Copyright (c) 2022 archinet.ai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+<img src="./LOGO.png"></img>
+
+A fully featured audio diffusion library, for PyTorch. Includes models for unconditional audio generation, text-conditional audio generation, diffusion autoencoding, upsampling, and vocoding. The provided models are waveform-based, however, the U-Net (built using [`a-unet`](https://github.com/archinetai/a-unet)), `DiffusionModel`, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats. **Notes: (1) no pre-trained models are provided here, (2) the configs shown are indicative and untested, see [Moûsai](https://arxiv.org/abs/2301.11757) for the configs used in the paper.**
+
+
+## Install
+
+```bash
+pip install audio-diffusion-pytorch
+```
+
+[![PyPI - Python Version](https://img.shields.io/pypi/v/audio-diffusion-pytorch?style=flat&colorA=black&colorB=black)](https://pypi.org/project/audio-diffusion-pytorch/)
+[![Downloads](https://static.pepy.tech/personalized-badge/audio-diffusion-pytorch?period=total&units=international_system&left_color=black&right_color=black&left_text=Downloads)](https://pepy.tech/project/audio-diffusion-pytorch)
+
+
+## Usage
+
+### Unconditional Generator
+
+```py
+from audio_diffusion_pytorch import DiffusionModel, UNetV0, VDiffusion, VSampler
+
+model = DiffusionModel(
+    net_t=UNetV0, # The model type used for diffusion (U-Net V0 in this case)
+    in_channels=2, # U-Net: number of input/output (audio) channels
+    channels=[8, 32, 64, 128, 256, 512, 512, 1024, 1024], # U-Net: channels at each layer
+    factors=[1, 4, 4, 4, 2, 2, 2, 2, 2], # U-Net: downsampling and upsampling factors at each layer
+    items=[1, 2, 2, 2, 2, 2, 2, 4, 4], # U-Net: number of repeating items at each layer
+    attentions=[0, 0, 0, 0, 0, 1, 1, 1, 1], # U-Net: attention enabled/disabled at each layer
+    attention_heads=8, # U-Net: number of attention heads per attention item
+    attention_features=64, # U-Net: number of attention features per attention item
+    diffusion_t=VDiffusion, # The diffusion method used
+    sampler_t=VSampler, # The diffusion sampler used
+)
+
+# Train model with audio waveforms
+audio = torch.randn(1, 2, 2**18) # [batch_size, in_channels, length]
+loss = model(audio)
+loss.backward()
+
+# Turn noise into new audio sample with diffusion
+noise = torch.randn(1, 2, 2**18) # [batch_size, in_channels, length]
+sample = model.sample(noise, num_steps=10) # Suggested num_steps 10-100
+```
+
+### Text-Conditional Generator
+A text-to-audio diffusion model that conditions the generation with `t5-base` text embeddings, requires `pip install transformers`.
+```py
+from audio_diffusion_pytorch import DiffusionModel, UNetV0, VDiffusion, VSampler
+
+model = DiffusionModel(
+    # ... same as unconditional model
+    use_text_conditioning=True, # U-Net: enables text conditioning (default T5-base)
+    use_embedding_cfg=True, # U-Net: enables classifier free guidance
+    embedding_max_length=64, # U-Net: text embedding maximum length (default for T5-base)
+    embedding_features=768, # U-Net: text mbedding features (default for T5-base)
+    cross_attentions=[0, 0, 0, 1, 1, 1, 1, 1, 1], # U-Net: cross-attention enabled/disabled at each layer
+)
+
+# Train model with audio waveforms
+audio_wave = torch.randn(1, 2, 2**18) # [batch, in_channels, length]
+loss = model(
+    audio_wave,
+    text=['The audio description'], # Text conditioning, one element per batch
+    embedding_mask_proba=0.1 # Probability of masking text with learned embedding (Classifier-Free Guidance Mask)
+)
+loss.backward()
+
+# Turn noise into new audio sample with diffusion
+noise = torch.randn(1, 2, 2**18)
+sample = model.sample(
+    noise,
+    text=['The audio description'],
+    embedding_scale=5.0, # Higher for more text importance, suggested range: 1-15 (Classifier-Free Guidance Scale)
+    num_steps=2 # Higher for better quality, suggested num_steps: 10-100
+)
+```
+
+### Diffusion Upsampler
+Upsample audio from a lower sample rate to higher sample rate using diffusion, e.g. 3kHz to 48kHz.
+```py
+from audio_diffusion_pytorch import DiffusionUpsampler, UNetV0, VDiffusion, VSampler
+
+upsampler = DiffusionUpsampler(
+    net_t=UNetV0, # The model type used for diffusion
+    upsample_factor=16, # The upsample factor (e.g. 16 can be used for 3kHz to 48kHz)
+    in_channels=2, # U-Net: number of input/output (audio) channels
+    channels=[8, 32, 64, 128, 256, 512, 512, 1024, 1024], # U-Net: channels at each layer
+    factors=[1, 4, 4, 4, 2, 2, 2, 2, 2], # U-Net: downsampling and upsampling factors at each layer
+    items=[1, 2, 2, 2, 2, 2, 2, 4, 4], # U-Net: number of repeating items at each layer
+    diffusion_t=VDiffusion, # The diffusion method used
+    sampler_t=VSampler, # The diffusion sampler used
+)
+
+# Train model with high sample rate audio waveforms
+audio = torch.randn(1, 2, 2**18) # [batch, in_channels, length]
+loss = upsampler(audio)
+loss.backward()
+
+# Turn low sample rate audio into high sample rate
+downsampled_audio = torch.randn(1, 2, 2**14) # [batch, in_channels, length]
+sample = upsampler.sample(downsampled_audio, num_steps=10) # Output has shape: [1, 2, 2**18]
+```
+
+### Diffusion Vocoder
+Convert a mel-spectrogram to wavefrom using diffusion.
+```py
+from audio_diffusion_pytorch import DiffusionVocoder, UNetV0, VDiffusion, VSampler
+
+vocoder = DiffusionVocoder(
+    mel_n_fft=1024, # Mel-spectrogram n_fft
+    mel_channels=80, # Mel-spectrogram channels
+    mel_sample_rate=48000, # Mel-spectrogram sample rate
+    mel_normalize_log=True, # Mel-spectrogram log normalization (alternative is mel_normalize=True for [-1,1] power normalization)
+    net_t=UNetV0, # The model type used for diffusion vocoding
+    channels=[8, 32, 64, 128, 256, 512, 512, 1024, 1024], # U-Net: channels at each layer
+    factors=[1, 4, 4, 4, 2, 2, 2, 2, 2], # U-Net: downsampling and upsampling factors at each layer
+    items=[1, 2, 2, 2, 2, 2, 2, 4, 4], # U-Net: number of repeating items at each layer
+    diffusion_t=VDiffusion, # The diffusion method used
+    sampler_t=VSampler, # The diffusion sampler used
+)
+
+# Train model on waveforms (automatically converted to mel internally)
+audio = torch.randn(1, 2, 2**18) # [batch, in_channels, length]
+loss = vocoder(audio)
+loss.backward()
+
+# Turn mel spectrogram into waveform
+mel_spectrogram = torch.randn(1, 2, 80, 1024) # [batch, in_channels, mel_channels, mel_length]
+sample = vocoder.sample(mel_spectrogram, num_steps=10) # Output has shape: [1, 2, 2**18]
+```
+
+### Diffusion Autoencoder
+Autoencode audio into a compressed latent using diffusion. Any encoder can be provided as long as it subclasses the `EncoderBase` class or contains an `out_channels` and `downsample_factor` field.
+```py
+from audio_diffusion_pytorch import DiffusionAE, UNetV0, VDiffusion, VSampler
+from audio_encoders_pytorch import MelE1d, TanhBottleneck
+
+autoencoder = DiffusionAE(
+    encoder=MelE1d( # The encoder used, in this case a mel-spectrogram encoder
+        in_channels=2,
+        channels=512,
+        multipliers=[1, 1],
+        factors=[2],
+        num_blocks=[12],
+        out_channels=32,
+        mel_channels=80,
+        mel_sample_rate=48000,
+        mel_normalize_log=True,
+        bottleneck=TanhBottleneck(),
+    ),
+    inject_depth=6,
+    net_t=UNetV0, # The model type used for diffusion upsampling
+    in_channels=2, # U-Net: number of input/output (audio) channels
+    channels=[8, 32, 64, 128, 256, 512, 512, 1024, 1024], # U-Net: channels at each layer
+    factors=[1, 4, 4, 4, 2, 2, 2, 2, 2], # U-Net: downsampling and upsampling factors at each layer
+    items=[1, 2, 2, 2, 2, 2, 2, 4, 4], # U-Net: number of repeating items at each layer
+    diffusion_t=VDiffusion, # The diffusion method used
+    sampler_t=VSampler, # The diffusion sampler used
+)
+
+# Train autoencoder with audio samples
+audio = torch.randn(1, 2, 2**18) # [batch, in_channels, length]
+loss = autoencoder(audio)
+loss.backward()
+
+# Encode/decode audio
+audio = torch.randn(1, 2, 2**18) # [batch, in_channels, length]
+latent = autoencoder.encode(audio) # Encode
+sample = autoencoder.decode(latent, num_steps=10) # Decode by sampling diffusion model conditioning on latent
+```
+
+## Other
+
+### Inpainting
+```py
+from audio_diffusion_pytorch import UNetV0, VInpainter
+
+# The diffusion UNetV0 (this is an example, the net must be trained to work)
+net = UNetV0(
+    dim=1,
+    in_channels=2, # U-Net: number of input/output (audio) channels
+    channels=[8, 32, 64, 128, 256, 512, 512, 1024, 1024], # U-Net: channels at each layer
+    factors=[1, 4, 4, 4, 2, 2, 2, 2, 2], # U-Net: downsampling and upsampling factors at each layer
+    items=[1, 2, 2, 2, 2, 2, 2, 4, 4], # U-Net: number of repeating items at each layer
+    attentions=[0, 0, 0, 0, 0, 1, 1, 1, 1], # U-Net: attention enabled/disabled at each layer
+    attention_heads=8, # U-Net: number of attention heads per attention block
+    attention_features=64, # U-Net: number of attention features per attention block,
+)
+
+# Instantiate inpainter with trained net
+inpainter = VInpainter(net=net)
+
+# Inpaint source
+y = inpainter(
+    source=torch.randn(1, 2, 2**18), # Start source
+    mask=torch.randint(0, 2, (1, 2, 2 ** 18), dtype=torch.bool),  # Set to `True` the parts you want to keep
+    num_steps=10, # Number of inpainting steps
+    num_resamples=2, # Number of resampling steps
+    show_progress=True,
+) # [1, 2, 2 ** 18]
+```
+
+## Appreciation
+
+* [StabilityAI](https://stability.ai/) for the compute, [Zach Evans](https://github.com/zqevans) and everyone else from [HarmonAI](https://www.harmonai.org/) for the interesting research discussions.
+* [ETH Zurich](https://inf.ethz.ch/) for the resources, [Zhijing Jin](https://zhijing-jin.com/), [Bernhard Schoelkopf](https://is.mpg.de/~bs), and [Mrinmaya Sachan](http://www.mrinmaya.io/) for supervising this Thesis.
+* [Phil Wang](https://github.com/lucidrains) for the beautiful open source contributions on [diffusion](https://github.com/lucidrains/denoising-diffusion-pytorch) and [Imagen](https://github.com/lucidrains/imagen-pytorch).
+* [Katherine Crowson](https://github.com/crowsonkb) for the experiments with [k-diffusion](https://github.com/crowsonkb/k-diffusion) and the insane collection of samplers.
+
+## Citations
+
+DDPM Diffusion
+```bibtex
+@misc{2006.11239,
+Author = {Jonathan Ho and Ajay Jain and Pieter Abbeel},
+Title = {Denoising Diffusion Probabilistic Models},
+Year = {2020},
+Eprint = {arXiv:2006.11239},
+}
+```
+
+DDIM (V-Sampler)
+```bibtex
+@misc{2010.02502,
+Author = {Jiaming Song and Chenlin Meng and Stefano Ermon},
+Title = {Denoising Diffusion Implicit Models},
+Year = {2020},
+Eprint = {arXiv:2010.02502},
+}
+```
+
+V-Diffusion
+```bibtex
+@misc{2202.00512,
+Author = {Tim Salimans and Jonathan Ho},
+Title = {Progressive Distillation for Fast Sampling of Diffusion Models},
+Year = {2022},
+Eprint = {arXiv:2202.00512},
+}
+```
+
+Imagen (T5 Text Conditioning)
+```bibtex
+@misc{2205.11487,
+Author = {Chitwan Saharia and William Chan and Saurabh Saxena and Lala Li and Jay Whang and Emily Denton and Seyed Kamyar Seyed Ghasemipour and Burcu Karagol Ayan and S. Sara Mahdavi and Rapha Gontijo Lopes and Tim Salimans and Jonathan Ho and David J Fleet and Mohammad Norouzi},
+Title = {Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding},
+Year = {2022},
+Eprint = {arXiv:2205.11487},
+}
+```

setup.py

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+from setuptools import find_packages, setup
+
+setup(
+    name="audio-diffusion-pytorch",
+    packages=find_packages(exclude=[]),
+    version="0.1.3",
+    license="MIT",
+    description="Audio Diffusion - PyTorch",
+    long_description_content_type="text/markdown",
+    author="Flavio Schneider",
+    author_email="archinetai@protonmail.com",
+    url="https://github.com/archinetai/audio-diffusion-pytorch",
+    keywords=["artificial intelligence", "deep learning", "audio generation"],
+    install_requires=[
+        "tqdm",
+        "torch>=1.6",
+        "torchaudio",
+        "data-science-types>=0.2",
+        "einops>=0.6",
+        "a-unet",
+    ],
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3.6",
+    ],
+)