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 ``` [](https://pypi.org/project/audio-diffusion-pytorch/) [](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}, } ```