audio diffusion pytorch
v0.1.3
Une bibliothèque de diffusion audio complète, pour PyTorch. Comprend des modèles pour la génération audio inconditionnelle, la génération audio conditionnelle de texte, l'encodage automatique de diffusion, le suréchantillonnage et le vocodage. Les modèles fournis sont basés sur la forme d'onde, cependant, l'U-Net (construit à l'aide de a-unet ), DiffusionModel , la méthode de diffusion et les échantillonneurs de diffusion sont tous deux génériques pour n'importe quelle dimension et hautement personnalisables pour fonctionner sur d'autres formats. Notes : (1) aucun modèle pré-entraîné n'est fourni ici, (2) les configurations présentées sont indicatives et non testées, voir Moûsai pour les configurations utilisées dans l'article.
pip install audio-diffusion-pytorch 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 Un modèle de diffusion texte-audio qui conditionne la génération avec des intégrations de texte t5-base nécessite pip install transformers .
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
)Suréchantillonnez l'audio d'une fréquence d'échantillonnage inférieure à une fréquence d'échantillonnage plus élevée en utilisant la diffusion, par exemple de 3 kHz à 48 kHz.
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]Convertissez un spectrogramme mel en wavefrom en utilisant la diffusion.
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] Encodez automatiquement l'audio dans un format latent compressé à l'aide de la diffusion. N'importe quel encodeur peut être fourni à condition qu'il sous-classe la classe EncoderBase ou qu'il contienne un champ out_channels et downsample_factor .
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 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] Diffusion DDPM
@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-Échantillonneur)
@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
@misc { 2202.00512 ,
Author = { Tim Salimans and Jonathan Ho } ,
Title = { Progressive Distillation for Fast Sampling of Diffusion Models } ,
Year = { 2022 } ,
Eprint = { arXiv:2202.00512 } ,
}Image (conditionnement de texte T5)
@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 } ,
}
