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import torch
import math
import numpy as np
from torch import nn
from torch.nn import functional as F
from torchaudio import transforms as T
from alias_free_torch import Activation1d
from .nn.layers import WNConv1d, WNConvTranspose1d
from typing import Literal, Dict, Any
# from .inference.sampling import sample
from .utils import prepare_audio
from .blocks import SnakeBeta
from .bottleneck import Bottleneck, DiscreteBottleneck
from .factory import create_pretransform_from_config, create_bottleneck_from_config
from .pretransforms import Pretransform
def checkpoint(function, *args, **kwargs):
kwargs.setdefault("use_reentrant", False)
return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
if activation == "elu":
act = nn.ELU()
elif activation == "snake":
act = SnakeBeta(channels)
elif activation == "none":
act = nn.Identity()
else:
raise ValueError(f"Unknown activation {activation}")
if antialias:
act = Activation1d(act)
return act
class ResidualUnit(nn.Module):
def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
super().__init__()
self.dilation = dilation
padding = (dilation * (7-1)) // 2
self.layers = nn.Sequential(
get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
WNConv1d(in_channels=in_channels, out_channels=out_channels,
kernel_size=7, dilation=dilation, padding=padding),
get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
WNConv1d(in_channels=out_channels, out_channels=out_channels,
kernel_size=1)
)
def forward(self, x):
res = x
#x = checkpoint(self.layers, x)
x = self.layers(x)
return x + res
class EncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
super().__init__()
self.layers = nn.Sequential(
ResidualUnit(in_channels=in_channels,
out_channels=in_channels, dilation=1, use_snake=use_snake),
ResidualUnit(in_channels=in_channels,
out_channels=in_channels, dilation=3, use_snake=use_snake),
ResidualUnit(in_channels=in_channels,
out_channels=in_channels, dilation=9, use_snake=use_snake),
get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
WNConv1d(in_channels=in_channels, out_channels=out_channels,
kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
)
def forward(self, x):
return self.layers(x)
class DecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
super().__init__()
if use_nearest_upsample:
upsample_layer = nn.Sequential(
nn.Upsample(scale_factor=stride, mode="nearest"),
WNConv1d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=2*stride,
stride=1,
bias=False,
padding='same')
)
else:
upsample_layer = WNConvTranspose1d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
self.layers = nn.Sequential(
get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
upsample_layer,
ResidualUnit(in_channels=out_channels, out_channels=out_channels,
dilation=1, use_snake=use_snake),
ResidualUnit(in_channels=out_channels, out_channels=out_channels,
dilation=3, use_snake=use_snake),
ResidualUnit(in_channels=out_channels, out_channels=out_channels,
dilation=9, use_snake=use_snake),
)
def forward(self, x):
return self.layers(x)
class OobleckEncoder(nn.Module):
def __init__(self,
in_channels=2,
channels=128,
latent_dim=32,
c_mults = [1, 2, 4, 8],
strides = [2, 4, 8, 8],
use_snake=False,
antialias_activation=False
):
super().__init__()
c_mults = [1] + c_mults
self.depth = len(c_mults)
layers = [
WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
]
for i in range(self.depth-1):
layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
layers += [
get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
]
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class OobleckDecoder(nn.Module):
def __init__(self,
out_channels=2,
channels=128,
latent_dim=32,
c_mults = [1, 2, 4, 8],
strides = [2, 4, 8, 8],
use_snake=False,
antialias_activation=False,
use_nearest_upsample=False,
final_tanh=True):
super().__init__()
c_mults = [1] + c_mults
self.depth = len(c_mults)
layers = [
WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
]
for i in range(self.depth-1, 0, -1):
layers += [DecoderBlock(
in_channels=c_mults[i]*channels,
out_channels=c_mults[i-1]*channels,
stride=strides[i-1],
use_snake=use_snake,
antialias_activation=antialias_activation,
use_nearest_upsample=use_nearest_upsample
)
]
layers += [
get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
nn.Tanh() if final_tanh else nn.Identity()
]
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class DACEncoderWrapper(nn.Module):
def __init__(self, in_channels=1, **kwargs):
super().__init__()
from dac.model.dac import Encoder as DACEncoder
latent_dim = kwargs.pop("latent_dim", None)
encoder_out_dim = kwargs["d_model"] * (2 ** len(kwargs["strides"]))
self.encoder = DACEncoder(d_latent=encoder_out_dim, **kwargs)
self.latent_dim = latent_dim
# Latent-dim support was added to DAC after this was first written, and implemented differently, so this is for backwards compatibility
self.proj_out = nn.Conv1d(self.encoder.enc_dim, latent_dim, kernel_size=1) if latent_dim is not None else nn.Identity()
if in_channels != 1:
self.encoder.block[0] = WNConv1d(in_channels, kwargs.get("d_model", 64), kernel_size=7, padding=3)
def forward(self, x):
x = self.encoder(x)
x = self.proj_out(x)
return x
class DACDecoderWrapper(nn.Module):
def __init__(self, latent_dim, out_channels=1, **kwargs):
super().__init__()
from dac.model.dac import Decoder as DACDecoder
self.decoder = DACDecoder(**kwargs, input_channel = latent_dim, d_out=out_channels)
self.latent_dim = latent_dim
def forward(self, x):
return self.decoder(x)
class AudioAutoencoder(nn.Module):
def __init__(
self,
encoder,
decoder,
latent_dim,
downsampling_ratio,
sample_rate,
io_channels=2,
bottleneck: Bottleneck = None,
pretransform: Pretransform = None,
in_channels = None,
out_channels = None,
soft_clip = False
):
super().__init__()
self.downsampling_ratio = downsampling_ratio
self.sample_rate = sample_rate
self.latent_dim = latent_dim
self.io_channels = io_channels
self.in_channels = io_channels
self.out_channels = io_channels
self.min_length = self.downsampling_ratio
if in_channels is not None:
self.in_channels = in_channels
if out_channels is not None:
self.out_channels = out_channels
self.bottleneck = bottleneck
self.encoder = encoder
self.decoder = decoder
self.pretransform = pretransform
self.soft_clip = soft_clip
self.is_discrete = self.bottleneck is not None and self.bottleneck.is_discrete
def encode(self, audio, return_info=False, skip_pretransform=False, iterate_batch=False, **kwargs):
info = {}
if self.pretransform is not None and not skip_pretransform:
if self.pretransform.enable_grad:
if iterate_batch:
audios = []
for i in range(audio.shape[0]):
audios.append(self.pretransform.encode(audio[i:i+1]))
audio = torch.cat(audios, dim=0)
else:
audio = self.pretransform.encode(audio)
else:
with torch.no_grad():
if iterate_batch:
audios = []
for i in range(audio.shape[0]):
audios.append(self.pretransform.encode(audio[i:i+1]))
audio = torch.cat(audios, dim=0)
else:
audio = self.pretransform.encode(audio)
if self.encoder is not None:
if iterate_batch:
latents = []
for i in range(audio.shape[0]):
latents.append(self.encoder(audio[i:i+1]))
latents = torch.cat(latents, dim=0)
else:
latents = self.encoder(audio)
else:
latents = audio
if self.bottleneck is not None:
# TODO: Add iterate batch logic, needs to merge the info dicts
latents, bottleneck_info = self.bottleneck.encode(latents, return_info=True, **kwargs)
info.update(bottleneck_info)
if return_info:
return latents, info
return latents
def decode(self, latents, iterate_batch=False, **kwargs):
if self.bottleneck is not None:
if iterate_batch:
decoded = []
for i in range(latents.shape[0]):
decoded.append(self.bottleneck.decode(latents[i:i+1]))
decoded = torch.cat(decoded, dim=0)
else:
latents = self.bottleneck.decode(latents)
if iterate_batch:
decoded = []
for i in range(latents.shape[0]):
decoded.append(self.decoder(latents[i:i+1]))
decoded = torch.cat(decoded, dim=0)
else:
decoded = self.decoder(latents, **kwargs)
if self.pretransform is not None:
if self.pretransform.enable_grad:
if iterate_batch:
decodeds = []
for i in range(decoded.shape[0]):
decodeds.append(self.pretransform.decode(decoded[i:i+1]))
decoded = torch.cat(decodeds, dim=0)
else:
decoded = self.pretransform.decode(decoded)
else:
with torch.no_grad():
if iterate_batch:
decodeds = []
for i in range(latents.shape[0]):
decodeds.append(self.pretransform.decode(decoded[i:i+1]))
decoded = torch.cat(decodeds, dim=0)
else:
decoded = self.pretransform.decode(decoded)
if self.soft_clip:
decoded = torch.tanh(decoded)
return decoded
def decode_tokens(self, tokens, **kwargs):
'''
Decode discrete tokens to audio
Only works with discrete autoencoders
'''
assert isinstance(self.bottleneck, DiscreteBottleneck), "decode_tokens only works with discrete autoencoders"
latents = self.bottleneck.decode_tokens(tokens, **kwargs)
return self.decode(latents, **kwargs)
def preprocess_audio_for_encoder(self, audio, in_sr):
'''
Preprocess single audio tensor (Channels x Length) to be compatible with the encoder.
If the model is mono, stereo audio will be converted to mono.
Audio will be silence-padded to be a multiple of the model's downsampling ratio.
Audio will be resampled to the model's sample rate.
The output will have batch size 1 and be shape (1 x Channels x Length)
'''
return self.preprocess_audio_list_for_encoder([audio], [in_sr])
def preprocess_audio_list_for_encoder(self, audio_list, in_sr_list):
'''
Preprocess a [list] of audio (Channels x Length) into a batch tensor to be compatable with the encoder.
The audio in that list can be of different lengths and channels.
in_sr can be an integer or list. If it's an integer it will be assumed it is the input sample_rate for every audio.
All audio will be resampled to the model's sample rate.
Audio will be silence-padded to the longest length, and further padded to be a multiple of the model's downsampling ratio.
If the model is mono, all audio will be converted to mono.
The output will be a tensor of shape (Batch x Channels x Length)
'''
batch_size = len(audio_list)
if isinstance(in_sr_list, int):
in_sr_list = [in_sr_list]*batch_size
assert len(in_sr_list) == batch_size, "list of sample rates must be the same length of audio_list"
new_audio = []
max_length = 0
# resample & find the max length
for i in range(batch_size):
audio = audio_list[i]
in_sr = in_sr_list[i]
if len(audio.shape) == 3 and audio.shape[0] == 1:
# batchsize 1 was given by accident. Just squeeze it.
audio = audio.squeeze(0)
elif len(audio.shape) == 1:
# Mono signal, channel dimension is missing, unsqueeze it in
audio = audio.unsqueeze(0)
assert len(audio.shape)==2, "Audio should be shape (Channels x Length) with no batch dimension"
# Resample audio
if in_sr != self.sample_rate:
resample_tf = T.Resample(in_sr, self.sample_rate).to(audio.device)
audio = resample_tf(audio)
new_audio.append(audio)
if audio.shape[-1] > max_length:
max_length = audio.shape[-1]
# Pad every audio to the same length, multiple of model's downsampling ratio
padded_audio_length = max_length + (self.min_length - (max_length % self.min_length)) % self.min_length
for i in range(batch_size):
# Pad it & if necessary, mixdown/duplicate stereo/mono channels to support model
new_audio[i] = prepare_audio(new_audio[i], in_sr=in_sr, target_sr=in_sr, target_length=padded_audio_length,
target_channels=self.in_channels, device=new_audio[i].device).squeeze(0)
# convert to tensor
return torch.stack(new_audio)
def encode_audio(self, audio, chunked=False, overlap=32, chunk_size=128, **kwargs):
'''
Encode audios into latents. Audios should already be preprocesed by preprocess_audio_for_encoder.
If chunked is True, split the audio into chunks of a given maximum size chunk_size, with given overlap.
Overlap and chunk_size params are both measured in number of latents (not audio samples)
# and therefore you likely could use the same values with decode_audio.
A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size.
Every autoencoder will have a different receptive field size, and thus ideal overlap.
You can determine it empirically by diffing unchunked vs chunked output and looking at maximum diff.
The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
Smaller chunk_size uses less memory, but more compute.
The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
'''
if not chunked:
# default behavior. Encode the entire audio in parallel
return self.encode(audio, **kwargs)
else:
# CHUNKED ENCODING
# samples_per_latent is just the downsampling ratio (which is also the upsampling ratio)
samples_per_latent = self.downsampling_ratio
total_size = audio.shape[2] # in samples
batch_size = audio.shape[0]
chunk_size *= samples_per_latent # converting metric in latents to samples
overlap *= samples_per_latent # converting metric in latents to samples
hop_size = chunk_size - overlap
chunks = []
for i in range(0, total_size - chunk_size + 1, hop_size):
chunk = audio[:,:,i:i+chunk_size]
chunks.append(chunk)
if i+chunk_size != total_size:
# Final chunk
chunk = audio[:,:,-chunk_size:]
chunks.append(chunk)
chunks = torch.stack(chunks)
num_chunks = chunks.shape[0]
# Note: y_size might be a different value from the latent length used in diffusion training
# because we can encode audio of varying lengths
# However, the audio should've been padded to a multiple of samples_per_latent by now.
y_size = total_size // samples_per_latent
# Create an empty latent, we will populate it with chunks as we encode them
y_final = torch.zeros((batch_size,self.latent_dim,y_size)).to(audio.device)
for i in range(num_chunks):
x_chunk = chunks[i,:]
# encode the chunk
y_chunk = self.encode(x_chunk)
# figure out where to put the audio along the time domain
if i == num_chunks-1:
# final chunk always goes at the end
t_end = y_size
t_start = t_end - y_chunk.shape[2]
else:
t_start = i * hop_size // samples_per_latent
t_end = t_start + chunk_size // samples_per_latent
# remove the edges of the overlaps
ol = overlap//samples_per_latent//2
chunk_start = 0
chunk_end = y_chunk.shape[2]
if i > 0:
# no overlap for the start of the first chunk
t_start += ol
chunk_start += ol
if i < num_chunks-1:
# no overlap for the end of the last chunk
t_end -= ol
chunk_end -= ol
# paste the chunked audio into our y_final output audio
y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
return y_final
def decode_audio(self, latents, chunked=False, overlap=32, chunk_size=128, **kwargs):
'''
Decode latents to audio.
If chunked is True, split the latents into chunks of a given maximum size chunk_size, with given overlap, both of which are measured in number of latents.
A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size.
Every autoencoder will have a different receptive field size, and thus ideal overlap.
You can determine it empirically by diffing unchunked vs chunked audio and looking at maximum diff.
The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
Smaller chunk_size uses less memory, but more compute.
The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
'''
if not chunked:
# default behavior. Decode the entire latent in parallel
return self.decode(latents, **kwargs)
else:
# chunked decoding
hop_size = chunk_size - overlap
total_size = latents.shape[2]
batch_size = latents.shape[0]
chunks = []
for i in range(0, total_size - chunk_size + 1, hop_size):
chunk = latents[:,:,i:i+chunk_size]
chunks.append(chunk)
if i+chunk_size != total_size:
# Final chunk
chunk = latents[:,:,-chunk_size:]
chunks.append(chunk)
chunks = torch.stack(chunks)
num_chunks = chunks.shape[0]
# samples_per_latent is just the downsampling ratio
samples_per_latent = self.downsampling_ratio
# Create an empty waveform, we will populate it with chunks as decode them
y_size = total_size * samples_per_latent
y_final = torch.zeros((batch_size,self.out_channels,y_size)).to(latents.device)
for i in range(num_chunks):
x_chunk = chunks[i,:]
# decode the chunk
y_chunk = self.decode(x_chunk)
# figure out where to put the audio along the time domain
if i == num_chunks-1:
# final chunk always goes at the end
t_end = y_size
t_start = t_end - y_chunk.shape[2]
else:
t_start = i * hop_size * samples_per_latent
t_end = t_start + chunk_size * samples_per_latent
# remove the edges of the overlaps
ol = (overlap//2) * samples_per_latent
chunk_start = 0
chunk_end = y_chunk.shape[2]
if i > 0:
# no overlap for the start of the first chunk
t_start += ol
chunk_start += ol
if i < num_chunks-1:
# no overlap for the end of the last chunk
t_end -= ol
chunk_end -= ol
# paste the chunked audio into our y_final output audio
y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
return y_final
# AE factories
def create_encoder_from_config(encoder_config: Dict[str, Any]):
encoder_type = encoder_config.get("type", None)
assert encoder_type is not None, "Encoder type must be specified"
if encoder_type == "oobleck":
encoder = OobleckEncoder(
**encoder_config["config"]
)
elif encoder_type == "seanet":
from encodec.modules import SEANetEncoder
seanet_encoder_config = encoder_config["config"]
#SEANet encoder expects strides in reverse order
seanet_encoder_config["ratios"] = list(reversed(seanet_encoder_config.get("ratios", [2, 2, 2, 2, 2])))
encoder = SEANetEncoder(
**seanet_encoder_config
)
elif encoder_type == "dac":
dac_config = encoder_config["config"]
encoder = DACEncoderWrapper(**dac_config)
elif encoder_type == "local_attn":
from .local_attention import TransformerEncoder1D
local_attn_config = encoder_config["config"]
encoder = TransformerEncoder1D(
**local_attn_config
)
else:
raise ValueError(f"Unknown encoder type {encoder_type}")
requires_grad = encoder_config.get("requires_grad", True)
if not requires_grad:
for param in encoder.parameters():
param.requires_grad = False
return encoder
def create_decoder_from_config(decoder_config: Dict[str, Any]):
decoder_type = decoder_config.get("type", None)
assert decoder_type is not None, "Decoder type must be specified"
if decoder_type == "oobleck":
decoder = OobleckDecoder(
**decoder_config["config"]
)
elif decoder_type == "seanet":
from encodec.modules import SEANetDecoder
decoder = SEANetDecoder(
**decoder_config["config"]
)
elif decoder_type == "dac":
dac_config = decoder_config["config"]
decoder = DACDecoderWrapper(**dac_config)
elif decoder_type == "local_attn":
from .local_attention import TransformerDecoder1D
local_attn_config = decoder_config["config"]
decoder = TransformerDecoder1D(
**local_attn_config
)
else:
raise ValueError(f"Unknown decoder type {decoder_type}")
requires_grad = decoder_config.get("requires_grad", True)
if not requires_grad:
for param in decoder.parameters():
param.requires_grad = False
return decoder
def create_autoencoder_from_config(config: Dict[str, Any]):
ae_config = config["model"]
encoder = create_encoder_from_config(ae_config["encoder"])
decoder = create_decoder_from_config(ae_config["decoder"])
bottleneck = ae_config.get("bottleneck", None)
latent_dim = ae_config.get("latent_dim", None)
assert latent_dim is not None, "latent_dim must be specified in model config"
downsampling_ratio = ae_config.get("downsampling_ratio", None)
assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
io_channels = ae_config.get("io_channels", None)
assert io_channels is not None, "io_channels must be specified in model config"
sample_rate = config.get("sample_rate", None)
assert sample_rate is not None, "sample_rate must be specified in model config"
in_channels = ae_config.get("in_channels", None)
out_channels = ae_config.get("out_channels", None)
pretransform = ae_config.get("pretransform", None)
if pretransform is not None:
pretransform = create_pretransform_from_config(pretransform, sample_rate)
if bottleneck is not None:
bottleneck = create_bottleneck_from_config(bottleneck)
soft_clip = ae_config["decoder"].get("soft_clip", False)
return AudioAutoencoder(
encoder,
decoder,
io_channels=io_channels,
latent_dim=latent_dim,
downsampling_ratio=downsampling_ratio,
sample_rate=sample_rate,
bottleneck=bottleneck,
pretransform=pretransform,
in_channels=in_channels,
out_channels=out_channels,
soft_clip=soft_clip
)