mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2025-12-10 06:24:26 +08:00
157 lines
4.6 KiB
Python
157 lines
4.6 KiB
Python
from typing import Literal
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import torch
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import torch.nn as nn
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from .distributions import DiagonalGaussianDistribution
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from .vae import VAE_16k
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from .bigvgan import BigVGANVocoder
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import logging
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try:
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import torchaudio
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except:
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logging.warning("torchaudio missing, MMAudio VAE model will be broken")
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
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return norm_fn(torch.clamp(x, min=clip_val) * C)
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def spectral_normalize_torch(magnitudes, norm_fn):
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output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
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return output
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class MelConverter(nn.Module):
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def __init__(
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self,
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*,
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sampling_rate: float,
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n_fft: int,
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num_mels: int,
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hop_size: int,
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win_size: int,
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fmin: float,
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fmax: float,
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norm_fn,
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):
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super().__init__()
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self.sampling_rate = sampling_rate
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self.n_fft = n_fft
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self.num_mels = num_mels
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self.hop_size = hop_size
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self.win_size = win_size
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self.fmin = fmin
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self.fmax = fmax
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self.norm_fn = norm_fn
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# mel = librosa_mel_fn(sr=self.sampling_rate,
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# n_fft=self.n_fft,
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# n_mels=self.num_mels,
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# fmin=self.fmin,
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# fmax=self.fmax)
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# mel_basis = torch.from_numpy(mel).float()
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mel_basis = torch.empty((num_mels, 1 + n_fft // 2))
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hann_window = torch.hann_window(self.win_size)
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self.register_buffer('mel_basis', mel_basis)
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self.register_buffer('hann_window', hann_window)
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@property
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def device(self):
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return self.mel_basis.device
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def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
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waveform = waveform.clamp(min=-1., max=1.).to(self.device)
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waveform = torch.nn.functional.pad(
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waveform.unsqueeze(1),
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[int((self.n_fft - self.hop_size) / 2),
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int((self.n_fft - self.hop_size) / 2)],
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mode='reflect')
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waveform = waveform.squeeze(1)
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spec = torch.stft(waveform,
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self.n_fft,
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hop_length=self.hop_size,
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win_length=self.win_size,
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window=self.hann_window,
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center=center,
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pad_mode='reflect',
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normalized=False,
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onesided=True,
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return_complex=True)
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spec = torch.view_as_real(spec)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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spec = torch.matmul(self.mel_basis, spec)
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spec = spectral_normalize_torch(spec, self.norm_fn)
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return spec
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class AudioAutoencoder(nn.Module):
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def __init__(
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self,
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*,
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# ckpt_path: str,
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mode=Literal['16k', '44k'],
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need_vae_encoder: bool = True,
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):
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super().__init__()
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assert mode == "16k", "Only 16k mode is supported currently."
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self.mel_converter = MelConverter(sampling_rate=16_000,
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n_fft=1024,
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num_mels=80,
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hop_size=256,
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win_size=1024,
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fmin=0,
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fmax=8_000,
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norm_fn=torch.log10)
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self.vae = VAE_16k().eval()
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bigvgan_config = {
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"resblock": "1",
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"num_mels": 80,
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"upsample_rates": [4, 4, 2, 2, 2, 2],
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"upsample_kernel_sizes": [8, 8, 4, 4, 4, 4],
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"upsample_initial_channel": 1536,
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"resblock_kernel_sizes": [3, 7, 11],
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"resblock_dilation_sizes": [
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[1, 3, 5],
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[1, 3, 5],
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[1, 3, 5],
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],
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"activation": "snakebeta",
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"snake_logscale": True,
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}
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self.vocoder = BigVGANVocoder(
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bigvgan_config
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).eval()
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@torch.inference_mode()
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def encode_audio(self, x) -> DiagonalGaussianDistribution:
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# x: (B * L)
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mel = self.mel_converter(x)
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dist = self.vae.encode(mel)
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return dist
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@torch.no_grad()
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def decode(self, z):
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mel_decoded = self.vae.decode(z)
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audio = self.vocoder(mel_decoded)
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audio = torchaudio.functional.resample(audio, 16000, 44100)
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return audio
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@torch.no_grad()
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def encode(self, audio):
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audio = audio.mean(dim=1)
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audio = torchaudio.functional.resample(audio, 44100, 16000)
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dist = self.encode_audio(audio)
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return dist.mean
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