mirror of
https://github.com/Nighthawk42/llm-tts-factory.git
synced 2026-08-30 07:22:27 +00:00
214 lines
6.6 KiB
Python
214 lines
6.6 KiB
Python
"""
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Adapted from https://github.com/gemelo-ai/vocos
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"""
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from typing import Optional
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import torchaudio
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import torch
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from torch import nn
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from .quantizer import FSQSTE
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def safe_log(x: torch.Tensor, clip_val: float = 5e-3) -> torch.Tensor:
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return torch.log(torch.clip(x, min=clip_val))
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class SimpleMLP(nn.Module):
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def __init__(self,
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dim,
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intermediate_dim,
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):
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super().__init__()
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self.pwconv1 = nn.Linear(dim, intermediate_dim)
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self.act = nn.GELU()
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self.pwconv2 = nn.Linear(intermediate_dim, dim)
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def forward(self, x):
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x = self.pwconv1(x)
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x = self.act(x)
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x = self.pwconv2(x)
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return x
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class ConvNeXtBlock(nn.Module):
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"""ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.
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Args:
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dim (int): Number of input channels.
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intermediate_dim (int): Dimensionality of the intermediate layer.
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layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
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Defaults to None.
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"""
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def __init__(
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self,
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dim: int,
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intermediate_dim: int,
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layer_scale_init_value: float,
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dw_kernel_size: int = 7,
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):
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super().__init__()
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self.dwconv = nn.Conv1d(dim, dim, kernel_size=dw_kernel_size, padding=dw_kernel_size//2, groups=dim) # depthwise conv
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self.norm = nn.LayerNorm(dim, eps=1e-6)
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self.mlp = SimpleMLP(dim, intermediate_dim)
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self.gamma = (
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nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
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if layer_scale_init_value > 0
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else None
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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residual = x
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x = self.dwconv(x)
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x = x.transpose(1, 2) # (B, C, T) -> (B, T, C)
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x = self.norm(x)
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x = self.mlp(x)
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if self.gamma is not None:
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x = self.gamma * x
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x = x.transpose(1, 2) # (B, T, C) -> (B, C, T)
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x = residual + x
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return x
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class VocosBackbone(nn.Module):
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"""
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Vocos backbone module built with ConvNeXt blocks.
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Args:
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input_channels (int): Number of input features channels.
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dim (int): Hidden dimension of the model.
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intermediate_dim (int): Intermediate dimension used in ConvNeXtBlock.
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num_layers (int): Number of ConvNeXtBlock layers.
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layer_scale_init_value (float, optional): Initial value for layer scaling.
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"""
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def __init__(
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self,
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input_channels: int,
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dim: int,
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intermediate_dim: int,
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num_layers: int,
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input_kernel_size: int = 7,
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dw_kernel_size: int = 7,
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layer_scale_init_value: Optional[float] = None,
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pad: str = 'zeros',
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):
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super().__init__()
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self.input_channels = input_channels
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self.dim = dim
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self.embed = nn.Conv1d(
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input_channels,
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dim,
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kernel_size=input_kernel_size,
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padding=input_kernel_size//2,
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padding_mode=pad
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)
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self.norm = nn.LayerNorm(dim, eps=1e-6)
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self.convnext = nn.ModuleList([
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ConvNeXtBlock(
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dim=dim,
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intermediate_dim=intermediate_dim,
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dw_kernel_size=dw_kernel_size,
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layer_scale_init_value=layer_scale_init_value or 1 / num_layers**0.5,
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)
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for _ in range(num_layers)
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])
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self.final_layer_norm = nn.LayerNorm(dim, eps=1e-6)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv1d, nn.Linear)):
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nn.init.trunc_normal_(m.weight, std=0.02)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x (Tensor): Input tensor of shape (B, C, L), where B is the batch size,
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C denotes output features, and L is the sequence length.
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Returns:
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Tensor: Output of shape (B, L, H), where B is the batch size, L is the sequence length,
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and H denotes the model dimension.
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"""
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x = self.embed(x) # (B, C, L)
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x = self.norm(x.transpose(1, 2))
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x = x.transpose(1, 2)
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for conv_block in self.convnext:
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x = conv_block(x)
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x = self.final_layer_norm(x.transpose(1, 2))
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x = x.transpose(1, 2)
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return x
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class Encoder(nn.Module):
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def __init__(self,
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num_input_mels=50,
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mel_hop_length=512,
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mel_hop_scale=0.25,
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encoder_num_layers=8,
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encoder_dim=768,
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encoder_intermediate_dim=None,
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fsq_levels=[8, 8, 5, 5, 5],
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dw_kernel=5,
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):
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super().__init__()
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self.downsample_scale = 2048 // mel_hop_length
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self.mel_hop_length = mel_hop_length
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self.mel_n_fft = int(mel_hop_length/mel_hop_scale)
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self.encoder_dim = encoder_dim
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self.encoder_intermediate_dim = encoder_intermediate_dim if encoder_intermediate_dim else encoder_dim*3
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self.encoder_num_layers = encoder_num_layers
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self.encoder_initial_channels = num_input_mels
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self.bottleneck_channels = 5
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self.mel_spec = torchaudio.transforms.MelSpectrogram(
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sample_rate=32000,
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n_fft=self.mel_n_fft,
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hop_length=self.mel_hop_length,
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n_mels=num_input_mels,
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center=True,
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power=1,
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)
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self.encoder = VocosBackbone(input_channels=self.encoder_initial_channels,
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dim=self.encoder_dim,
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intermediate_dim=self.encoder_intermediate_dim,
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num_layers=self.encoder_num_layers,
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input_kernel_size=1,
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dw_kernel_size=dw_kernel,
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pad='zeros'
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)
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self.downsampler = nn.Linear(self.encoder_dim, self.bottleneck_channels)
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self.quant = FSQSTE(levels=fsq_levels)
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def encode(self, x):
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x = self.encoder(x)
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# import pdb;pdb.set_trace()
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x = x[:, :, ::self.downsample_scale] # What the heck is this? Brute force downsampling from mel -> tokens.
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x = x.transpose(1,2)
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x = self.downsampler(x)
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x = self.quant(x)
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return x
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def preprocess(self, audio):
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if audio.dim() == 2: # raw audio
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x = self.mel_spec(audio)
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x = safe_log(x)
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elif audio.dim() == 3: # mel spectrogram
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x = audio
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return x
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def forward(self, audio):
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x = self.preprocess(audio)
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# print("done preprocessing: ",x.shape)
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# import pdb;pdb.set_trace()
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x = self.encode(x)
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codes = self.quant.to_codebook_index(x)
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return codes
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