deepgeek/Matcha-TTS
2
0
Fork 0
mirror of https://github.com/shivammehta25/Matcha-TTS.git synced 2026-02-05 18:29:19 +08:00
Code Issues Packages Projects Releases Wiki Activity

Adding option to do flow matching based duration prediction

Browse Source
This commit is contained in:
Shivam Mehta
2024-01-05 11:13:07 +00:00
parent 0ed9290c31
commit a58bab5403
8 changed files with 288 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
matcha/models/components/text_encoder.py
View File

@@ -67,33 +67,6 @@ class ConvReluNorm(nn.Module):
return x * x_mask
class DurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.p_dropout = p_dropout
self.drop = torch.nn.Dropout(p_dropout)
self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_1 = LayerNorm(filter_channels)
self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_2 = LayerNorm(filter_channels)
self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
def forward(self, x, x_mask):
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
x = self.drop(x)
x = self.proj(x * x_mask)
return x * x_mask
class RotaryPositionalEmbeddings(nn.Module):
"""
## RoPE module
@@ -330,7 +303,6 @@ class TextEncoder(nn.Module):
self,
encoder_type,
encoder_params,
duration_predictor_params,
n_vocab,
n_spks=1,
spk_emb_dim=128,
@@ -368,12 +340,6 @@ class TextEncoder(nn.Module):
)
self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1)
self.proj_w = DurationPredictor(
self.n_channels + (spk_emb_dim if n_spks > 1 else 0),
duration_predictor_params.filter_channels_dp,
duration_predictor_params.kernel_size,
duration_predictor_params.p_dropout,
)
def forward(self, x, x_lengths, spks=None):
"""Run forward pass to the transformer based encoder and duration predictor
@@ -404,7 +370,7 @@ class TextEncoder(nn.Module):
x = self.encoder(x, x_mask)
mu = self.proj_m(x) * x_mask
x_dp = torch.detach(x)
logw = self.proj_w(x_dp, x_mask)
# x_dp = torch.detach(x)
# logw = self.proj_w(x_dp, x_mask)
return mu, logw, x_mask
return mu, x, x_mask