deepgeek/CosyVoice
2
0
Fork 0
mirror of https://github.com/FunAudioLLM/CosyVoice.git synced 2026-02-04 17:39:25 +08:00
Code Issues Packages Projects Releases Wiki Activity

update stream code

Browse Source
This commit is contained in:
lyuxiang.lx
2024-07-30 16:11:28 +08:00
parent 02f941d348
commit f4e70e222c
15 changed files with 182 additions and 109 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
cosyvoice/flow/flow.py
View File

@@ -105,6 +105,7 @@ class MaskedDiffWithXvec(torch.nn.Module):
embedding = self.spk_embed_affine_layer(embedding)
# concat text and prompt_text
token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(embedding)
token = self.input_embedding(torch.clamp(token, min=0)) * mask
@@ -112,17 +113,16 @@ class MaskedDiffWithXvec(torch.nn.Module):
# text encode
h, h_lengths = self.encoder(token, token_len)
h = self.encoder_proj(h)
feat_len = (token_len / 50 * 22050 / 256).int()
h, h_lengths = self.length_regulator(h, feat_len)
mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / 50 * 22050 / 256)
h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2)
# get conditions
conds = torch.zeros([1, feat_len.max().item(), self.output_size], device=token.device)
if prompt_feat.shape[1] != 0:
for i, j in enumerate(prompt_feat_len):
conds[i, :j] = prompt_feat[i]
conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device)
conds[:, :mel_len1] = prompt_feat
conds = conds.transpose(1, 2)
mask = (~make_pad_mask(feat_len)).to(h)
# mask = (~make_pad_mask(feat_len)).to(h)
mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
feat = self.decoder(
mu=h.transpose(1, 2).contiguous(),
mask=mask.unsqueeze(1),
@@ -130,6 +130,6 @@ class MaskedDiffWithXvec(torch.nn.Module):
cond=conds,
n_timesteps=10
)
if prompt_feat.shape[1] != 0:
feat = feat[:, :, prompt_feat.shape[1]:]
feat = feat[:, :, mel_len1:]
assert feat.shape[2] == mel_len2
return feat