deepgeek/CosyVoice
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update token args

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lyuxiang.lx
2024-09-29 10:35:10 +08:00
parent 8555ab4ded
commit ffa28e3bbd
2 changed files with 4 additions and 7 deletions
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9
cosyvoice/cli/model.py
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@@ -31,8 +31,8 @@ class CosyVoiceModel:
self.llm = llm self.llm = llm
self.flow = flow self.flow = flow
self.hift = hift self.hift = hift
self.token_min_hop_len = 100 self.token_min_hop_len = 2 * self.flow.input_frame_rate
self.token_max_hop_len = 200 self.token_max_hop_len = 4 * self.flow.input_frame_rate
self.token_overlap_len = 20 self.token_overlap_len = 20
# mel fade in out # mel fade in out
self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256) self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
@@ -87,10 +87,7 @@ class CosyVoiceModel:
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device), prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device).half(), embedding=llm_embedding.to(self.device).half()):
sampling=25,
max_token_text_ratio=30,
min_token_text_ratio=3):
self.tts_speech_token_dict[uuid].append(i) self.tts_speech_token_dict[uuid].append(i)
self.llm_end_dict[uuid] = True self.llm_end_dict[uuid] = True

2
cosyvoice/llm/llm.py
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@@ -197,7 +197,7 @@ class TransformerLM(torch.nn.Module):
offset = 0 offset = 0
att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device) att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device)
for i in range(max_len): for i in range(max_len):
y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=0, required_cache_size=-1, y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1,
att_cache=att_cache, cnn_cache=cnn_cache, att_cache=att_cache, cnn_cache=cnn_cache,
att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]),
device=lm_input.device)).to(torch.bool)) device=lm_input.device)).to(torch.bool))