mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-05 01:49:25 +08:00
update tempo change
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@@ -15,6 +15,7 @@ import torch
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import numpy as np
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import threading
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import time
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from torch.nn import functional as F
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from contextlib import nullcontext
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import uuid
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from cosyvoice.utils.common import fade_in_out
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@@ -91,7 +92,7 @@ class CosyVoiceModel:
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self.tts_speech_token_dict[uuid].append(i)
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self.llm_end_dict[uuid] = True
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def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False):
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def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
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tts_mel = self.flow.inference(token=token.to(self.device),
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token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_token=prompt_token.to(self.device),
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@@ -116,6 +117,9 @@ class CosyVoiceModel:
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self.hift_cache_dict[uuid] = {'source': tts_source[:, :, -self.source_cache_len:], 'mel': tts_mel[:, :, -self.mel_cache_len:]}
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tts_speech = tts_speech[:, :-self.source_cache_len]
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else:
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if speed != 1.0:
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assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
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tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
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tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
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return tts_speech
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@@ -123,7 +127,7 @@ class CosyVoiceModel:
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prompt_text=torch.zeros(1, 0, dtype=torch.int32),
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llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, **kwargs):
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prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
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# this_uuid is used to track variables related to this inference thread
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this_uuid = str(uuid.uuid1())
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with self.lock:
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@@ -169,7 +173,8 @@ class CosyVoiceModel:
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=True)
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finalize=True,
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speed=speed)
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yield {'tts_speech': this_tts_speech.cpu()}
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with self.lock:
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self.tts_speech_token_dict.pop(this_uuid)
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