Spaces:
Running
on
Zero
Running
on
Zero
revert trt TODO
Browse files- cosyvoice/cli/cosyvoice.py +1 -4
- cosyvoice/cli/model.py +2 -8
- cosyvoice/flow/flow_matching.py +2 -10
cosyvoice/cli/cosyvoice.py
CHANGED
@@ -21,7 +21,7 @@ from cosyvoice.utils.file_utils import logging
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class CosyVoice:
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def __init__(self, model_dir, load_jit=True
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instruct = True if '-Instruct' in model_dir else False
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self.model_dir = model_dir
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if not os.path.exists(model_dir):
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@@ -42,9 +42,6 @@ class CosyVoice:
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if load_jit:
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self.model.load_jit('{}/llm.text_encoder.fp16.zip'.format(model_dir),
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'{}/llm.llm.fp16.zip'.format(model_dir))
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if load_trt:
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# TODO
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self.model.load_trt()
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del configs
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def list_avaliable_spks(self):
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class CosyVoice:
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+
def __init__(self, model_dir, load_jit=True):
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instruct = True if '-Instruct' in model_dir else False
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self.model_dir = model_dir
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if not os.path.exists(model_dir):
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if load_jit:
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self.model.load_jit('{}/llm.text_encoder.fp16.zip'.format(model_dir),
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'{}/llm.llm.fp16.zip'.format(model_dir))
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del configs
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def list_avaliable_spks(self):
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cosyvoice/cli/model.py
CHANGED
@@ -66,11 +66,6 @@ class CosyVoiceModel:
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llm_llm = torch.jit.load(llm_llm_model)
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self.llm.llm = llm_llm
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def load_trt(self):
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# TODO 你需要的TRT推理的准备
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self.flow.decoder.estimator = xxx
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self.flow.decoder.session = xxx
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-
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def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
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with self.llm_context:
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for i in self.llm.inference(text=text.to(self.device),
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@@ -126,7 +121,6 @@ class CosyVoiceModel:
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self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid], self.mel_overlap_dict[this_uuid], self.hift_cache_dict[this_uuid] = [], False, None, None
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p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
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p.start()
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p.join()
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if stream is True:
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token_hop_len = self.token_min_hop_len
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while True:
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@@ -147,7 +141,7 @@ class CosyVoiceModel:
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token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
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if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
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break
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-
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# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
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with self.flow_hift_context:
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@@ -160,7 +154,7 @@ class CosyVoiceModel:
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yield {'tts_speech': this_tts_speech.cpu()}
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else:
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# deal with all tokens
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-
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
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with self.flow_hift_context:
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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llm_llm = torch.jit.load(llm_llm_model)
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self.llm.llm = llm_llm
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def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
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with self.llm_context:
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for i in self.llm.inference(text=text.to(self.device),
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self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid], self.mel_overlap_dict[this_uuid], self.hift_cache_dict[this_uuid] = [], False, None, None
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p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
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p.start()
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if stream is True:
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token_hop_len = self.token_min_hop_len
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while True:
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token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
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if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
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break
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p.join()
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# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
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with self.flow_hift_context:
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yield {'tts_speech': this_tts_speech.cpu()}
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else:
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# deal with all tokens
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p.join()
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
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with self.flow_hift_context:
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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cosyvoice/flow/flow_matching.py
CHANGED
@@ -77,10 +77,10 @@ class ConditionalCFM(BASECFM):
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sol = []
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for step in range(1, len(t_span)):
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dphi_dt = self.
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# Classifier-Free Guidance inference introduced in VoiceBox
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if self.inference_cfg_rate > 0:
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cfg_dphi_dt = self.
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x, mask,
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torch.zeros_like(mu), t,
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torch.zeros_like(spks) if spks is not None else None,
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@@ -96,14 +96,6 @@ class ConditionalCFM(BASECFM):
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return sol[-1]
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# TODO
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def forward_estimator(self):
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if isinstance(self.estimator, trt):
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assert self.training is False, 'tensorrt cannot be used in training'
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return xxx
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else:
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return self.estimator.forward
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def compute_loss(self, x1, mask, mu, spks=None, cond=None):
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"""Computes diffusion loss
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sol = []
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for step in range(1, len(t_span)):
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dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
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# Classifier-Free Guidance inference introduced in VoiceBox
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if self.inference_cfg_rate > 0:
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cfg_dphi_dt = self.estimator(
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x, mask,
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torch.zeros_like(mu), t,
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torch.zeros_like(spks) if spks is not None else None,
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return sol[-1]
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def compute_loss(self, x1, mask, mu, spks=None, cond=None):
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"""Computes diffusion loss
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