deepgeek/CosyVoice
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lyuxiang.lx
2024-12-12 18:48:25 +08:00
parent c693039d14
commit 2511a49a72
4 changed files with 18 additions and 33 deletions
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4
cosyvoice/cli/model.py
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@@ -287,8 +287,6 @@ class CosyVoice2Model:
def load(self, llm_model, flow_model, hift_model):
self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
self.llm.to(self.device).eval()
if self.fp16 is True:
self.llm.half()
self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
self.flow.to(self.device).eval()
self.flow.decoder.fp16 = False
@@ -319,8 +317,6 @@ class CosyVoice2Model:
self.flow.decoder.fp16 = True
def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
if self.fp16 is True:
llm_embedding = llm_embedding.half()
with self.llm_context:
for i in self.llm.inference(text=text.to(self.device),
text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),

37
cosyvoice/flow/flow_matching.py
View File

@@ -136,41 +136,26 @@ class ConditionalCFM(BASECFM):
'mask': mask.cpu().numpy(),
'mu': mu.cpu().numpy(),
't': t.cpu().numpy(),
'spk': spks.cpu().numpy(),
'cond': cond.cpu().numpy(),
'mask_rand': torch.randn(1, 1, 1).numpy()
'spks': spks.cpu().numpy(),
'cond': cond.cpu().numpy()
}
output = self.estimator.run(None, ort_inputs)[0]
return torch.tensor(output, dtype=x.dtype, device=x.device)
else:
if not x.is_contiguous():
x = x.contiguous()
if not mask.is_contiguous():
mask = mask.contiguous()
if not mu.is_contiguous():
mu = mu.contiguous()
if not t.is_contiguous():
t = t.contiguous()
if not spks.is_contiguous():
spks = spks.contiguous()
if not cond.is_contiguous():
cond = cond.contiguous()
self.estimator.set_input_shape('x', (2, 80, x.size(2)))
self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
self.estimator.set_input_shape('t', (2,))
self.estimator.set_input_shape('spk', (2, 80))
self.estimator.set_input_shape('spks', (2, 80))
self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
self.estimator.set_input_shape('mask_rand', (1, 1, 1))
# run trt engine
self.estimator.execute_v2([x.data_ptr(),
mask.data_ptr(),
mu.data_ptr(),
t.data_ptr(),
spks.data_ptr(),
cond.data_ptr(),
torch.randn(1, 1, 1).to(x.device).data_ptr(),
x.data_ptr()])
self.estimator.execute_v2([x.contiguous().data_ptr(),
mask.contiguous().data_ptr(),
mu.contiguous().data_ptr(),
t.contiguous().data_ptr(),
spks.contiguous().data_ptr(),
cond.contiguous().data_ptr(),
x.data_ptr()])
return x
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
@@ -241,7 +226,7 @@ class CausalConditionalCFM(ConditionalCFM):
"""
z = self.rand_noise[:, :, :mu.size(2)].to(mu.device) * temperature
if self.sp16 is True:
if self.fp16 is True:
z = z.half()
# fix prompt and overlap part mu and z
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)