deepgeek/CosyVoice
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zhoubofan.zbf
2024-08-30 00:47:40 +08:00
parent 5f21aef786
commit 53a3c1b17f
4 changed files with 58 additions and 11 deletions
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2
cosyvoice/flow/flow.py
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@@ -107,7 +107,7 @@ class MaskedDiffWithXvec(torch.nn.Module):
# 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)
mask = (~make_pad_mask(token_len)).to(embedding.dtype).unsqueeze(-1).to(embedding)
token = self.input_embedding(torch.clamp(token, min=0)) * mask
# text encode

36
cosyvoice/flow/flow_matching.py
View File

@@ -32,6 +32,7 @@ class ConditionalCFM(BASECFM):
self.estimator_context = None
self.estimator_engine = None
self.is_saved = None
@torch.inference_mode()
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
@@ -123,6 +124,41 @@ class ConditionalCFM(BASECFM):
self.estimator_context.execute_async_v3(stream_handle=handle)
return ret
else:
if self.is_saved == None:
self.is_saved = True
output = self.estimator.forward(x, mask, mu, t, spks, cond)
torch.save(x, "x.pt")
torch.save(mask, "mask.pt")
torch.save(mu, "mu.pt")
torch.save(t, "t.pt")
torch.save(spks, "spks.pt")
torch.save(cond, "cond.pt")
torch.save(output, "output.pt")
dummy_input = (x, mask, mu, t, spks, cond)
torch.onnx.export(
self.estimator,
dummy_input,
"estimator_fp32.onnx",
export_params=True,
opset_version=17,
do_constant_folding=True,
input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
output_names=['output'],
dynamic_axes={
'x': {2: 'seq_len'},
'mask': {2: 'seq_len'},
'mu': {2: 'seq_len'},
'cond': {2: 'seq_len'},
'output': {2: 'seq_len'},
}
)
# print("x, x.shape", x, x.shape)
# print("mask, mask.shape", mask, mask.shape)
# print("mu, mu.shape", mu, mu.shape)
# print("t, t.shape", t, t.shape)
# print("spks, spks.shape", spks, spks.shape)
# print("cond, cond.shape", cond, cond.shape)
return self.estimator.forward(x, mask, mu, t, spks, cond)
def compute_loss(self, x1, mask, mu, spks=None, cond=None):