deepgeek/CosyVoice
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lyuxiang.lx
2025-08-19 18:53:18 +08:00
parent e3c2400abb
commit da41f6175b
5 changed files with 986 additions and 13 deletions
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165
cosyvoice/flow/flow.py
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@@ -37,14 +37,11 @@ class MaskedDiffWithXvec(torch.nn.Module):
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.decoder_conf = decoder_conf
self.mel_feat_conf = mel_feat_conf
self.vocab_size = vocab_size
self.output_type = output_type
self.input_frame_rate = input_frame_rate
@@ -165,14 +162,11 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.decoder_conf = decoder_conf
self.mel_feat_conf = mel_feat_conf
self.vocab_size = vocab_size
self.output_type = output_type
self.input_frame_rate = input_frame_rate
@@ -279,3 +273,158 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
feat = feat[:, :, mel_len1:]
assert feat.shape[2] == mel_len2
return feat.float(), None
class CausalMaskedDiffWithDiT(torch.nn.Module):
def __init__(self,
input_size: int = 512,
output_size: int = 80,
spk_embed_dim: int = 192,
output_type: str = "mel",
vocab_size: int = 4096,
input_frame_rate: int = 50,
only_mask_loss: bool = True,
token_mel_ratio: int = 2,
pre_lookahead_len: int = 3,
pre_lookahead_layer: torch.nn.Module = None,
decoder: torch.nn.Module = None,
decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.decoder_conf = decoder_conf
self.vocab_size = vocab_size
self.output_type = output_type
self.input_frame_rate = input_frame_rate
logging.info(f"input frame rate={self.input_frame_rate}")
self.input_embedding = nn.Embedding(vocab_size, input_size)
self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
self.pre_lookahead_len = pre_lookahead_len
self.pre_lookahead_layer = pre_lookahead_layer
self.decoder = decoder
self.only_mask_loss = only_mask_loss
self.token_mel_ratio = token_mel_ratio
def forward(
self,
batch: dict,
device: torch.device,
) -> Dict[str, Optional[torch.Tensor]]:
token = batch['speech_token'].to(device)
token_len = batch['speech_token_len'].to(device)
feat = batch['speech_feat'].to(device)
feat_len = batch['speech_feat_len'].to(device)
embedding = batch['embedding'].to(device)
# NOTE unified training, static_chunk_size > 0 or = 0
streaming = True if random.random() < 0.5 else False
# xvec projection
embedding = F.normalize(embedding, dim=1)
embedding = self.spk_embed_affine_layer(embedding)
# concat text and prompt_text
mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
token = self.input_embedding(torch.clamp(token, min=0)) * mask
# text encode
h, h_lengths = self.encoder(token, token_len, streaming=streaming)
h = self.encoder_proj(h)
# get conditions
conds = torch.zeros(feat.shape, device=token.device)
for i, j in enumerate(feat_len):
if random.random() < 0.5:
continue
index = random.randint(0, int(0.3 * j))
conds[i, :index] = feat[i, :index]
conds = conds.transpose(1, 2)
mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
loss, _ = self.decoder.compute_loss(
feat.transpose(1, 2).contiguous(),
mask.unsqueeze(1),
h.transpose(1, 2).contiguous(),
embedding,
cond=conds,
streaming=streaming,
)
return {'loss': loss}
@torch.inference_mode()
def inference(self,
token,
token_len,
prompt_token,
prompt_token_len,
prompt_feat,
prompt_feat_len,
embedding,
streaming,
finalize):
assert token.shape[0] == 1
# xvec projection
embedding = F.normalize(embedding, dim=1)
embedding = self.spk_embed_affine_layer(embedding)
# concat text and prompt_text
token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
token = self.input_embedding(torch.clamp(token, min=0)) * mask
# text encode
if finalize is True:
h = self.pre_lookahead_layer(token)
else:
h = self.pre_lookahead_layer(token[:, :-self.pre_lookahead_len], context=token[:, -self.pre_lookahead_len:])
h = h.repeat_interleave(self.token_mel_ratio, dim=1)
mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
# get conditions
conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
conds[:, :mel_len1] = prompt_feat
conds = conds.transpose(1, 2)
mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
feat, _ = self.decoder(
mu=h.transpose(1, 2).contiguous(),
mask=mask.unsqueeze(1),
spks=embedding,
cond=conds,
n_timesteps=10,
streaming=streaming
)
feat = feat[:, :, mel_len1:]
assert feat.shape[2] == mel_len2
return feat.float(), None
if __name__ == '__main__':
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from hyperpyyaml import load_hyperpyyaml
with open('./pretrained_models/CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f:
configs = load_hyperpyyaml(f, overrides={'llm': None, 'hift': None})
model = configs['flow']
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
model.eval()
max_len = 10 * model.decoder.estimator.static_chunk_size
chunk_size = model.decoder.estimator.static_chunk_size
context_size = model.pre_lookahead_layer.pre_lookahead_len
token = torch.randint(0, 6561, size=(1, max_len)).to(device)
token_len = torch.tensor([max_len]).to(device)
prompt_token = torch.randint(0, 6561, size=(1, chunk_size)).to(device)
prompt_token_len = torch.tensor([chunk_size]).to(device)
prompt_feat = torch.rand(1, chunk_size * 2, 80).to(device)
prompt_feat_len = torch.tensor([chunk_size * 2]).to(device)
prompt_embedding = torch.rand(1, 192).to(device)
pred_gt, _ = model.inference(token, token_len, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=True)
for i in range(0, max_len, chunk_size):
finalize = True if i + chunk_size + context_size >= max_len else False
pred_chunk, _ = model.inference(token[:, :i + chunk_size + context_size], torch.tensor([token[:, :i + chunk_size + context_size].shape[1]]).to(device), prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=finalize)
pred_chunk = pred_chunk[:, :, i * model.token_mel_ratio:]
print((pred_gt[:, :, i * model.token_mel_ratio: i * model.token_mel_ratio + pred_chunk.shape[2]] - pred_chunk).abs().max().item())