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志浩 2024-09-13 16:24:06 +08:00
parent 441b997f19
commit 4a8cb6f0c4
1 changed files with 37 additions and 36 deletions
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73
funasr/models/llm_asr/model.py
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@ -3130,42 +3130,43 @@ class LLMASRXvecSlotTTS(nn.Module):
_text = f"<|endofprompt|><|sil|>{text}" + ("<|sil|>" if is_last else "")
text_token = self.tts_tokenizer_warpper(_text)
text_token = torch.tensor([text_token], dtype=torch.long, device=device)
text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device)
cur_token, feat = self.tts_model.streaming_one_step(
text_token,
text_token_len,
xvec=None,
xvec_lengths=None,
prompt_dict={
"prompt_token": prompt_token,
"prompt_audio": prompt_audio,
},
outside_prompt=llm_cur_kv_cache,
outside_prompt_lengths=llm_cur_kv_cache_len,
sampling="threshold_1e-6",
chunk_idx=chunk_idx,
)
if cur_token is not None and cur_token.shape[1] > 0 and feat.shape[2] > 0:
# process first package, token in B,T,D, feat in B,F,T
if prompt_token[0] is None:
prompt_token = [
cur_token,
torch.tensor([cur_token.shape[1]], dtype=torch.long, device=device),
]
prompt_audio = [
feat.transpose(1, 2),
torch.tensor([feat.shape[2]], dtype=torch.long, device=device),
]
else:
prompt_token[1] = prompt_token[1] + cur_token.shape[1]
prompt_token[0] = torch.concat([prompt_token[0], cur_token], dim=1)
prompt_audio[1] = prompt_audio[1] + feat.shape[2]
prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1)
wav = self.vocoder.inference(feat.transpose(1, 2))
chunk_idx += 1
else:
cur_token, feat, wav = None, None, None
cur_token, feat, wav = None, None, None
if len(text_token) > tts_text_chunk_size:
text_token = torch.tensor([text_token], dtype=torch.long, device=device)
text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device)
cur_token, feat = self.tts_model.streaming_one_step(
text_token,
text_token_len,
xvec=None,
xvec_lengths=None,
prompt_dict={
"prompt_token": prompt_token,
"prompt_audio": prompt_audio,
},
outside_prompt=llm_cur_kv_cache,
outside_prompt_lengths=llm_cur_kv_cache_len,
sampling="threshold_1e-6",
chunk_idx=chunk_idx,
diff_steps=5,
)
if cur_token is not None and cur_token.shape[1] > 0 and feat.shape[2] > 0:
# process first package, token in B,T,D, feat in B,F,T
if prompt_token[0] is None:
prompt_token = [
cur_token,
torch.tensor([cur_token.shape[1]], dtype=torch.long, device=device),
]
prompt_audio = [
feat.transpose(1, 2),
torch.tensor([feat.shape[2]], dtype=torch.long, device=device),
]
else:
prompt_token[1] = prompt_token[1] + cur_token.shape[1]
prompt_token[0] = torch.concat([prompt_token[0], cur_token], dim=1)
prompt_audio[1] = prompt_audio[1] + feat.shape[2]
prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1)
wav = self.vocoder.inference(feat.transpose(1, 2))
chunk_idx += 1
return ((cur_token, feat, wav), (text, last_t_size, prompt_token, prompt_audio, chunk_idx))