blackteay/GPT-SoVITS
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KamioRinn 2024-08-19 21:26:20 +08:00
parent 49427c75bc
commit dbb6b42fdb
1 changed files with 10 additions and 9 deletions
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api.py
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@ -221,7 +221,7 @@ def get_sovits_weights(sovits_path):
hps.model.version = "v1"
else:
hps.model.version = "v2"
print("sovits版本:",hps.model.version)
logger.info(f"模型版本: {hps.model.version}")
model_params_dict = vars(hps.model)
vq_model = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
@ -489,8 +489,7 @@ def pack_raw(audio_bytes, data, rate):
def pack_wav(audio_bytes, rate):
data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
wav_bytes = BytesIO()
sf.write(wav_bytes, data, rate, format='wav')
sf.write(wav_bytes, data, rate, format='WAV')
return wav_bytes
@ -543,6 +542,7 @@ def only_punc(text):
return not any(t.isalnum() or t.isalpha() for t in text)
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, top_k= 15, top_p = 0.6, temperature = 0.6, speed = 1, inp_refs = None, spk = "default"):
infer_sovits = speaker_list[spk].sovits
vq_model = infer_sovits.vq_model
@ -554,6 +554,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
t0 = ttime()
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
prompt_language, text = prompt_language, text.strip("\n")
dtype = torch.float16 if is_half == True else torch.float32
zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
@ -599,6 +600,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
continue
audio_opt = []
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
bert = torch.cat([bert1, bert2], 1)
@ -607,7 +609,6 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
t2 = ttime()
with torch.no_grad():
# pred_semantic = t2s_model.model.infer(
pred_semantic, idx = t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
@ -618,20 +619,20 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
top_p = top_p,
temperature = temperature,
early_stop_num=hz * max_sec)
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
t3 = ttime()
# print(pred_semantic.shape,idx)
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
audio = \
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
refers,speed=speed).detach().cpu().numpy()[
0, 0] ###试试重建不带上prompt部分
max_audio=np.abs(audio).max()#简单防止16bit爆音
if max_audio>1:audio/=max_audio
max_audio=np.abs(audio).max()
if max_audio>1:
audio/=max_audio
audio_opt.append(audio)
audio_opt.append(zero_wav)
t4 = ttime()
audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),hps.data.sampling_rate)
# audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 2147483647).astype(np.int32),hps.data.sampling_rate)
# logger.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
if stream_mode == "normal":
audio_bytes, audio_chunk = read_clean_buffer(audio_bytes)