mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 07:02:57 +08:00
Merge 4afa9bf4733c26fea7ee8389962c6d9faab15500 into 7fc216107baadebc6fd5ae4d9c6ad9e0f8bb5f7c
This commit is contained in:
刘悦
GitHub
commit
1ccbfc5f55
@ -365,7 +365,7 @@ def merge_short_text_in_array(texts, threshold):
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result[len(result) - 1] += text
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return result
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6):
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=5, top_p=1, temperature=1):
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t0 = ttime()
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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@ -591,7 +591,9 @@ def get_weights_names():
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SoVITS_names, GPT_names = get_weights_names()
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with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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def main():
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with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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gr.Markdown(
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value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
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)
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@ -652,10 +654,13 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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button5.click(cut5, [text_inp], [text_opt])
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gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。"))
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app.queue(concurrency_count=511, max_size=1022).launch(
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app.queue(concurrency_count=511, max_size=1022).launch(
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server_name="0.0.0.0",
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inbrowser=True,
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share=is_share,
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server_port=infer_ttswebui,
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quiet=True,
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)
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)
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if __name__ == '__main__':
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main()
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81
api.py
81
api.py
@ -30,7 +30,7 @@ endpoint: `/`
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使用执行参数指定的参考音频:
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GET:
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`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
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`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=中文`
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POST:
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```json
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{
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@ -41,7 +41,7 @@ POST:
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手动指定当次推理所使用的参考音频:
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GET:
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`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
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`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=中文&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=中文`
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POST:
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```json
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{
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@ -129,6 +129,7 @@ from text.cleaner import clean_text
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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import config as global_config
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from inference_webui import get_tts_wav
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g_config = global_config.Config()
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@ -316,82 +317,6 @@ dict_language = {
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}
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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prompt_language, text = prompt_language, text.strip("\n")
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zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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wav16k = torch.from_numpy(wav16k)
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zero_wav_torch = torch.from_numpy(zero_wav)
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if (is_half == True):
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wav16k = wav16k.half().to(device)
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zero_wav_torch = zero_wav_torch.half().to(device)
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else:
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wav16k = wav16k.to(device)
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zero_wav_torch = zero_wav_torch.to(device)
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wav16k = torch.cat([wav16k, zero_wav_torch])
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
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codes = vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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t1 = ttime()
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
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phones1 = cleaned_text_to_sequence(phones1)
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texts = text.split("\n")
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audio_opt = []
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for text in texts:
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phones2, word2ph2, norm_text2 = clean_text(text, text_language)
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phones2 = cleaned_text_to_sequence(phones2)
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if (prompt_language == "zh"):
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bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
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else:
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bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
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device)
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if (text_language == "zh"):
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bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
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else:
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bert2 = torch.zeros((1024, len(phones2))).to(bert1)
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
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prompt = prompt_semantic.unsqueeze(0).to(device)
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t2 = ttime()
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with torch.no_grad():
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# pred_semantic = t2s_model.model.infer(
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pred_semantic, idx = t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_len,
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prompt,
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bert,
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# prompt_phone_len=ph_offset,
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top_k=config['inference']['top_k'],
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early_stop_num=hz * max_sec)
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t3 = ttime()
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# print(pred_semantic.shape,idx)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
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refer = get_spepc(hps, ref_wav_path) # .to(device)
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if (is_half == True):
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refer = refer.half().to(device)
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else:
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refer = refer.to(device)
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# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
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audio = \
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vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
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refer).detach().cpu().numpy()[
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0, 0] ###试试重建不带上prompt部分
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audio_opt.append(audio)
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audio_opt.append(zero_wav)
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t4 = ttime()
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
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def handle_control(command):
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if command == "restart":
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os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
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