update_infer
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@@ -392,6 +392,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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1, 2
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) # .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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@@ -423,9 +424,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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print(i18n("实际输入的目标文本(每句):"), text)
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phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
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bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
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bert = torch.cat([bert1, bert2], 1)
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bert = torch.cat([bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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all_phoneme_ids = torch.LongTensor(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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@@ -435,14 +436,14 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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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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None,
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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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)
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t3 = ttime()
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# print(pred_semantic.shape,idx)
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print(pred_semantic,idx)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(
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0
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) # .unsqueeze(0)#mq要多unsqueeze一次
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@@ -620,7 +621,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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inference_button.click(
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get_tts_wav,
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[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut],
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[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature],
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[output],
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)
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