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Update api.py
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api.py
9
api.py
@ -192,13 +192,18 @@ 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) # 派蒙
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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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@ -209,7 +214,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_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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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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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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