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Update app.py
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app.py
CHANGED
@@ -28,8 +28,8 @@ def predict(text, speaker):
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inputs = processor(text=text, return_tensors="pt")
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# limit input length
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if speaker == "Surprise Me!":
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# load one of the provided speaker embeddings at random
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@@ -51,8 +51,8 @@ def predict(text, speaker):
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#speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
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speaker_embedding = torch.tensor(speaker_embedding) #the saved model is already unsqueezed, but is not a tensor, so make it one
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speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
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speech = (speech.numpy() * 32767).astype(np.int16)
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return (16000, speech)
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inputs = processor(text=text, return_tensors="pt")
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# limit input length
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input_ids = inputs["input_ids"]
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input_ids = input_ids[..., :model.config.max_text_positions]
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if speaker == "Surprise Me!":
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# load one of the provided speaker embeddings at random
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#speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
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speaker_embedding = torch.tensor(speaker_embedding) #the saved model is already unsqueezed, but is not a tensor, so make it one
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speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)
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#speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
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speech = (speech.numpy() * 32767).astype(np.int16)
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return (16000, speech)
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