Merge branch 'fix' into 'master'
fix synthesis for transformerTTS and FastSpeech, use int64 explicitly See merge request !60
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4eeece4158
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@ -83,8 +83,8 @@ def synthesis(text_input, args):
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pos_text = np.arange(1, text.shape[1] + 1)
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pos_text = np.expand_dims(pos_text, axis=0)
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text = dg.to_variable(text)
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pos_text = dg.to_variable(pos_text)
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text = dg.to_variable(text).astype(np.int64)
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pos_text = dg.to_variable(pos_text).astype(np.int64)
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_, mel_output_postnet = model(text, pos_text, alpha=args.alpha)
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@ -92,15 +92,17 @@ def synthesis(text_input, args):
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model_vocoder.eval()
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# init input
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text = np.asarray(text_to_sequence(text_input))
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text = fluid.layers.unsqueeze(dg.to_variable(text), [0])
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text = fluid.layers.unsqueeze(dg.to_variable(text).astype(np.int64), [0])
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mel_input = dg.to_variable(np.zeros([1, 1, 80])).astype(np.float32)
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pos_text = np.arange(1, text.shape[1] + 1)
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pos_text = fluid.layers.unsqueeze(dg.to_variable(pos_text), [0])
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pos_text = fluid.layers.unsqueeze(
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dg.to_variable(pos_text).astype(np.int64), [0])
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pbar = tqdm(range(args.max_len))
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for i in pbar:
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pos_mel = np.arange(1, mel_input.shape[1] + 1)
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pos_mel = fluid.layers.unsqueeze(dg.to_variable(pos_mel), [0])
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pos_mel = fluid.layers.unsqueeze(
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dg.to_variable(pos_mel).astype(np.int64), [0])
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mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
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text, mel_input, pos_text, pos_mel)
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mel_input = fluid.layers.concat(
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