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Merge branch 'fix' into 'master' 

fix synthesis for transformerTTS and FastSpeech, use int64 explicitly

See merge request !60
This commit is contained in:
liuyibing01 2020-06-12 18:14:48 +08:00
parent 11f94b4f63 9dad6c3d41
commit 4eeece4158
2 changed files with 7 additions and 5 deletions
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4
examples/fastspeech/synthesis.py
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@ -83,8 +83,8 @@ def synthesis(text_input, args):
pos_text = np.arange(1, text.shape[1] + 1) pos_text = np.arange(1, text.shape[1] + 1)
pos_text = np.expand_dims(pos_text, axis=0) pos_text = np.expand_dims(pos_text, axis=0)
text = dg.to_variable(text) text = dg.to_variable(text).astype(np.int64)
pos_text = dg.to_variable(pos_text) pos_text = dg.to_variable(pos_text).astype(np.int64)
_, mel_output_postnet = model(text, pos_text, alpha=args.alpha) _, mel_output_postnet = model(text, pos_text, alpha=args.alpha)

8
examples/transformer_tts/synthesis.py
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@ -92,15 +92,17 @@ def synthesis(text_input, args):
model_vocoder.eval() model_vocoder.eval()
# init input # init input
text = np.asarray(text_to_sequence(text_input)) text = np.asarray(text_to_sequence(text_input))
text = fluid.layers.unsqueeze(dg.to_variable(text), [0]) text = fluid.layers.unsqueeze(dg.to_variable(text).astype(np.int64), [0])
mel_input = dg.to_variable(np.zeros([1, 1, 80])).astype(np.float32) mel_input = dg.to_variable(np.zeros([1, 1, 80])).astype(np.float32)
pos_text = np.arange(1, text.shape[1] + 1) pos_text = np.arange(1, text.shape[1] + 1)
pos_text = fluid.layers.unsqueeze(dg.to_variable(pos_text), [0]) pos_text = fluid.layers.unsqueeze(
dg.to_variable(pos_text).astype(np.int64), [0])
pbar = tqdm(range(args.max_len)) pbar = tqdm(range(args.max_len))
for i in pbar: for i in pbar:
pos_mel = np.arange(1, mel_input.shape[1] + 1) pos_mel = np.arange(1, mel_input.shape[1] + 1)
pos_mel = fluid.layers.unsqueeze(dg.to_variable(pos_mel), [0]) pos_mel = fluid.layers.unsqueeze(
dg.to_variable(pos_mel).astype(np.int64), [0])
mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model( mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
text, mel_input, pos_text, pos_mel) text, mel_input, pos_text, pos_mel)
mel_input = fluid.layers.concat( mel_input = fluid.layers.concat(