Medix-AI/coqui-tts
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coqui-tts
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create inference function

This commit is contained in:
Edresson 2021-04-29 18:18:37 -03:00
parent 20e42a3381
commit 446b1da936
1 changed files with 44 additions and 40 deletions
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84
TTS/bin/extract_tts_spectrograms.py
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@ -124,6 +124,49 @@ def format_data(data):
)
@torch.no_grad()
def inference(model_name, model, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask=None, speaker_ids=None, speaker_embeddings=None):
if model_name == "glow_tts":
mel_input = mel_input.permute(0, 2, 1) # B x D x T
speaker_c = None
if speaker_ids is not None:
speaker_c = speaker_ids
elif speaker_embeddings is not None:
speaker_c = speaker_embeddings
model_output, _, _, _, _, _, _ = model.inference_with_MAS(
text_input, text_lengths, mel_input, mel_lengths, attn_mask, g=speaker_c
)
model_output = model_output.transpose(1, 2).detach().cpu().numpy()
elif "tacotron" in model_name:
if c.bidirectional_decoder or c.double_decoder_consistency:
(
_,
postnet_outputs,
_,
_,
_,
_,
) = model(
text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings
)
else:
_, postnet_outputs, _, _ = model(
text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings
)
# normalize tacotron output
if model_name == "tacotron":
mel_specs = []
postnet_outputs = postnet_outputs.data.cpu().numpy()
for b in range(postnet_outputs.shape[0]):
postnet_output = postnet_outputs[b]
mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())
model_output = torch.stack(mel_specs)
elif model_name == "tacotron2":
model_output = postnet_outputs.detach().cpu().numpy()
return model_output
def extract_spectrograms(data_loader, model, ap, output_path, quantized_wav=False, save_audio=False, debug=False, metada_name="metada.txt"):
model.eval()
export_metadata = []
@ -143,46 +186,7 @@ def extract_spectrograms(data_loader, model, ap, output_path, quantized_wav=Fals
item_idx,
) = format_data(data)
if c.model.lower() == "glow_tts":
mel_input = mel_input.permute(0, 2, 1) # B x D x T
speaker_c = None
if speaker_ids is not None:
speaker_c = speaker_ids
elif speaker_embeddings is not None:
speaker_c = speaker_embeddings
model_output, _, _, _, _, _, _ = model.inference_with_MAS(
text_input, text_lengths, mel_input, mel_lengths, attn_mask, g=speaker_c
)
model_output = model_output.transpose(1, 2).detach().cpu().numpy()
elif "tacotron" in c.model.lower():
if c.bidirectional_decoder or c.double_decoder_consistency:
(
_,
postnet_outputs,
_,
_,
_,
_,
) = model(
text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings
)
else:
_, postnet_outputs, _, _ = model(
text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings
)
# normalize tacotron output
if c.model.lower() == "tacotron":
mel_specs = []
postnet_outputs = postnet_outputs.data.cpu().numpy()
for b in range(postnet_outputs.shape[0]):
postnet_output = postnet_outputs[b]
mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())
model_output = torch.stack(mel_specs)
elif c.model.lower() == "tacotron2":
model_output = postnet_outputs.detach().cpu().numpy()
model_output = inference(c.model.lower(), model, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask, speaker_ids, speaker_embeddings)
for idx in range(text_input.shape[0]):
wav_file_path = item_idx[idx]