mirror of https://github.com/coqui-ai/TTS.git
update synthesize.py
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erogol
parent
69f525f17d
commit
7080b5fb34
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@ -1,53 +1,41 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import argparse
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import json
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# pylint: disable=redefined-outer-name, unused-argument
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import os
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import time
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import argparse
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import torch
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import json
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import string
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import time
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import torch
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from TTS.tts.utils.synthesis import synthesis
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from TTS.tts.utils.generic_utils import setup_model
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from TTS.tts.utils.io import load_config
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from TTS.tts.utils.text.symbols import make_symbols, symbols, phonemes
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from TTS.tts.utils.audio import AudioProcessor
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from TTS.tts.utils.synthesis import synthesis
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from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.io import load_config
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from TTS.vocoder.utils.generic_utils import setup_generator
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def tts(model,
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vocoder_model,
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C,
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VC,
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text,
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ap,
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ap_vocoder,
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use_cuda,
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batched_vocoder,
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speaker_id=None,
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figures=False):
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def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_id):
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t_1 = time.time()
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use_vocoder_model = vocoder_model is not None
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waveform, alignment, _, postnet_output, stop_tokens, _ = synthesis(
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model, text, C, use_cuda, ap, speaker_id, style_wav=False,
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truncated=False, enable_eos_bos_chars=C.enable_eos_bos_chars,
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use_griffin_lim=(not use_vocoder_model), do_trim_silence=True)
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if C.model == "Tacotron" and use_vocoder_model:
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postnet_output = ap.out_linear_to_mel(postnet_output.T).T
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# correct if there is a scale difference b/w two models
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if use_vocoder_model:
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postnet_output = ap._denormalize(postnet_output)
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postnet_output = ap_vocoder._normalize(postnet_output)
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vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
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waveform = vocoder_model.generate(
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vocoder_input.cuda() if use_cuda else vocoder_input,
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batched=batched_vocoder,
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target=8000,
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overlap=400)
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print(" > Run-time: {}".format(time.time() - t_1))
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return alignment, postnet_output, stop_tokens, waveform
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waveform, _, _, mel_postnet_spec, stop_tokens, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, None, False, CONFIG.enable_eos_bos_chars, use_gl)
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if CONFIG.model == "Tacotron" and not use_gl:
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mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T
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if not use_gl:
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waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))
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if use_cuda and not use_gl:
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waveform = waveform.cpu()
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if not use_gl:
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waveform = waveform.numpy()
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waveform = waveform.squeeze()
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rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)
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tps = (time.time() - t_1) / len(waveform)
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print(" > Run-time: {}".format(time.time() - t_1))
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print(" > Real-time factor: {}".format(rtf))
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print(" > Time per step: {}".format(tps))
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return waveform
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if __name__ == "__main__":
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@ -100,10 +88,6 @@ if __name__ == "__main__":
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default=None)
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args = parser.parse_args()
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if args.vocoder_path != "":
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assert args.use_cuda, " [!] Enable cuda for vocoder."
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from WaveRNN.models.wavernn import Model as VocoderModel
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# load the config
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C = load_config(args.config_path)
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C.forward_attn_mask = True
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@ -125,7 +109,7 @@ if __name__ == "__main__":
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# load the model
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num_chars = len(phonemes) if C.use_phonemes else len(symbols)
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model = setup_model(num_chars, num_speakers, C)
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cp = torch.load(args.model_path)
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cp = torch.load(args.model_path, map_location=torch.device('cpu'))
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model.load_state_dict(cp['model'])
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model.eval()
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if args.use_cuda:
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@ -135,46 +119,20 @@ if __name__ == "__main__":
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# load vocoder model
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if args.vocoder_path != "":
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VC = load_config(args.vocoder_config_path)
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ap_vocoder = AudioProcessor(**VC.audio)
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bits = 10
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vocoder_model = VocoderModel(rnn_dims=512,
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fc_dims=512,
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mode=VC.mode,
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mulaw=VC.mulaw,
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pad=VC.pad,
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upsample_factors=VC.upsample_factors,
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feat_dims=VC.audio["num_mels"],
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compute_dims=128,
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res_out_dims=128,
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res_blocks=10,
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hop_length=ap.hop_length,
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sample_rate=ap.sample_rate,
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use_aux_net=True,
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use_upsample_net=True)
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check = torch.load(args.vocoder_path)
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vocoder_model.load_state_dict(check['model'])
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vocoder_model.eval()
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vocoder_model = setup_generator(VC)
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vocoder_model.load_state_dict(torch.load(args.vocoder_path, map_location="cpu")["model"])
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vocoder_model.remove_weight_norm()
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if args.use_cuda:
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vocoder_model.cuda()
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vocoder_model.eval()
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else:
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vocoder_model = None
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VC = None
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ap_vocoder = None
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# synthesize voice
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use_griffin_lim = args.vocoder_path == ""
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print(" > Text: {}".format(args.text))
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_, _, _, wav = tts(model,
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vocoder_model,
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C,
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VC,
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args.text,
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ap,
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ap_vocoder,
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args.use_cuda,
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args.batched_vocoder,
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speaker_id=args.speaker_id,
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figures=False)
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wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_id)
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# save the results
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file_name = args.text.replace(" ", "_")
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@ -121,6 +121,7 @@ class ParallelWaveganGenerator(torch.nn.Module):
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return x
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@torch.no_grad()
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def inference(self, c):
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c = c.to(self.first_conv.weight.device)
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c = torch.nn.functional.pad(
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