101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
import os
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from scipy.io.wavfile import write
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from parakeet.g2p.en import text_to_sequence
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import numpy as np
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from tqdm import tqdm
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from tensorboardX import SummaryWriter
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from ruamel import yaml
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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from pathlib import Path
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import argparse
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from parse import add_config_options_to_parser
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from pprint import pprint
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from collections import OrderedDict
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from parakeet import audio
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from parakeet.models.transformer_tts.vocoder import Vocoder
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from parakeet.models.transformer_tts.transformer_tts import TransformerTTS
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def load_checkpoint(step, model_path):
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model_dict, _ = fluid.dygraph.load_dygraph(os.path.join(model_path, step))
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new_state_dict = OrderedDict()
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for param in model_dict:
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if param.startswith('_layers.'):
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new_state_dict[param[8:]] = model_dict[param]
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else:
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new_state_dict[param] = model_dict[param]
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return new_state_dict
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def synthesis(text_input, args):
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place = (fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace())
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with open(args.config_path) as f:
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cfg = yaml.load(f, Loader=yaml.Loader)
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# tensorboard
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if not os.path.exists(args.log_dir):
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os.mkdir(args.log_dir)
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path = os.path.join(args.log_dir,'synthesis')
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writer = SummaryWriter(path)
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with dg.guard(place):
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with fluid.unique_name.guard():
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model = TransformerTTS(cfg)
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model.set_dict(load_checkpoint(str(args.transformer_step), os.path.join(args.checkpoint_path, "nostop_token/transformer")))
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model.eval()
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with fluid.unique_name.guard():
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model_postnet = Vocoder(cfg, args.batch_size)
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model_postnet.set_dict(load_checkpoint(str(args.postnet_step), os.path.join(args.checkpoint_path, "postnet")))
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model_postnet.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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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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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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mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(text, mel_input, pos_text, pos_mel)
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mel_input = fluid.layers.concat([mel_input, postnet_pred[:,-1:,:]], axis=1)
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mag_pred = model_postnet(postnet_pred)
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_ljspeech_processor = audio.AudioProcessor(
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sample_rate=cfg['audio']['sr'],
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num_mels=cfg['audio']['num_mels'],
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min_level_db=cfg['audio']['min_level_db'],
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ref_level_db=cfg['audio']['ref_level_db'],
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n_fft=cfg['audio']['n_fft'],
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win_length= cfg['audio']['win_length'],
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hop_length= cfg['audio']['hop_length'],
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power=cfg['audio']['power'],
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preemphasis=cfg['audio']['preemphasis'],
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signal_norm=True,
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symmetric_norm=False,
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max_norm=1.,
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mel_fmin=0,
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mel_fmax=None,
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clip_norm=True,
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griffin_lim_iters=60,
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do_trim_silence=False,
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sound_norm=False)
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wav = _ljspeech_processor.inv_spectrogram(fluid.layers.transpose(fluid.layers.squeeze(mag_pred,[0]), [1,0]).numpy())
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writer.add_audio(text_input, wav, 0, cfg['audio']['sr'])
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if not os.path.exists(args.sample_path):
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os.mkdir(args.sample_path)
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write(os.path.join(args.sample_path,'test.wav'), cfg['audio']['sr'], wav)
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writer.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Synthesis model")
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add_config_options_to_parser(parser)
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args = parser.parse_args()
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synthesis("Transformer model is so fast!", args)
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