# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from scipy.io.wavfile import write import numpy as np from tqdm import tqdm from matplotlib import cm from tensorboardX import SummaryWriter from ruamel import yaml from pathlib import Path import argparse from pprint import pprint import paddle.fluid as fluid import paddle.fluid.dygraph as dg from parakeet.g2p.en import text_to_sequence from parakeet.models.transformer_tts.utils import * from parakeet.models.transformer_tts import TransformerTTS from parakeet.models.waveflow import WaveFlowModule from parakeet.modules.weight_norm import WeightNormWrapper from parakeet.utils import io def add_config_options_to_parser(parser): parser.add_argument("--config", type=str, help="path of the config file") parser.add_argument("--use_gpu", type=int, default=0, help="device to use") parser.add_argument( "--stop_threshold", type=float, default=0.5, help="The threshold of stop token which indicates the time step should stop generate spectrum or not." ) parser.add_argument( "--max_len", type=int, default=1000, help="The max length of audio when synthsis.") parser.add_argument( "--checkpoint_transformer", type=str, help="transformer_tts checkpoint to synthesis") parser.add_argument( "--vocoder", type=str, default="griffinlim", choices=['griffinlim', 'waveflow'], help="vocoder method") parser.add_argument( "--config_vocoder", type=str, help="path of the vocoder config file") parser.add_argument( "--checkpoint_vocoder", type=str, help="vocoder checkpoint to synthesis") parser.add_argument( "--output", type=str, default="synthesis", help="path to save experiment results") def synthesis(text_input, args): local_rank = dg.parallel.Env().local_rank place = (fluid.CUDAPlace(local_rank) if args.use_gpu else fluid.CPUPlace()) with open(args.config) as f: cfg = yaml.load(f, Loader=yaml.Loader) # tensorboard if not os.path.exists(args.output): os.mkdir(args.output) writer = SummaryWriter(os.path.join(args.output, 'log')) fluid.enable_dygraph(place) with fluid.unique_name.guard(): network_cfg = cfg['network'] model = TransformerTTS( network_cfg['embedding_size'], network_cfg['hidden_size'], network_cfg['encoder_num_head'], network_cfg['encoder_n_layers'], cfg['audio']['num_mels'], network_cfg['outputs_per_step'], network_cfg['decoder_num_head'], network_cfg['decoder_n_layers']) # Load parameters. global_step = io.load_parameters( model=model, checkpoint_path=args.checkpoint_transformer) model.eval() # init input text = np.asarray(text_to_sequence(text_input)) 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) pos_text = np.arange(1, text.shape[1] + 1) pos_text = fluid.layers.unsqueeze( dg.to_variable(pos_text).astype(np.int64), [0]) for i in range(args.max_len): pos_mel = np.arange(1, mel_input.shape[1] + 1) 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( text, mel_input, pos_text, pos_mel) if stop_preds.numpy()[0, -1] > args.stop_threshold: break mel_input = fluid.layers.concat( [mel_input, postnet_pred[:, -1:, :]], axis=1) global_step = 0 for i, prob in enumerate(attn_probs): for j in range(4): x = np.uint8(cm.viridis(prob.numpy()[j]) * 255) writer.add_image( 'Attention_%d_0' % global_step, x, i * 4 + j, dataformats="HWC") if args.vocoder == 'griffinlim': #synthesis use griffin-lim wav = synthesis_with_griffinlim(postnet_pred, cfg['audio']) elif args.vocoder == 'waveflow': # synthesis use waveflow wav = synthesis_with_waveflow(postnet_pred, args, args.checkpoint_vocoder, place) else: print( 'vocoder error, we only support griffinlim and waveflow, but recevied %s.' % args.vocoder) writer.add_audio(text_input + '(' + args.vocoder + ')', wav, 0, cfg['audio']['sr']) if not os.path.exists(os.path.join(args.output, 'samples')): os.mkdir(os.path.join(args.output, 'samples')) write( os.path.join( os.path.join(args.output, 'samples'), args.vocoder + '.wav'), cfg['audio']['sr'], wav) print("Synthesis completed !!!") writer.close() def synthesis_with_griffinlim(mel_output, cfg): # synthesis with griffin-lim mel_output = fluid.layers.transpose( fluid.layers.squeeze(mel_output, [0]), [1, 0]) mel_output = np.exp(mel_output.numpy()) basis = librosa.filters.mel(cfg['sr'], cfg['n_fft'], cfg['num_mels'], fmin=cfg['fmin'], fmax=cfg['fmax']) inv_basis = np.linalg.pinv(basis) spec = np.maximum(1e-10, np.dot(inv_basis, mel_output)) wav = librosa.core.griffinlim( spec**cfg['power'], hop_length=cfg['hop_length'], win_length=cfg['win_length']) return wav def synthesis_with_waveflow(mel_output, args, checkpoint, place): fluid.enable_dygraph(place) args.config = args.config_vocoder args.use_fp16 = False config = io.add_yaml_config_to_args(args) mel_spectrogram = fluid.layers.transpose( fluid.layers.squeeze(mel_output, [0]), [1, 0]) mel_spectrogram = fluid.layers.unsqueeze(mel_spectrogram, [0]) # Build model. waveflow = WaveFlowModule(config) io.load_parameters(model=waveflow, checkpoint_path=checkpoint) for layer in waveflow.sublayers(): if isinstance(layer, WeightNormWrapper): layer.remove_weight_norm() # Run model inference. wav = waveflow.synthesize(mel_spectrogram, sigma=config.sigma) return wav.numpy()[0] if __name__ == '__main__': parser = argparse.ArgumentParser(description="Synthesis model") add_config_options_to_parser(parser) args = parser.parse_args() # Print the whole config setting. pprint(vars(args)) synthesis( "Life was like a box of chocolates, you never know what you're gonna get.", args)