# 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 tensorboardX import SummaryWriter from scipy.io.wavfile import write from collections import OrderedDict import argparse from pprint import pprint from ruamel import yaml from matplotlib import cm import numpy as np import paddle.fluid as fluid import paddle.fluid.dygraph as dg from parakeet.g2p.en import text_to_sequence from parakeet import audio from parakeet.models.fastspeech.fastspeech import FastSpeech from parakeet.models.transformer_tts.utils import * from parakeet.models.wavenet import WaveNet, UpsampleNet from parakeet.models.clarinet import STFT, Clarinet, ParallelWaveNet from parakeet.modules import weight_norm from parakeet.models.waveflow import WaveFlowModule from parakeet.utils.layer_tools import freeze 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( "--vocoder", type=str, default="griffinlim", choices=['griffinlim', 'clarinet', 'waveflow'], help="vocoder method") parser.add_argument( "--config_vocoder", type=str, help="path of the vocoder config file") parser.add_argument("--use_gpu", type=int, default=0, help="device to use") parser.add_argument( "--alpha", type=float, default=1, help="determine the length of the expanded sequence mel, controlling the voice speed." ) parser.add_argument( "--checkpoint", type=str, help="fastspeech checkpoint to synthesis") 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()) fluid.enable_dygraph(place) 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')) model = FastSpeech(cfg['network'], num_mels=cfg['audio']['num_mels']) # Load parameters. global_step = io.load_parameters( model=model, checkpoint_path=args.checkpoint) model.eval() text = np.asarray(text_to_sequence(text_input)) text = np.expand_dims(text, axis=0) pos_text = np.arange(1, text.shape[1] + 1) pos_text = np.expand_dims(pos_text, axis=0) text = dg.to_variable(text).astype(np.int64) pos_text = dg.to_variable(pos_text).astype(np.int64) _, mel_output_postnet = model(text, pos_text, alpha=args.alpha) if args.vocoder == 'griffinlim': #synthesis use griffin-lim wav = synthesis_with_griffinlim( mel_output_postnet, sr=cfg['audio']['sr'], n_fft=cfg['audio']['n_fft'], num_mels=cfg['audio']['num_mels'], power=cfg['audio']['power'], hop_length=cfg['audio']['hop_length'], win_length=cfg['audio']['win_length']) elif args.vocoder == 'clarinet': # synthesis use clarinet wav = synthesis_with_clarinet(mel_output_postnet, args.config_vocoder, args.checkpoint_vocoder, place) elif args.vocoder == 'waveflow': wav = synthesis_with_waveflow(mel_output_postnet, args, args.checkpoint_vocoder, place) else: print( 'vocoder error, we only support griffinlim, clarinet 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, sr, n_fft, num_mels, power, hop_length, win_length): mel_output = fluid.layers.transpose( fluid.layers.squeeze(mel_output, [0]), [1, 0]) mel_output = np.exp(mel_output.numpy()) basis = librosa.filters.mel(sr, n_fft, num_mels) inv_basis = np.linalg.pinv(basis) spec = np.maximum(1e-10, np.dot(inv_basis, mel_output)) wav = librosa.core.griffinlim( spec**power, hop_length=hop_length, win_length=win_length) return wav def synthesis_with_clarinet(mel_output, config_path, checkpoint, place): mel_spectrogram = np.exp(mel_output.numpy()) with open(config_path, 'rt') as f: config = yaml.safe_load(f) data_config = config["data"] n_mels = data_config["n_mels"] teacher_config = config["teacher"] n_loop = teacher_config["n_loop"] n_layer = teacher_config["n_layer"] filter_size = teacher_config["filter_size"] # only batch=1 for validation is enabled fluid.enable_dygraph(place) # conditioner(upsampling net) conditioner_config = config["conditioner"] upsampling_factors = conditioner_config["upsampling_factors"] upsample_net = UpsampleNet(upscale_factors=upsampling_factors) freeze(upsample_net) residual_channels = teacher_config["residual_channels"] loss_type = teacher_config["loss_type"] output_dim = teacher_config["output_dim"] log_scale_min = teacher_config["log_scale_min"] assert loss_type == "mog" and output_dim == 3, \ "the teacher wavenet should be a wavenet with single gaussian output" teacher = WaveNet(n_loop, n_layer, residual_channels, output_dim, n_mels, filter_size, loss_type, log_scale_min) # load & freeze upsample_net & teacher freeze(teacher) student_config = config["student"] n_loops = student_config["n_loops"] n_layers = student_config["n_layers"] student_residual_channels = student_config["residual_channels"] student_filter_size = student_config["filter_size"] student_log_scale_min = student_config["log_scale_min"] student = ParallelWaveNet(n_loops, n_layers, student_residual_channels, n_mels, student_filter_size) stft_config = config["stft"] stft = STFT( n_fft=stft_config["n_fft"], hop_length=stft_config["hop_length"], win_length=stft_config["win_length"]) lmd = config["loss"]["lmd"] model = Clarinet(upsample_net, teacher, student, stft, student_log_scale_min, lmd) io.load_parameters(model=model, checkpoint_path=checkpoint) if not os.path.exists(args.output): os.makedirs(args.output) model.eval() # Rescale mel_spectrogram. min_level, ref_level = 1e-5, 20 # hard code it mel_spectrogram = 20 * np.log10(np.maximum(min_level, mel_spectrogram)) mel_spectrogram = mel_spectrogram - ref_level mel_spectrogram = np.clip((mel_spectrogram + 100) / 100, 0, 1) mel_spectrogram = dg.to_variable(mel_spectrogram) mel_spectrogram = fluid.layers.transpose(mel_spectrogram, [0, 2, 1]) wav_var = model.synthesis(mel_spectrogram) wav_np = wav_var.numpy()[0] return wav_np def synthesis_with_waveflow(mel_output, args, checkpoint, place): #mel_output = np.exp(mel_output.numpy()) mel_output = mel_output.numpy() fluid.enable_dygraph(place) args.config = args.config_vocoder args.use_fp16 = False config = io.add_yaml_config_to_args(args) mel_spectrogram = dg.to_variable(mel_output) mel_spectrogram = fluid.layers.transpose(mel_spectrogram, [0, 2, 1]) # Build model. waveflow = WaveFlowModule(config) io.load_parameters(model=waveflow, checkpoint_path=checkpoint) for layer in waveflow.sublayers(): if isinstance(layer, weight_norm.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() pprint(vars(args)) synthesis("Simple as this proposition is, it is necessary to be stated,", args)