# Copyright (c) 2021 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. # remain for chains import argparse import logging import os from pathlib import Path import numpy as np import paddle import soundfile as sf import yaml from paddle import jit from paddle.static import InputSpec from yacs.config import CfgNode from paddlespeech.t2s.frontend.zh_frontend import Frontend from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator from paddlespeech.t2s.models.parallel_wavegan import PWGInference from paddlespeech.t2s.models.speedyspeech import SpeedySpeech from paddlespeech.t2s.models.speedyspeech import SpeedySpeechInference from paddlespeech.t2s.modules.normalizer import ZScore def evaluate(args, speedyspeech_config, pwg_config): # dataloader has been too verbose logging.getLogger("DataLoader").disabled = True # construct dataset for evaluation sentences = [] with open(args.text, 'rt') as f: for line in f: items = line.strip().split() utt_id = items[0] sentence = "".join(items[1:]) sentences.append((utt_id, sentence)) with open(args.phones_dict, "r") as f: phn_id = [line.strip().split() for line in f.readlines()] vocab_size = len(phn_id) print("vocab_size:", vocab_size) with open(args.tones_dict, "r") as f: tone_id = [line.strip().split() for line in f.readlines()] tone_size = len(tone_id) print("tone_size:", tone_size) model = SpeedySpeech( vocab_size=vocab_size, tone_size=tone_size, **speedyspeech_config["model"]) model.set_state_dict( paddle.load(args.speedyspeech_checkpoint)["main_params"]) model.eval() vocoder = PWGGenerator(**pwg_config["generator_params"]) vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"]) vocoder.remove_weight_norm() vocoder.eval() print("model done!") stat = np.load(args.speedyspeech_stat) mu, std = stat mu = paddle.to_tensor(mu) std = paddle.to_tensor(std) speedyspeech_normalizer = ZScore(mu, std) stat = np.load(args.pwg_stat) mu, std = stat mu = paddle.to_tensor(mu) std = paddle.to_tensor(std) pwg_normalizer = ZScore(mu, std) speedyspeech_inference = SpeedySpeechInference(speedyspeech_normalizer, model) speedyspeech_inference.eval() speedyspeech_inference = jit.to_static( speedyspeech_inference, input_spec=[ InputSpec([-1], dtype=paddle.int64), InputSpec( [-1], dtype=paddle.int64) ]) paddle.jit.save(speedyspeech_inference, os.path.join(args.inference_dir, "speedyspeech")) speedyspeech_inference = paddle.jit.load( os.path.join(args.inference_dir, "speedyspeech")) pwg_inference = PWGInference(pwg_normalizer, vocoder) pwg_inference.eval() pwg_inference = jit.to_static( pwg_inference, input_spec=[ InputSpec([-1, 80], dtype=paddle.float32), ]) paddle.jit.save(pwg_inference, os.path.join(args.inference_dir, "pwg")) pwg_inference = paddle.jit.load(os.path.join(args.inference_dir, "pwg")) frontend = Frontend( phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict) print("frontend done!") output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) for utt_id, sentence in sentences: input_ids = frontend.get_input_ids( sentence, merge_sentences=True, get_tone_ids=True) phone_ids = input_ids["phone_ids"] tone_ids = input_ids["tone_ids"] flags = 0 for i in range(len(phone_ids)): part_phone_ids = phone_ids[i] part_tone_ids = tone_ids[i] with paddle.no_grad(): mel = speedyspeech_inference(part_phone_ids, part_tone_ids) temp_wav = pwg_inference(mel) if flags == 0: wav = temp_wav flags = 1 else: wav = paddle.concat([wav, temp_wav]) sf.write( output_dir / (utt_id + ".wav"), wav.numpy(), samplerate=speedyspeech_config.fs) print(f"{utt_id} done!") def main(): # parse args and config and redirect to train_sp parser = argparse.ArgumentParser( description="Synthesize with speedyspeech & parallel wavegan.") parser.add_argument( "--speedyspeech-config", type=str, help="config file for speedyspeech.") parser.add_argument( "--speedyspeech-checkpoint", type=str, help="speedyspeech checkpoint to load.") parser.add_argument( "--speedyspeech-stat", type=str, help="mean and standard deviation used to normalize spectrogram when training speedyspeech." ) parser.add_argument( "--pwg-config", type=str, help="config file for parallelwavegan.") parser.add_argument( "--pwg-checkpoint", type=str, help="parallel wavegan checkpoint to load.") parser.add_argument( "--pwg-stat", type=str, help="mean and standard deviation used to normalize spectrogram when training speedyspeech." ) parser.add_argument( "--text", type=str, help="text to synthesize, a 'utt_id sentence' pair per line") parser.add_argument( "--phones-dict", type=str, default=None, help="phone vocabulary file.") parser.add_argument( "--tones-dict", type=str, default=None, help="tone vocabulary file.") parser.add_argument("--output-dir", type=str, help="output dir") parser.add_argument( "--inference-dir", type=str, help="dir to save inference models") parser.add_argument( "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") args, _ = parser.parse_known_args() if args.ngpu == 0: paddle.set_device("cpu") elif args.ngpu > 0: paddle.set_device("gpu") else: print("ngpu should >= 0 !") with open(args.speedyspeech_config) as f: speedyspeech_config = CfgNode(yaml.safe_load(f)) with open(args.pwg_config) as f: pwg_config = CfgNode(yaml.safe_load(f)) print("========Args========") print(yaml.safe_dump(vars(args))) print("========Config========") print(speedyspeech_config) print(pwg_config) evaluate(args, speedyspeech_config, pwg_config) if __name__ == "__main__": main()