# 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. import argparse from pathlib import Path import numpy import soundfile as sf from paddle import inference from paddlespeech.t2s.frontend import English from paddlespeech.t2s.frontend.zh_frontend import Frontend # only inference for models trained with csmsc now def main(): parser = argparse.ArgumentParser( description="Paddle Infernce with speedyspeech & parallel wavegan.") # acoustic model parser.add_argument( '--am', type=str, default='fastspeech2_csmsc', choices=[ 'speedyspeech_csmsc', 'fastspeech2_csmsc', 'fastspeech2_aishell3', 'fastspeech2_vctk' ], help='Choose acoustic model type of tts task.') 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( "--speaker_dict", type=str, default=None, help="speaker id map file.") parser.add_argument( '--spk_id', type=int, default=0, help='spk id for multi speaker acoustic model') # voc parser.add_argument( '--voc', type=str, default='pwgan_csmsc', choices=[ 'pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc', 'pwgan_aishell3', 'pwgan_vctk' ], help='Choose vocoder type of tts task.') # other parser.add_argument( '--lang', type=str, default='zh', help='Choose model language. zh or en') parser.add_argument( "--text", type=str, help="text to synthesize, a 'utt_id sentence' pair per line") parser.add_argument( "--inference_dir", type=str, help="dir to save inference models") parser.add_argument("--output_dir", type=str, help="output dir") args, _ = parser.parse_known_args() # frontend if args.lang == 'zh': frontend = Frontend( phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict) elif args.lang == 'en': frontend = English(phone_vocab_path=args.phones_dict) print("frontend done!") # model: {model_name}_{dataset} am_name = args.am[:args.am.rindex('_')] am_dataset = args.am[args.am.rindex('_') + 1:] am_config = inference.Config( str(Path(args.inference_dir) / (args.am + ".pdmodel")), str(Path(args.inference_dir) / (args.am + ".pdiparams"))) am_config.enable_use_gpu(100, 0) # This line must be commented for fastspeech2, if not, it will OOM if am_name != 'fastspeech2': am_config.enable_memory_optim() am_predictor = inference.create_predictor(am_config) voc_config = inference.Config( str(Path(args.inference_dir) / (args.voc + ".pdmodel")), str(Path(args.inference_dir) / (args.voc + ".pdiparams"))) voc_config.enable_use_gpu(100, 0) voc_config.enable_memory_optim() voc_predictor = inference.create_predictor(voc_config) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) sentences = [] print("in new inference") # construct dataset for evaluation sentences = [] with open(args.text, 'rt') as f: for line in f: items = line.strip().split() utt_id = items[0] if args.lang == 'zh': sentence = "".join(items[1:]) elif args.lang == 'en': sentence = " ".join(items[1:]) sentences.append((utt_id, sentence)) get_tone_ids = False get_spk_id = False if am_name == 'speedyspeech': get_tone_ids = True if am_dataset in {"aishell3", "vctk"} and args.speaker_dict: get_spk_id = True spk_id = numpy.array([args.spk_id]) am_input_names = am_predictor.get_input_names() print("am_input_names:", am_input_names) merge_sentences = True for utt_id, sentence in sentences: if args.lang == 'zh': input_ids = frontend.get_input_ids( sentence, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids) phone_ids = input_ids["phone_ids"] elif args.lang == 'en': input_ids = frontend.get_input_ids( sentence, merge_sentences=merge_sentences) phone_ids = input_ids["phone_ids"] else: print("lang should in {'zh', 'en'}!") if get_tone_ids: tone_ids = input_ids["tone_ids"] tones = tone_ids[0].numpy() tones_handle = am_predictor.get_input_handle(am_input_names[1]) tones_handle.reshape(tones.shape) tones_handle.copy_from_cpu(tones) if get_spk_id: spk_id_handle = am_predictor.get_input_handle(am_input_names[1]) spk_id_handle.reshape(spk_id.shape) spk_id_handle.copy_from_cpu(spk_id) phones = phone_ids[0].numpy() phones_handle = am_predictor.get_input_handle(am_input_names[0]) phones_handle.reshape(phones.shape) phones_handle.copy_from_cpu(phones) am_predictor.run() am_output_names = am_predictor.get_output_names() am_output_handle = am_predictor.get_output_handle(am_output_names[0]) am_output_data = am_output_handle.copy_to_cpu() voc_input_names = voc_predictor.get_input_names() mel_handle = voc_predictor.get_input_handle(voc_input_names[0]) mel_handle.reshape(am_output_data.shape) mel_handle.copy_from_cpu(am_output_data) voc_predictor.run() voc_output_names = voc_predictor.get_output_names() voc_output_handle = voc_predictor.get_output_handle(voc_output_names[0]) wav = voc_output_handle.copy_to_cpu() sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000) print(f"{utt_id} done!") if __name__ == "__main__": main()