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184 lines
6.5 KiB
184 lines
6.5 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from pathlib import Path
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import numpy
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import soundfile as sf
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from paddle import inference
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from paddlespeech.t2s.frontend import English
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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# only inference for models trained with csmsc now
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def main():
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parser = argparse.ArgumentParser(
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description="Paddle Infernce with speedyspeech & parallel wavegan.")
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# acoustic model
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'speedyspeech_csmsc', 'fastspeech2_csmsc', 'fastspeech2_aishell3',
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'fastspeech2_vctk', 'tacotron2_csmsc'
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],
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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parser.add_argument(
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"--speaker_dict", type=str, default=None, help="speaker id map file.")
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parser.add_argument(
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'--spk_id',
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type=int,
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default=0,
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help='spk id for multi speaker acoustic model')
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# voc
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parser.add_argument(
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'--voc',
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type=str,
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default='pwgan_csmsc',
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choices=[
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'pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc', 'pwgan_aishell3',
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'pwgan_vctk', 'wavernn_csmsc'
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],
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help='Choose vocoder type of tts task.')
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# other
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parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en')
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line")
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parser.add_argument(
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"--inference_dir", type=str, help="dir to save inference models")
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parser.add_argument("--output_dir", type=str, help="output dir")
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args, _ = parser.parse_known_args()
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# frontend
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if args.lang == 'zh':
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frontend = Frontend(
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phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
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elif args.lang == 'en':
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frontend = English(phone_vocab_path=args.phones_dict)
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print("frontend done!")
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# model: {model_name}_{dataset}
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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am_config = inference.Config(
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str(Path(args.inference_dir) / (args.am + ".pdmodel")),
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str(Path(args.inference_dir) / (args.am + ".pdiparams")))
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am_config.enable_use_gpu(100, 0)
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# This line must be commented for fastspeech2, if not, it will OOM
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if am_name != 'fastspeech2':
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am_config.enable_memory_optim()
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am_predictor = inference.create_predictor(am_config)
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voc_config = inference.Config(
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str(Path(args.inference_dir) / (args.voc + ".pdmodel")),
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str(Path(args.inference_dir) / (args.voc + ".pdiparams")))
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voc_config.enable_use_gpu(100, 0)
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voc_config.enable_memory_optim()
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voc_predictor = inference.create_predictor(voc_config)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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sentences = []
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print("in new inference")
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# construct dataset for evaluation
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sentences = []
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with open(args.text, 'rt') as f:
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for line in f:
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items = line.strip().split()
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utt_id = items[0]
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if args.lang == 'zh':
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sentence = "".join(items[1:])
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elif args.lang == 'en':
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sentence = " ".join(items[1:])
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sentences.append((utt_id, sentence))
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get_tone_ids = False
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get_spk_id = False
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if am_name == 'speedyspeech':
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get_tone_ids = True
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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get_spk_id = True
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spk_id = numpy.array([args.spk_id])
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am_input_names = am_predictor.get_input_names()
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print("am_input_names:", am_input_names)
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merge_sentences = True
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for utt_id, sentence in sentences:
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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elif args.lang == 'en':
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in {'zh', 'en'}!")
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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tones = tone_ids[0].numpy()
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tones_handle = am_predictor.get_input_handle(am_input_names[1])
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tones_handle.reshape(tones.shape)
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tones_handle.copy_from_cpu(tones)
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if get_spk_id:
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spk_id_handle = am_predictor.get_input_handle(am_input_names[1])
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spk_id_handle.reshape(spk_id.shape)
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spk_id_handle.copy_from_cpu(spk_id)
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phones = phone_ids[0].numpy()
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phones_handle = am_predictor.get_input_handle(am_input_names[0])
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phones_handle.reshape(phones.shape)
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phones_handle.copy_from_cpu(phones)
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am_predictor.run()
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am_output_names = am_predictor.get_output_names()
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am_output_handle = am_predictor.get_output_handle(am_output_names[0])
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am_output_data = am_output_handle.copy_to_cpu()
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voc_input_names = voc_predictor.get_input_names()
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mel_handle = voc_predictor.get_input_handle(voc_input_names[0])
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mel_handle.reshape(am_output_data.shape)
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mel_handle.copy_from_cpu(am_output_data)
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voc_predictor.run()
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voc_output_names = voc_predictor.get_output_names()
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voc_output_handle = voc_predictor.get_output_handle(voc_output_names[0])
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wav = voc_output_handle.copy_to_cpu()
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sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
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print(f"{utt_id} done!")
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if __name__ == "__main__":
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main()
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