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218 lines
6.7 KiB
218 lines
6.7 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 paddle
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import get_am_output
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_predictor
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_voc_output
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from paddlespeech.t2s.utils import str2bool
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Paddle Infernce with acoustic model & vocoder.")
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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',
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'fastspeech2_csmsc',
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'fastspeech2_aishell3',
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'fastspeech2_ljspeech',
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'fastspeech2_vctk',
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'tacotron2_csmsc',
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'fastspeech2_mix',
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'fastspeech2_male-zh',
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'fastspeech2_male-en',
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'fastspeech2_male-mix',
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'fastspeech2_canton',
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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',
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'pwgan_aishell3',
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'pwgan_ljspeech',
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'pwgan_vctk',
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'mb_melgan_csmsc',
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'hifigan_csmsc',
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'hifigan_aishell3',
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'hifigan_ljspeech',
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'hifigan_vctk',
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'wavernn_csmsc',
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'pwgan_male',
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'hifigan_male',
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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 or mix')
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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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# inference
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parser.add_argument(
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"--use_trt",
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type=str2bool,
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default=False,
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help="whether to use TensorRT or not in GPU", )
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parser.add_argument(
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"--use_mkldnn",
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type=str2bool,
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default=False,
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help="whether to use MKLDNN or not in CPU.", )
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parser.add_argument(
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"--precision",
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type=str,
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default='fp32',
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choices=['fp32', 'fp16', 'bf16', 'int8'],
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help="mode of running")
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parser.add_argument(
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"--device",
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default="gpu",
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choices=["gpu", "cpu", "xpu"],
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help="Device selected for inference.", )
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parser.add_argument('--cpu_threads', type=int, default=1)
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args, _ = parser.parse_known_args()
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return args
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# only inference for models trained with csmsc now
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def main():
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args = parse_args()
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paddle.set_device(args.device)
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# frontend
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frontend = get_frontend(
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lang=args.lang,
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phones_dict=args.phones_dict,
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tones_dict=args.tones_dict)
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# am_predictor
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am_predictor = get_predictor(
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model_dir=args.inference_dir,
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model_file=args.am + ".pdmodel",
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params_file=args.am + ".pdiparams",
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device=args.device,
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use_trt=args.use_trt,
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use_mkldnn=args.use_mkldnn,
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cpu_threads=args.cpu_threads,
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precision=args.precision)
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# model: {model_name}_{dataset}
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am_dataset = args.am[args.am.rindex('_') + 1:]
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# voc_predictor
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voc_predictor = get_predictor(
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model_dir=args.inference_dir,
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model_file=args.voc + ".pdmodel",
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params_file=args.voc + ".pdiparams",
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device=args.device,
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use_trt=args.use_trt,
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use_mkldnn=args.use_mkldnn,
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cpu_threads=args.cpu_threads,
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precision=args.precision)
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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 = get_sentences(text_file=args.text, lang=args.lang)
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merge_sentences = True
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fs = 24000 if am_dataset != 'ljspeech' else 22050
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# warmup
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for utt_id, sentence in sentences[:3]:
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with timer() as t:
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mel = get_am_output(
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input=sentence,
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am_predictor=am_predictor,
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am=args.am,
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frontend=frontend,
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lang=args.lang,
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merge_sentences=merge_sentences,
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speaker_dict=args.speaker_dict,
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spk_id=args.spk_id, )
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wav = get_voc_output(voc_predictor=voc_predictor, input=mel)
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speed = wav.size / t.elapse
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rtf = fs / speed
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print("warm up done!")
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N = 0
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T = 0
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for utt_id, sentence in sentences:
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with timer() as t:
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mel = get_am_output(
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input=sentence,
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am_predictor=am_predictor,
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am=args.am,
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frontend=frontend,
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lang=args.lang,
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merge_sentences=merge_sentences,
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speaker_dict=args.speaker_dict,
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spk_id=args.spk_id, )
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wav = get_voc_output(voc_predictor=voc_predictor, input=mel)
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = fs / speed
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sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=fs)
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print(f"{utt_id} done!")
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print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
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if __name__ == "__main__":
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main()
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