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PaddleSpeech/paddlespeech/t2s/exps/inference.py

184 lines
6.5 KiB

# 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', 'tacotron2_csmsc'
],
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', 'wavernn_csmsc'
],
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()