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PaddleSpeech/paddlespeech/t2s/exps/synthesize.py

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# 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
import logging
from pathlib import Path
import jsonlines
import numpy as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.t2s.datasets.data_table import DataTable
from paddlespeech.t2s.modules.normalizer import ZScore
model_alias = {
# acoustic model
"speedyspeech":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
"speedyspeech_inference":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
# voc
"pwgan":
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
"pwgan_inference":
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
}
def evaluate(args):
# dataloader has been too verbose
logging.getLogger("DataLoader").disabled = True
# construct dataset for evaluation
with jsonlines.open(args.test_metadata, 'r') as reader:
test_metadata = list(reader)
# Init body.
with open(args.am_config) as f:
am_config = CfgNode(yaml.safe_load(f))
with open(args.voc_config) as f:
voc_config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(am_config)
print(voc_config)
# construct dataset for evaluation
# model: {model_name}_{dataset}
am_name = args.am[:args.am.rindex('_')]
am_dataset = args.am[args.am.rindex('_') + 1:]
if am_name == 'fastspeech2':
fields = ["utt_id", "text"]
spk_num = None
if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
print("multiple speaker fastspeech2!")
with open(args.speaker_dict, 'rt') as f:
spk_id = [line.strip().split() for line in f.readlines()]
spk_num = len(spk_id)
fields += ["spk_id"]
elif args.voice_cloning:
print("voice cloning!")
fields += ["spk_emb"]
else:
print("single speaker fastspeech2!")
print("spk_num:", spk_num)
elif am_name == 'speedyspeech':
fields = ["utt_id", "phones", "tones"]
test_dataset = DataTable(data=test_metadata, fields=fields)
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)
tone_size = None
if args.tones_dict:
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)
# acoustic model
odim = am_config.n_mels
am_class = dynamic_import(am_name, model_alias)
am_inference_class = dynamic_import(am_name + '_inference', model_alias)
if am_name == 'fastspeech2':
am = am_class(
idim=vocab_size, odim=odim, spk_num=spk_num, **am_config["model"])
elif am_name == 'speedyspeech':
am = am_class(
vocab_size=vocab_size, tone_size=tone_size, **am_config["model"])
am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
am.eval()
am_mu, am_std = np.load(args.am_stat)
am_mu = paddle.to_tensor(am_mu)
am_std = paddle.to_tensor(am_std)
am_normalizer = ZScore(am_mu, am_std)
am_inference = am_inference_class(am_normalizer, am)
print("am_inference.training0:", am_inference.training)
am_inference.eval()
print("acoustic model done!")
# vocoder
# model: {model_name}_{dataset}
voc_name = args.voc[:args.voc.rindex('_')]
voc_class = dynamic_import(voc_name, model_alias)
voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
voc = voc_class(**voc_config["generator_params"])
voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"])
voc.remove_weight_norm()
voc.eval()
voc_mu, voc_std = np.load(args.voc_stat)
voc_mu = paddle.to_tensor(voc_mu)
voc_std = paddle.to_tensor(voc_std)
voc_normalizer = ZScore(voc_mu, voc_std)
voc_inference = voc_inference_class(voc_normalizer, voc)
print("voc_inference.training0:", voc_inference.training)
voc_inference.eval()
print("voc done!")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for datum in test_dataset:
utt_id = datum["utt_id"]
with paddle.no_grad():
# acoustic model
if am_name == 'fastspeech2':
phone_ids = paddle.to_tensor(datum["text"])
spk_emb = None
spk_id = None
# multi speaker
if args.voice_cloning and "spk_emb" in datum:
spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
elif "spk_id" in datum:
spk_id = paddle.to_tensor(datum["spk_id"])
mel = am_inference(phone_ids, spk_id=spk_id, spk_emb=spk_emb)
elif am_name == 'speedyspeech':
phone_ids = paddle.to_tensor(datum["phones"])
tone_ids = paddle.to_tensor(datum["tones"])
mel = am_inference(phone_ids, tone_ids)
# vocoder
wav = voc_inference(mel)
sf.write(
str(output_dir / (utt_id + ".wav")),
wav.numpy(),
samplerate=am_config.fs)
print(f"{utt_id} done!")
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(
description="Synthesize with acoustic model & vocoder")
# acoustic model
parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=[
'speedyspeech_csmsc', 'fastspeech2_csmsc', 'fastspeech2_ljspeech',
'fastspeech2_aishell3', 'fastspeech2_vctk'
],
help='Choose acoustic model type of tts task.')
parser.add_argument(
'--am_config',
type=str,
default=None,
help='Config of acoustic model. Use deault config when it is None.')
parser.add_argument(
'--am_ckpt',
type=str,
default=None,
help='Checkpoint file of acoustic model.')
parser.add_argument(
"--am_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
)
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.")
def str2bool(str):
return True if str.lower() == 'true' else False
parser.add_argument(
"--voice-cloning",
type=str2bool,
default=False,
help="whether training voice cloning model.")
# vocoder
parser.add_argument(
'--voc',
type=str,
default='pwgan_csmsc',
choices=[
'pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3', 'pwgan_vctk',
'mb_melgan_csmsc'
],
help='Choose vocoder type of tts task.')
parser.add_argument(
'--voc_config',
type=str,
default=None,
help='Config of voc. Use deault config when it is None.')
parser.add_argument(
'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
parser.add_argument(
"--voc_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training voc."
)
# other
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--test_metadata", type=str, help="test metadata.")
parser.add_argument("--output_dir", type=str, help="output dir.")
args = parser.parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
evaluate(args)
if __name__ == "__main__":
main()