# 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 numpy as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from paddlespeech.t2s.frontend.en_frontend import English
from paddlespeech.t2s.models.transformer_tts import TransformerTTS
from paddlespeech.t2s.models.transformer_tts import TransformerTTSInference
from paddlespeech.t2s.models.waveflow import ConditionalWaveFlow
from paddlespeech.t2s.modules.normalizer import ZScore
from paddlespeech.t2s.utils import layer_tools
[docs]def evaluate(args, acoustic_model_config, vocoder_config):
# dataloader has been too verbose
logging.getLogger("DataLoader").disabled = True
# construct dataset for evaluation
sentences = []
with open(args.text, 'rt') as f:
for line in f:
line_list = line.strip().split()
utt_id = line_list[0]
sentence = " ".join(line_list[1:])
sentences.append((utt_id, sentence))
with open(args.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
phone_id_map = {}
for phn, id in phn_id:
phone_id_map[phn] = int(id)
print("vocab_size:", vocab_size)
odim = acoustic_model_config.n_mels
model = TransformerTTS(
idim=vocab_size, odim=odim, **acoustic_model_config["model"])
model.set_state_dict(
paddle.load(args.transformer_tts_checkpoint)["main_params"])
model.eval()
# remove ".pdparams" in waveflow_checkpoint
vocoder_checkpoint_path = args.waveflow_checkpoint[:-9] if args.waveflow_checkpoint.endswith(
".pdparams") else args.waveflow_checkpoint
vocoder = ConditionalWaveFlow.from_pretrained(vocoder_config,
vocoder_checkpoint_path)
layer_tools.recursively_remove_weight_norm(vocoder)
vocoder.eval()
print("model done!")
frontend = English()
print("frontend done!")
stat = np.load(args.transformer_tts_stat)
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
transformer_tts_normalizer = ZScore(mu, std)
transformer_tts_inference = TransformerTTSInference(
transformer_tts_normalizer, model)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for utt_id, sentence in sentences:
phones = frontend.phoneticize(sentence)
# remove start_symbol and end_symbol
phones = phones[1:-1]
phones = [phn for phn in phones if not phn.isspace()]
phones = [phn if phn in phone_id_map else "," for phn in phones]
phone_ids = [phone_id_map[phn] for phn in phones]
with paddle.no_grad():
mel = transformer_tts_inference(paddle.to_tensor(phone_ids))
# mel shape is (T, feats) and waveflow's input shape is (batch, feats, T)
mel = mel.unsqueeze(0).transpose([0, 2, 1])
# wavflow's output shape is (B, T)
wav = vocoder.infer(mel)[0]
sf.write(
str(output_dir / (utt_id + ".wav")),
wav.numpy(),
samplerate=acoustic_model_config.fs)
print(f"{utt_id} done!")
[docs]def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(
description="Synthesize with transformer tts & waveflow.")
parser.add_argument(
"--transformer-tts-config",
type=str,
help="transformer tts config file.")
parser.add_argument(
"--transformer-tts-checkpoint",
type=str,
help="transformer tts checkpoint to load.")
parser.add_argument(
"--transformer-tts-stat",
type=str,
help="mean and standard deviation used to normalize spectrogram when training transformer tts."
)
parser.add_argument(
"--waveflow-config", type=str, help="waveflow config file.")
# not normalize when training waveflow
parser.add_argument(
"--waveflow-checkpoint", type=str, help="waveflow checkpoint to load.")
parser.add_argument(
"--phones-dict",
type=str,
default="phone_id_map.txt",
help="phone vocabulary file.")
parser.add_argument(
"--text",
type=str,
help="text to synthesize, a 'utt_id sentence' pair per line.")
parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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 !")
with open(args.transformer_tts_config) as f:
transformer_tts_config = CfgNode(yaml.safe_load(f))
with open(args.waveflow_config) as f:
waveflow_config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(transformer_tts_config)
print(waveflow_config)
evaluate(args, transformer_tts_config, waveflow_config)
if __name__ == "__main__":
main()