You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
143 lines
5.1 KiB
143 lines
5.1 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
|
|
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.t2s.datasets.data_table import DataTable
|
|
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
|
|
|
|
|
|
def evaluate(args, acoustic_model_config, vocoder_config):
|
|
# 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)
|
|
test_dataset = DataTable(data=test_metadata, fields=["utt_id", "text"])
|
|
|
|
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)
|
|
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!")
|
|
|
|
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 datum in test_dataset:
|
|
utt_id = datum["utt_id"]
|
|
text = paddle.to_tensor(datum["text"])
|
|
|
|
with paddle.no_grad():
|
|
mel = transformer_tts_inference(text)
|
|
# 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!")
|
|
|
|
|
|
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=None, help="phone vocabulary file.")
|
|
|
|
parser.add_argument("--test-metadata", type=str, help="test metadata.")
|
|
parser.add_argument("--output-dir", type=str, help="output dir.")
|
|
parser.add_argument(
|
|
"--device", type=str, default="gpu", help="device type to use.")
|
|
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
paddle.set_device(args.device)
|
|
|
|
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()
|