# 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 as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
from paddlespeech.t2s.models.fastspeech2 import StyleFastSpeech2Inference
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
from paddlespeech.t2s.modules.normalizer import ZScore
def evaluate(args, fastspeech2_config, pwg_config):
# construct dataset for evaluation
sentences = []
with open(args.text, 'rt') as f:
for line in f:
items = line.strip().split()
utt_id = items[0]
sentence = "".join(items[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)
print("vocab_size:", vocab_size)
odim = fastspeech2_config.n_mels
model = FastSpeech2(
idim=vocab_size, odim=odim, **fastspeech2_config["model"])
model.set_state_dict(
paddle.load(args.fastspeech2_checkpoint)["main_params"])
model.eval()
vocoder = PWGGenerator(**pwg_config["generator_params"])
vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
vocoder.remove_weight_norm()
vocoder.eval()
print("model done!")
frontend = Frontend(phone_vocab_path=args.phones_dict)
print("frontend done!")
stat = np.load(args.fastspeech2_stat)
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
fastspeech2_normalizer = ZScore(mu, std)
stat = np.load(args.pwg_stat)
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
pwg_normalizer = ZScore(mu, std)
fastspeech2_inference = StyleFastSpeech2Inference(
fastspeech2_normalizer, model, args.fastspeech2_pitch_stat,
args.fastspeech2_energy_stat)
fastspeech2_inference.eval()
pwg_inference = PWGInference(pwg_normalizer, vocoder)
pwg_inference.eval()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
styles = ["normal", "robot", "1.2xspeed", "0.8xspeed", "child_voice"]
for style in styles:
robot = False
durations = None
durations_scale = None
durations_bias = None
pitch = None
pitch_scale = None
pitch_bias = None
energy = None
energy_scale = None
energy_bias = None
if style == "robot":
# all tones in phones be `1`
# all pitch should be the same, we use mean here
robot = True
if style == "1.2xspeed":
durations_scale = 1 / 1.2
if style == "0.8xspeed":
durations_scale = 1 / 0.8
if style == "child_voice":
pitch_scale = 1.3
sub_output_dir = output_dir / style
sub_output_dir.mkdir(parents=True, exist_ok=True)
for utt_id, sentence in sentences:
input_ids = frontend.get_input_ids(
sentence, merge_sentences=True, robot=robot)
phone_ids = input_ids["phone_ids"][0]
with paddle.no_grad():
mel = fastspeech2_inference(
phone_ids,
durations=durations,
durations_scale=durations_scale,
durations_bias=durations_bias,
pitch=pitch,
pitch_scale=pitch_scale,
pitch_bias=pitch_bias,
energy=energy,
energy_scale=energy_scale,
energy_bias=energy_bias,
robot=robot)
wav = pwg_inference(mel)
sf.write(
str(sub_output_dir / (utt_id + ".wav")),
wav.numpy(),
samplerate=fastspeech2_config.fs)
print(f"{style}_{utt_id} done!")
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(
description="Synthesize with fastspeech2 & parallel wavegan.")
parser.add_argument(
"--fastspeech2-config", type=str, help="fastspeech2 config file.")
parser.add_argument(
"--fastspeech2-checkpoint",
type=str,
help="fastspeech2 checkpoint to load.")
parser.add_argument(
"--fastspeech2-stat",
type=str,
help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
)
parser.add_argument(
"--fastspeech2-pitch-stat",
type=str,
help="mean and standard deviation used to normalize pitch when training fastspeech2"
)
parser.add_argument(
"--fastspeech2-energy-stat",
type=str,
help="mean and standard deviation used to normalize energy when training fastspeech2."
)
parser.add_argument(
"--pwg-config", type=str, help="parallel wavegan config file.")
parser.add_argument(
"--pwg-checkpoint",
type=str,
help="parallel wavegan generator parameters to load.")
parser.add_argument(
"--pwg-stat",
type=str,
help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
)
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.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
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.fastspeech2_config) as f:
fastspeech2_config = CfgNode(yaml.safe_load(f))
with open(args.pwg_config) as f:
pwg_config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(fastspeech2_config)
print(pwg_config)
evaluate(args, fastspeech2_config, pwg_config)
if __name__ == "__main__":
main()