Comparative-Analysis-of-Speech-Synthesis-Models
/
TensorFlowTTS
/examples
/tacotron2
/decode_tacotron2.py
# -*- coding: utf-8 -*- | |
# Copyright 2020 Minh Nguyen (@dathudeptrai) | |
# | |
# 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. | |
"""Decode Tacotron-2.""" | |
import argparse | |
import logging | |
import os | |
import sys | |
sys.path.append(".") | |
import numpy as np | |
import tensorflow as tf | |
import yaml | |
from tqdm import tqdm | |
import matplotlib.pyplot as plt | |
from examples.tacotron2.tacotron_dataset import CharactorMelDataset | |
from tensorflow_tts.configs import Tacotron2Config | |
from tensorflow_tts.models import TFTacotron2 | |
def main(): | |
"""Running decode tacotron-2 mel-spectrogram.""" | |
parser = argparse.ArgumentParser( | |
description="Decode mel-spectrogram from folder ids with trained Tacotron-2 " | |
"(See detail in tensorflow_tts/example/tacotron2/decode_tacotron2.py)." | |
) | |
parser.add_argument( | |
"--rootdir", | |
default=None, | |
type=str, | |
required=True, | |
help="directory including ids/durations files.", | |
) | |
parser.add_argument( | |
"--outdir", type=str, required=True, help="directory to save generated speech." | |
) | |
parser.add_argument( | |
"--checkpoint", type=str, required=True, help="checkpoint file to be loaded." | |
) | |
parser.add_argument( | |
"--use-norm", default=1, type=int, help="usr norm-mels for train or raw." | |
) | |
parser.add_argument("--batch-size", default=8, type=int, help="batch size.") | |
parser.add_argument("--win-front", default=3, type=int, help="win-front.") | |
parser.add_argument("--win-back", default=3, type=int, help="win-front.") | |
parser.add_argument( | |
"--config", | |
default=None, | |
type=str, | |
required=True, | |
help="yaml format configuration file. if not explicitly provided, " | |
"it will be searched in the checkpoint directory. (default=None)", | |
) | |
parser.add_argument( | |
"--verbose", | |
type=int, | |
default=1, | |
help="logging level. higher is more logging. (default=1)", | |
) | |
args = parser.parse_args() | |
# set logger | |
if args.verbose > 1: | |
logging.basicConfig( | |
level=logging.DEBUG, | |
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", | |
) | |
elif args.verbose > 0: | |
logging.basicConfig( | |
level=logging.INFO, | |
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", | |
) | |
else: | |
logging.basicConfig( | |
level=logging.WARN, | |
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", | |
) | |
logging.warning("Skip DEBUG/INFO messages") | |
# check directory existence | |
if not os.path.exists(args.outdir): | |
os.makedirs(args.outdir) | |
# load config | |
with open(args.config) as f: | |
config = yaml.load(f, Loader=yaml.Loader) | |
config.update(vars(args)) | |
if config["format"] == "npy": | |
char_query = "*-ids.npy" | |
mel_query = "*-raw-feats.npy" if args.use_norm is False else "*-norm-feats.npy" | |
char_load_fn = np.load | |
mel_load_fn = np.load | |
else: | |
raise ValueError("Only npy is supported.") | |
# define data-loader | |
dataset = CharactorMelDataset( | |
dataset=config["tacotron2_params"]["dataset"], | |
root_dir=args.rootdir, | |
charactor_query=char_query, | |
mel_query=mel_query, | |
charactor_load_fn=char_load_fn, | |
mel_load_fn=mel_load_fn, | |
reduction_factor=config["tacotron2_params"]["reduction_factor"] | |
) | |
dataset = dataset.create(allow_cache=True, batch_size=args.batch_size) | |
# define model and load checkpoint | |
tacotron2 = TFTacotron2( | |
config=Tacotron2Config(**config["tacotron2_params"]), | |
name="tacotron2", | |
) | |
tacotron2._build() # build model to be able load_weights. | |
tacotron2.load_weights(args.checkpoint) | |
# setup window | |
tacotron2.setup_window(win_front=args.win_front, win_back=args.win_back) | |
for data in tqdm(dataset, desc="[Decoding]"): | |
utt_ids = data["utt_ids"] | |
utt_ids = utt_ids.numpy() | |
# tacotron2 inference. | |
( | |
mel_outputs, | |
post_mel_outputs, | |
stop_outputs, | |
alignment_historys, | |
) = tacotron2.inference( | |
input_ids=data["input_ids"], | |
input_lengths=data["input_lengths"], | |
speaker_ids=data["speaker_ids"], | |
) | |
# convert to numpy | |
post_mel_outputs = post_mel_outputs.numpy() | |
for i, post_mel_output in enumerate(post_mel_outputs): | |
stop_token = tf.math.round(tf.nn.sigmoid(stop_outputs[i])) # [T] | |
real_length = tf.math.reduce_sum( | |
tf.cast(tf.math.equal(stop_token, 0.0), tf.int32), -1 | |
) | |
post_mel_output = post_mel_output[:real_length, :] | |
saved_name = utt_ids[i].decode("utf-8") | |
# save D to folder. | |
np.save( | |
os.path.join(args.outdir, f"{saved_name}-norm-feats.npy"), | |
post_mel_output.astype(np.float32), | |
allow_pickle=False, | |
) | |
if __name__ == "__main__": | |
main() | |