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# -*- 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()
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