# Copyright (c) 2019 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import os
from os.path import dirname, join, basename, splitext, exists
from tqdm import tqdm
import numpy as np
import nltk
from paddle import fluid
import paddle.fluid.dygraph as dg
import audio
from deepvoice3_paddle import frontend
from deepvoice3_paddle.dry_run import dry_run
from hparams import hparams
from train import make_deepvoice3_from_hparams
from eval_model import tts, plot_alignment
from deepvoice3_paddle.save_load import load_checkpoint
def build_parser():
parser = argparse.ArgumentParser(
description="Synthesis waveform from trained model.")
parser.add_argument(
"--hparams", type=str, default="", help="Hyper parameters.")
parser.add_argument(
"--preset",
type=str,
required=True,
help="Path of preset parameters (json).")
parser.add_argument(
"--use-gpu",
action="store_true",
help="Whether to use gpu for generation.")
parser.add_argument(
"--checkpoint-seq2seq",
type=str,
help="Load seq2seq model from checkpoint path.")
parser.add_argument(
"--checkpoint-postnet",
type=str,
help="Load postnet model from checkpoint path.")
parser.add_argument(
"--file-name-suffix", type=str, default="", help="File name suffix.")
parser.add_argument(
"--max-decoder-steps", type=int, default=500, help="Max decoder steps.")
parser.add_argument(
"--replace_pronunciation_prob",
type=float,
default=0.,
help="Probility to replace text with pronunciation.")
parser.add_argument(
"--speaker-id", type=int, help="Speaker ID (for multi-speaker model).")
parser.add_argument(
"--output-html", action="store_true", help="Output html for blog post.")
parser.add_argument(
"checkpoint", type=str, help="The checkpoint used for synthesis")
parser.add_argument(
"text_list_file",
type=str,
help="Text file to synthesis, a sentence per line.")
parser.add_argument(
"dst_dir", type=str, help="Directory to save synthesis results.")
return parser
if __name__ == "__main__":
parser = build_parser()
args, _ = parser.parse_known_args()
checkpoint_path = args.checkpoint
text_list_file_path = args.text_list_file
dst_dir = args.dst_dir
use_gpu = args.use_gpu
checkpoint_seq2seq_path = args.checkpoint_seq2seq
checkpoint_postnet_path = args.checkpoint_postnet
max_decoder_steps = args.max_decoder_steps
file_name_suffix = args.file_name_suffix
replace_pronunciation_prob = args.replace_pronunciation_prob
output_html = args.output_html
speaker_id = args.speaker_id
preset = args.preset
print("Command Line Args:")
for k, v in vars(args).items():
print(" {}: {}".format(k, v))
# Load preset if specified
if preset is not None:
with open(preset) as f:
hparams.parse_json(f.read())
# Override hyper parameters
hparams.parse(args.hparams)
assert hparams.name == "deepvoice3"
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
with dg.guard(place):
# Model
model = make_deepvoice3_from_hparams(hparams)
dry_run(model)
load_checkpoint(checkpoint_path, model)
checkpoint_name = splitext(basename(checkpoint_path))[0]
model.seq2seq.decoder.max_decoder_steps = max_decoder_steps
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
with open(text_list_file_path, "rb") as f:
lines = f.readlines()
for idx, line in enumerate(lines):
text = line.decode("utf-8")[:-1]
words = nltk.word_tokenize(text)
waveform, alignment, _, _ = tts(model,
text,
p=replace_pronunciation_prob,
speaker_id=speaker_id)
dst_wav_path = join(dst_dir, "{}_{}{}.wav".format(
idx, checkpoint_name, file_name_suffix))
dst_alignment_path = join(
dst_dir, "{}_{}{}_alignment.png".format(
idx, checkpoint_name, file_name_suffix))
plot_alignment(
alignment.T,
dst_alignment_path,
info="{}, {}".format(hparams.builder,
basename(checkpoint_path)))
audio.save_wav(waveform, dst_wav_path)
name = splitext(basename(text_list_file_path))[0]
if output_html:
print("""
{}
({} chars, {} words)