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PaddleSpeech/paddlespeech/t2s/exps/fastspeech2/inference.py

136 lines
4.6 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 os
from pathlib import Path
import soundfile as sf
from paddle import inference
from paddlespeech.t2s.frontend.zh_frontend import Frontend
def main():
parser = argparse.ArgumentParser(
description="Paddle Infernce with speedyspeech & parallel wavegan.")
parser.add_argument(
"--inference-dir", type=str, help="dir to save inference models")
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(
"--enable-auto-log", action="store_true", help="use auto log")
parser.add_argument(
"--phones-dict",
type=str,
default="phones.txt",
help="phone vocabulary file.")
args, _ = parser.parse_known_args()
frontend = Frontend(phone_vocab_path=args.phones_dict)
print("frontend done!")
fastspeech2_config = inference.Config(
str(Path(args.inference_dir) / "fastspeech2.pdmodel"),
str(Path(args.inference_dir) / "fastspeech2.pdiparams"))
fastspeech2_config.enable_use_gpu(50, 0)
# This line must be commented, if not, it will OOM
# fastspeech2_config.enable_memory_optim()
fastspeech2_predictor = inference.create_predictor(fastspeech2_config)
pwg_config = inference.Config(
str(Path(args.inference_dir) / "pwg.pdmodel"),
str(Path(args.inference_dir) / "pwg.pdiparams"))
pwg_config.enable_use_gpu(100, 0)
pwg_config.enable_memory_optim()
pwg_predictor = inference.create_predictor(pwg_config)
if args.enable_auto_log:
import auto_log
os.makedirs("output", exist_ok=True)
pid = os.getpid()
logger = auto_log.AutoLogger(
model_name="fastspeech2",
model_precision='float32',
batch_size=1,
data_shape="dynamic",
save_path="./output/auto_log.log",
inference_config=fastspeech2_config,
pids=pid,
process_name=None,
gpu_ids=0,
time_keys=['preprocess_time', 'inference_time', 'postprocess_time'],
warmup=0)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
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))
for utt_id, sentence in sentences:
if args.enable_auto_log:
logger.times.start()
input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
phone_ids = input_ids["phone_ids"]
phones = phone_ids[0].numpy()
if args.enable_auto_log:
logger.times.stamp()
input_names = fastspeech2_predictor.get_input_names()
phones_handle = fastspeech2_predictor.get_input_handle(input_names[0])
phones_handle.reshape(phones.shape)
phones_handle.copy_from_cpu(phones)
fastspeech2_predictor.run()
output_names = fastspeech2_predictor.get_output_names()
output_handle = fastspeech2_predictor.get_output_handle(output_names[0])
output_data = output_handle.copy_to_cpu()
input_names = pwg_predictor.get_input_names()
mel_handle = pwg_predictor.get_input_handle(input_names[0])
mel_handle.reshape(output_data.shape)
mel_handle.copy_from_cpu(output_data)
pwg_predictor.run()
output_names = pwg_predictor.get_output_names()
output_handle = pwg_predictor.get_output_handle(output_names[0])
wav = output_data = output_handle.copy_to_cpu()
if args.enable_auto_log:
logger.times.stamp()
sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
if args.enable_auto_log:
logger.times.end(stamp=True)
print(f"{utt_id} done!")
if args.enable_auto_log:
logger.report()
if __name__ == "__main__":
main()