TianYuan
3 years ago
parent
149b1fb1fa
commit
dafe7c3657
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#!/bin/bash
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train_output_path=$1
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stage=2
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stop_stage=2
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../inference_streaming.py \
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--inference_dir=${train_output_path}/inference_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/pd_infer_out_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../inference_streaming.py \
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--inference_dir=${train_output_path}/inference_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=mb_melgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/pd_infer_out_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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fi
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# hifigan
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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python3 ${BIN_DIR}/../inference_streaming.py \
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--inference_dir=${train_output_path}/inference_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/pd_infer_out_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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fi
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train_output_path=$1
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stage=0
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stop_stage=0
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# e2e, synthesize from text
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../ort_predict_streaming.py \
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--inference_dir=${train_output_path}/inference_onnx_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_csmsc \
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--output_dir=${train_output_path}/onnx_infer_out_streaming \
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--text=${BIN_DIR}/../csmsc_test.txt \
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--phones_dict=dump/phone_id_map.txt \
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--device=cpu \
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--cpu_threads=2 \
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--am_streaming=True
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fi
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from pathlib import Path
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import numpy as np
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import denorm
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from paddlespeech.t2s.exps.syn_utils import get_am_sublayer_output
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from paddlespeech.t2s.exps.syn_utils import get_chunks
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_predictor
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_streaming_am_output
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from paddlespeech.t2s.exps.syn_utils import get_streaming_am_predictor
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from paddlespeech.t2s.exps.syn_utils import get_voc_output
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from paddlespeech.t2s.utils import str2bool
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Paddle Infernce with acoustic model & vocoder.")
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# acoustic model
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=['fastspeech2_csmsc'],
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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"--am_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training acoustic model."
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)
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parser.add_argument(
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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parser.add_argument(
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"--speaker_dict", type=str, default=None, help="speaker id map file.")
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parser.add_argument(
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'--spk_id',
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type=int,
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default=0,
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help='spk id for multi speaker acoustic model')
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# voc
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parser.add_argument(
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'--voc',
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type=str,
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default='pwgan_csmsc',
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choices=['pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc'],
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help='Choose vocoder type of tts task.')
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# other
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parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en')
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line")
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parser.add_argument(
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"--inference_dir", type=str, help="dir to save inference models")
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parser.add_argument("--output_dir", type=str, help="output dir")
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# inference
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parser.add_argument(
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"--device",
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default="gpu",
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choices=["gpu", "cpu"],
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help="Device selected for inference.", )
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# streaming related
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parser.add_argument(
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"--am_streaming",
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type=str2bool,
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default=False,
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help="whether use streaming acoustic model")
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parser.add_argument(
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"--chunk_size", type=int, default=42, help="chunk size of am streaming")
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parser.add_argument(
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"--pad_size", type=int, default=12, help="pad size of am streaming")
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args, _ = parser.parse_known_args()
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return args
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# only inference for models trained with csmsc now
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def main():
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args = parse_args()
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# frontend
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frontend = get_frontend(args)
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# am_predictor
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am_encoder_infer_predictor, am_decoder_predictor, am_postnet_predictor = get_streaming_am_predictor(
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args)
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am_mu, am_std = np.load(args.am_stat)
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# model: {model_name}_{dataset}
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am_dataset = args.am[args.am.rindex('_') + 1:]
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# voc_predictor
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voc_predictor = get_predictor(args, filed='voc')
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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sentences = get_sentences(args)
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merge_sentences = True
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fs = 24000 if am_dataset != 'ljspeech' else 22050
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# warmup
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for utt_id, sentence in sentences[:3]:
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with timer() as t:
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normalized_mel = get_streaming_am_output(
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args,
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am_encoder_infer_predictor=am_encoder_infer_predictor,
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am_decoder_predictor=am_decoder_predictor,
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am_postnet_predictor=am_postnet_predictor,
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frontend=frontend,
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merge_sentences=merge_sentences,
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input=sentence)
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mel = denorm(normalized_mel, am_mu, am_std)
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wav = get_voc_output(voc_predictor=voc_predictor, input=mel)
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speed = wav.size / t.elapse
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rtf = fs / speed
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print("warm up done!")
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N = 0
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T = 0
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chunk_size = args.chunk_size
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pad_size = args.pad_size
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get_tone_ids = False
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for utt_id, sentence in sentences:
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with timer() as t:
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# frontend
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should be 'zh' here!")
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phones = phone_ids[0].numpy()
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# acoustic model
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orig_hs = get_am_sublayer_output(
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am_encoder_infer_predictor, input=phones)
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if args.am_streaming:
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hss = get_chunks(orig_hs, chunk_size, pad_size)
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chunk_num = len(hss)
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mel_list = []
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for i, hs in enumerate(hss):
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am_decoder_output = get_am_sublayer_output(
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am_decoder_predictor, input=hs)
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am_postnet_output = get_am_sublayer_output(
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am_postnet_predictor,
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input=np.transpose(am_decoder_output, (0, 2, 1)))
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am_output_data = am_decoder_output + np.transpose(
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am_postnet_output, (0, 2, 1))
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normalized_mel = am_output_data[0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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# clip output part of pad
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if i == 0:
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sub_mel = sub_mel[:-pad_size]
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elif i == chunk_num - 1:
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# 最后一块的右侧一定没有 pad 够
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sub_mel = sub_mel[pad_size:]
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else:
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# 倒数几块的右侧也可能没有 pad 够
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sub_mel = sub_mel[pad_size:(chunk_size + pad_size) -
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sub_mel.shape[0]]
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mel_list.append(sub_mel)
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mel = np.concatenate(mel_list, axis=0)
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else:
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am_decoder_output = get_am_sublayer_output(
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am_decoder_predictor, input=orig_hs)
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am_postnet_output = get_am_sublayer_output(
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am_postnet_predictor,
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input=np.transpose(am_decoder_output, (0, 2, 1)))
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am_output_data = am_decoder_output + np.transpose(
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am_postnet_output, (0, 2, 1))
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normalized_mel = am_output_data[0]
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mel = denorm(normalized_mel, am_mu, am_std)
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# vocoder
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wav = get_voc_output(voc_predictor=voc_predictor, input=mel)
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = fs / speed
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sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print(f"{utt_id} done!")
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print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
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if __name__ == "__main__":
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main()
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@ -0,0 +1,233 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from pathlib import Path
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import numpy as np
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import denorm
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from paddlespeech.t2s.exps.syn_utils import get_chunks
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_sess
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from paddlespeech.t2s.exps.syn_utils import get_streaming_am_sess
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from paddlespeech.t2s.utils import str2bool
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def ort_predict(args):
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# frontend
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frontend = get_frontend(args)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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sentences = get_sentences(args)
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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fs = 24000 if am_dataset != 'ljspeech' else 22050
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# am
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am_encoder_infer_sess, am_decoder_sess, am_postnet_sess = get_streaming_am_sess(
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args)
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am_mu, am_std = np.load(args.am_stat)
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# vocoder
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voc_sess = get_sess(args, filed='voc')
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# frontend warmup
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# Loading model cost 0.5+ seconds
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if args.lang == 'zh':
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frontend.get_input_ids("你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=True)
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else:
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print("lang should in be 'zh' here!")
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# am warmup
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for T in [27, 38, 54]:
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phone_ids = np.random.randint(1, 266, size=(T, ))
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am_encoder_infer_sess.run(None, input_feed={'text': phone_ids})
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am_decoder_input = np.random.rand(1, T * 15, 384).astype('float32')
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am_decoder_sess.run(None, input_feed={'xs': am_decoder_input})
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am_postnet_input = np.random.rand(1, 80, T * 15).astype('float32')
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am_postnet_sess.run(None, input_feed={'xs': am_postnet_input})
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# voc warmup
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for T in [227, 308, 544]:
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data = np.random.rand(T, 80).astype("float32")
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voc_sess.run(None, input_feed={"logmel": data})
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print("warm up done!")
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N = 0
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T = 0
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merge_sentences = True
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get_tone_ids = False
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chunk_size = args.chunk_size
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pad_size = args.pad_size
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for utt_id, sentence in sentences:
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with timer() as t:
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in be 'zh' here!")
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# merge_sentences=True here, so we only use the first item of phone_ids
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phone_ids = phone_ids[0].numpy()
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orig_hs = am_encoder_infer_sess.run(
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None, input_feed={'text': phone_ids})
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if args.am_streaming:
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hss = get_chunks(orig_hs[0], chunk_size, pad_size)
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chunk_num = len(hss)
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mel_list = []
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for i, hs in enumerate(hss):
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am_decoder_output = am_decoder_sess.run(
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None, input_feed={'xs': hs})
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am_postnet_output = am_postnet_sess.run(
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None,
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input_feed={
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'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
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})
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am_output_data = am_decoder_output + np.transpose(
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am_postnet_output[0], (0, 2, 1))
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normalized_mel = am_output_data[0][0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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# clip output part of pad
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if i == 0:
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sub_mel = sub_mel[:-pad_size]
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elif i == chunk_num - 1:
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# 最后一块的右侧一定没有 pad 够
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sub_mel = sub_mel[pad_size:]
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else:
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# 倒数几块的右侧也可能没有 pad 够
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sub_mel = sub_mel[pad_size:(chunk_size + pad_size) -
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sub_mel.shape[0]]
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mel_list.append(sub_mel)
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mel = np.concatenate(mel_list, axis=0)
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else:
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am_decoder_output = am_decoder_sess.run(
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None, input_feed={'xs': orig_hs[0]})
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am_postnet_output = am_postnet_sess.run(
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None,
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input_feed={
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'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
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})
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am_output_data = am_decoder_output + np.transpose(
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am_postnet_output[0], (0, 2, 1))
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normalized_mel = am_output_data[0]
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mel = denorm(normalized_mel, am_mu, am_std)
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mel = mel[0]
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# vocoder
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wav = voc_sess.run(output_names=None, input_feed={'logmel': mel})
|
||||
|
||||
N += len(wav[0])
|
||||
T += t.elapse
|
||||
speed = len(wav[0]) / t.elapse
|
||||
rtf = fs / speed
|
||||
sf.write(
|
||||
str(output_dir / (utt_id + ".wav")),
|
||||
np.array(wav)[0],
|
||||
samplerate=fs)
|
||||
print(
|
||||
f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
|
||||
)
|
||||
print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="Infernce with onnxruntime.")
|
||||
# acoustic model
|
||||
parser.add_argument(
|
||||
'--am',
|
||||
type=str,
|
||||
default='fastspeech2_csmsc',
|
||||
choices=['fastspeech2_csmsc'],
|
||||
help='Choose acoustic model type of tts task.')
|
||||
parser.add_argument(
|
||||
"--am_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
|
||||
|
||||
# voc
|
||||
parser.add_argument(
|
||||
'--voc',
|
||||
type=str,
|
||||
default='hifigan_csmsc',
|
||||
choices=['hifigan_csmsc', 'mb_melgan_csmsc', 'pwgan_csmsc'],
|
||||
help='Choose vocoder type of tts task.')
|
||||
# other
|
||||
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(
|
||||
'--lang',
|
||||
type=str,
|
||||
default='zh',
|
||||
help='Choose model language. zh or en')
|
||||
|
||||
# inference
|
||||
parser.add_argument(
|
||||
"--use_trt",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Whether to use inference engin TensorRT.", )
|
||||
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
default="gpu",
|
||||
choices=["gpu", "cpu"],
|
||||
help="Device selected for inference.", )
|
||||
parser.add_argument('--cpu_threads', type=int, default=1)
|
||||
|
||||
# streaming related
|
||||
parser.add_argument(
|
||||
"--am_streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="whether use streaming acoustic model")
|
||||
parser.add_argument(
|
||||
"--chunk_size", type=int, default=42, help="chunk size of am streaming")
|
||||
parser.add_argument(
|
||||
"--pad_size", type=int, default=12, help="pad size of am streaming")
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
ort_predict(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue