TianYuan
2 years ago
parent
028742b69a
commit
5503c8bd6b
@ -1 +1,45 @@
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#!/bin/bash
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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stage=1
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stop_stage=1
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# use am to predict duration here
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# 增加 am_phones_dict am_tones_dict 等，也可以用新的方式构造 am, 不需要这么多参数了就
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/synthesize.py \
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--erniesat_config=${config_path} \
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--erniesat_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--erniesat_stat=dump/train/speech_stats.npy \
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--voc=pwgan_vctk \
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--voc_config=pwg_vctk_ckpt_0.1.1/default.yaml \
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--voc_ckpt=pwg_vctk_ckpt_0.1.1/snapshot_iter_1500000.pdz \
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--voc_stat=pwg_vctk_ckpt_0.1.1/feats_stats.npy \
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--test_metadata=dump/test/norm/metadata.jsonl \
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--output_dir=${train_output_path}/test \
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--phones_dict=dump/phone_id_map.txt
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fi
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# hifigan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/synthesize.py \
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--erniesat_config=${config_path} \
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--erniesat_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--erniesat_stat=dump/train/speech_stats.npy \
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--voc=hifigan_vctk \
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--voc_config=hifigan_vctk_ckpt_0.2.0/default.yaml \
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--voc_ckpt=hifigan_vctk_ckpt_0.2.0/snapshot_iter_2500000.pdz \
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--voc_stat=hifigan_vctk_ckpt_0.2.0/feats_stats.npy \
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--test_metadata=dump/test/norm/metadata.jsonl \
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--output_dir=${train_output_path}/test \
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--phones_dict=dump/phone_id_map.txt
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fi
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@ -0,0 +1,201 @@
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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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import logging
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from pathlib import Path
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import jsonlines
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.am_batch_fn import build_erniesat_collate_fn
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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_inference
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from paddlespeech.t2s.exps.syn_utils import get_test_dataset
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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def evaluate(args):
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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# construct dataset for evaluation
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with jsonlines.open(args.test_metadata, 'r') as reader:
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test_metadata = list(reader)
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# Init body.
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with open(args.erniesat_config) as f:
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erniesat_config = CfgNode(yaml.safe_load(f))
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with open(args.voc_config) as f:
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voc_config = CfgNode(yaml.safe_load(f))
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(erniesat_config)
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print(voc_config)
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# ernie sat model
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erniesat_inference = get_am_inference(
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am='erniesat_dataset',
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am_config=erniesat_config,
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am_ckpt=args.erniesat_ckpt,
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am_stat=args.erniesat_stat,
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phones_dict=args.phones_dict)
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test_dataset = get_test_dataset(
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test_metadata=test_metadata, am='erniesat_dataset')
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# vocoder
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voc_inference = get_voc_inference(
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voc=args.voc,
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voc_config=voc_config,
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voc_ckpt=args.voc_ckpt,
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voc_stat=args.voc_stat)
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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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collate_fn = build_erniesat_collate_fn(
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mlm_prob=erniesat_config.mlm_prob,
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mean_phn_span=erniesat_config.mean_phn_span,
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seg_emb=erniesat_config.model['enc_input_layer'] == 'sega_mlm',
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text_masking=False,
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epoch=-1)
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gen_raw = True
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erniesat_mu, erniesat_std = np.load(args.erniesat_stat)
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for datum in test_dataset:
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# collate function and dataloader
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utt_id = datum["utt_id"]
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speech_len = datum["speech_lengths"]
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# mask the middle 1/3 speech
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left_bdy, right_bdy = speech_len // 3, 2 * speech_len // 3
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span_bdy = [left_bdy, right_bdy]
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datum.update({"span_bdy": span_bdy})
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batch = collate_fn([datum])
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with paddle.no_grad():
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out_mels = erniesat_inference(
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speech=batch["speech"],
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text=batch["text"],
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masked_pos=batch["masked_pos"],
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speech_mask=batch["speech_mask"],
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text_mask=batch["text_mask"],
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speech_seg_pos=batch["speech_seg_pos"],
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text_seg_pos=batch["text_seg_pos"],
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span_bdy=span_bdy)
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# vocoder
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wav_list = []
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for mel in out_mels:
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part_wav = voc_inference(mel)
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wav_list.append(part_wav)
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wav = paddle.concat(wav_list)
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wav = wav.numpy()
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if gen_raw:
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speech = datum['speech']
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denorm_mel = denorm(speech, erniesat_mu, erniesat_std)
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denorm_mel = paddle.to_tensor(denorm_mel)
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wav_raw = voc_inference(denorm_mel)
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wav_raw = wav_raw.numpy()
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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wav,
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samplerate=erniesat_config.fs)
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if gen_raw:
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sf.write(
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str(output_dir / (utt_id + "_raw" + ".wav")),
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wav_raw,
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samplerate=erniesat_config.fs)
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print(f"{utt_id} done!")
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def parse_args():
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# parse args and config
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parser = argparse.ArgumentParser(
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description="Synthesize with acoustic model & vocoder")
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# ernie sat
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parser.add_argument(
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'--erniesat_config',
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type=str,
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default=None,
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help='Config of acoustic model.')
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parser.add_argument(
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'--erniesat_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of acoustic model.')
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parser.add_argument(
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"--erniesat_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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# vocoder
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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=[
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'pwgan_aishell3',
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'pwgan_vctk',
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'hifigan_aishell3',
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'hifigan_vctk',
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],
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help='Choose vocoder type of tts task.')
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parser.add_argument(
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'--voc_config', type=str, default=None, help='Config of voc.')
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parser.add_argument(
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'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
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parser.add_argument(
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"--voc_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 voc."
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)
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# other
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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parser.add_argument("--test_metadata", type=str, help="test metadata.")
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parser.add_argument("--output_dir", type=str, help="output dir.")
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if args.ngpu == 0:
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paddle.set_device("cpu")
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elif args.ngpu > 0:
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paddle.set_device("gpu")
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else:
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print("ngpu should >= 0 !")
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evaluate(args)
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if __name__ == "__main__":
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main()
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