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227 lines
7.4 KiB
227 lines
7.4 KiB
# 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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# generate mels using durations.txt
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# for mb melgan finetune
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import argparse
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import os
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from pathlib import Path
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import numpy as np
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import paddle
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import yaml
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from tqdm import tqdm
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
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from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import StyleFastSpeech2Inference
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from paddlespeech.t2s.modules.normalizer import ZScore
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from paddlespeech.t2s.utils import str2bool
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def evaluate(args, fastspeech2_config):
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rootdir = Path(args.rootdir).expanduser()
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assert rootdir.is_dir()
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# construct dataset for evaluation
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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phone_dict = {}
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for phn, id in phn_id:
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phone_dict[phn] = int(id)
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if args.speaker_dict:
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with open(args.speaker_dict, 'rt') as f:
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spk_id_list = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id_list)
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else:
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spk_num = None
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odim = fastspeech2_config.n_mels
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model = FastSpeech2(
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idim=vocab_size,
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odim=odim,
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**fastspeech2_config["model"],
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spk_num=spk_num)
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model.set_state_dict(
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paddle.load(args.fastspeech2_checkpoint)["main_params"])
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model.eval()
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stat = np.load(args.fastspeech2_stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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fastspeech2_normalizer = ZScore(mu, std)
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fastspeech2_inference = StyleFastSpeech2Inference(fastspeech2_normalizer,
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model)
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fastspeech2_inference.eval()
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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, speaker_set = get_phn_dur(args.dur_file)
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merge_silence(sentences)
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if args.dataset == "baker":
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wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
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# split data into 3 sections
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num_train = 9800
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num_dev = 100
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train_wav_files = wav_files[:num_train]
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dev_wav_files = wav_files[num_train:num_train + num_dev]
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test_wav_files = wav_files[num_train + num_dev:]
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elif args.dataset == "aishell3":
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sub_num_dev = 5
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wav_dir = rootdir / "train" / "wav"
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train_wav_files = []
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dev_wav_files = []
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test_wav_files = []
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for speaker in os.listdir(wav_dir):
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wav_files = sorted(list((wav_dir / speaker).rglob("*.wav")))
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if len(wav_files) > 100:
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train_wav_files += wav_files[:-sub_num_dev * 2]
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dev_wav_files += wav_files[-sub_num_dev * 2:-sub_num_dev]
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test_wav_files += wav_files[-sub_num_dev:]
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else:
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train_wav_files += wav_files
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train_wav_files = [
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os.path.basename(str(str_path)) for str_path in train_wav_files
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]
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dev_wav_files = [
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os.path.basename(str(str_path)) for str_path in dev_wav_files
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]
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test_wav_files = [
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os.path.basename(str(str_path)) for str_path in test_wav_files
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]
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for i, utt_id in enumerate(tqdm(sentences)):
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phones = sentences[utt_id][0]
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durations = sentences[utt_id][1]
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speaker = sentences[utt_id][2]
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# 裁剪掉开头和结尾的 sil
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if args.cut_sil:
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if phones[0] == "sil" and len(durations) > 1:
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durations = durations[1:]
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phones = phones[1:]
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if phones[-1] == 'sil' and len(durations) > 1:
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durations = durations[:-1]
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phones = phones[:-1]
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# sentences[utt_id][0] = phones
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# sentences[utt_id][1] = durations
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phone_ids = [phone_dict[phn] for phn in phones]
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phone_ids = paddle.to_tensor(np.array(phone_ids))
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if args.speaker_dict:
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speaker_id = int(
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[item[1] for item in spk_id_list if speaker == item[0]][0])
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speaker_id = paddle.to_tensor(speaker_id)
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else:
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speaker_id = None
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durations = paddle.to_tensor(np.array(durations))
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# 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
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# split data into 3 sections
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wav_path = utt_id + ".wav"
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if wav_path in train_wav_files:
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sub_output_dir = output_dir / ("train/raw")
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elif wav_path in dev_wav_files:
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sub_output_dir = output_dir / ("dev/raw")
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elif wav_path in test_wav_files:
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sub_output_dir = output_dir / ("test/raw")
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sub_output_dir.mkdir(parents=True, exist_ok=True)
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with paddle.no_grad():
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mel = fastspeech2_inference(
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phone_ids, durations=durations, spk_id=speaker_id)
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np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(
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description="Synthesize with fastspeech2 & parallel wavegan.")
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parser.add_argument(
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"--dataset",
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default="baker",
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type=str,
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help="name of dataset, should in {baker, ljspeech, vctk} now")
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parser.add_argument(
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"--rootdir", default=None, type=str, help="directory to dataset.")
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parser.add_argument(
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"--fastspeech2-config", type=str, help="fastspeech2 config file.")
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parser.add_argument(
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"--fastspeech2-checkpoint",
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type=str,
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help="fastspeech2 checkpoint to load.")
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parser.add_argument(
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"--fastspeech2-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
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)
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parser.add_argument(
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"--phones-dict",
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type=str,
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default="phone_id_map.txt",
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help="phone 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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"--dur-file", default=None, type=str, help="path to durations.txt.")
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parser.add_argument("--output-dir", type=str, help="output dir.")
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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(
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"--cut-sil",
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type=str2bool,
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default=True,
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help="whether cut sil in the edge of audio")
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args = parser.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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with open(args.fastspeech2_config) as f:
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fastspeech2_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(fastspeech2_config)
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evaluate(args, fastspeech2_config)
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if __name__ == "__main__":
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main()
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