[tts] add gen_gta_mel.py for finetuning speedypeech, test=tts

paddlespeech/t2s/exps/speedyspeech/gen_gta_mel.py

+# 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.
+# generate mels using durations.txt
+# for mb melgan finetune
+# 长度和原本的 mel 不一致怎么办？
+import argparse
+from pathlib import Path
+
+import numpy as np
+import paddle
+import yaml
+from yacs.config import CfgNode
+from tqdm import tqdm
+import os
+
+from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
+from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
+from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
+from paddlespeech.t2s.models.speedyspeech import SpeedySpeechInference
+from paddlespeech.t2s.modules.normalizer import ZScore
+from paddlespeech.t2s.frontend.zh_frontend import Frontend
+
+def evaluate(args, speedyspeech_config):
+    rootdir = Path(args.rootdir).expanduser()
+    assert rootdir.is_dir()
+
+    # construct dataset for evaluation
+    with open(args.phones_dict, "r") as f:
+        phn_id = [line.strip().split() for line in f.readlines()]
+    vocab_size = len(phn_id)
+    print("vocab_size:", vocab_size)
+
+    phone_dict = {}
+    for phn, id in phn_id:
+        phone_dict[phn] = int(id)
+
+    with open(args.tones_dict, "r") as f:
+        tone_id = [line.strip().split() for line in f.readlines()]
+    tone_size = len(tone_id)
+    print("tone_size:", tone_size)
+
+    frontend = Frontend(phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
+
+    if args.speaker_dict:
+        with open(args.speaker_dict, 'rt') as f:
+            spk_id_list = [line.strip().split() for line in f.readlines()]
+            spk_num = len(spk_id_list)
+    else:
+        spk_num=None
+
+    model = SpeedySpeech(
+        vocab_size=vocab_size, tone_size=tone_size, **speedyspeech_config["model"], spk_num=spk_num)
+
+    model.set_state_dict(
+        paddle.load(args.speedyspeech_checkpoint)["main_params"])
+    model.eval()
+
+    stat = np.load(args.speedyspeech_stat)
+    mu, std = stat
+    mu = paddle.to_tensor(mu)
+    std = paddle.to_tensor(std)
+    speedyspeech_normalizer = ZScore(mu, std)
+
+    speedyspeech_inference = SpeedySpeechInference(speedyspeech_normalizer,
+                                                      model)
+    speedyspeech_inference.eval()
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    sentences, speaker_set = get_phn_dur(args.dur_file)
+    merge_silence(sentences)
+
+    if args.dataset == "baker":
+        wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
+        # split data into 3 sections
+        num_train = 9800
+        num_dev = 100
+        train_wav_files = wav_files[:num_train]
+        dev_wav_files = wav_files[num_train:num_train + num_dev]
+        test_wav_files = wav_files[num_train + num_dev:]
+    elif args.dataset == "aishell3":
+        sub_num_dev = 5
+        wav_dir = rootdir / "train" / "wav"
+        train_wav_files = []
+        dev_wav_files = []
+        test_wav_files = []
+        for speaker in os.listdir(wav_dir):
+            wav_files = sorted(list((wav_dir / speaker).rglob("*.wav")))
+            if len(wav_files) > 100:
+                train_wav_files += wav_files[:-sub_num_dev * 2]
+                dev_wav_files += wav_files[-sub_num_dev * 2:-sub_num_dev]
+                test_wav_files += wav_files[-sub_num_dev:]
+            else:
+                train_wav_files += wav_files
+
+    train_wav_files = [os.path.basename(str(str_path)) for str_path in train_wav_files]
+    dev_wav_files = [os.path.basename(str(str_path)) for str_path in dev_wav_files]
+    test_wav_files = [os.path.basename(str(str_path)) for str_path in test_wav_files]
+
+    for i, utt_id in enumerate(tqdm(sentences)):
+        phones = sentences[utt_id][0]
+        durations = sentences[utt_id][1]
+        speaker = sentences[utt_id][2]
+        # 裁剪掉开头和结尾的 sil
+        if args.cut_sil:
+            if phones[0] == "sil" and len(durations) > 1:
+                durations = durations[1:]
+                phones = phones[1:]
+            if phones[-1] == 'sil' and len(durations) > 1:
+                durations = durations[:-1]
+                phones = phones[:-1]
+
+        phones, tones = frontend._get_phone_tone(
+                phones, get_tone_ids=True)
+        if tones:
+            tone_ids = frontend._t2id(tones)
+            tone_ids = paddle.to_tensor(tone_ids)
+        if phones:
+            phone_ids = frontend._p2id(phones)
+            phone_ids = paddle.to_tensor(phone_ids)
+
+        if args.speaker_dict:
+            speaker_id = int([item[1] for item in spk_id_list if speaker == item[0]][0])    
+            speaker_id = paddle.to_tensor(speaker_id)
+        else:
+            speaker_id = None
+
+        durations = paddle.to_tensor(np.array(durations))
+        # 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
+        # split data into 3 sections
+
+        wav_path = utt_id + ".wav"
+
+        if wav_path in train_wav_files:
+            sub_output_dir = output_dir / ("train/raw")
+        elif wav_path in dev_wav_files:
+            sub_output_dir = output_dir / ("dev/raw")
+        elif wav_path in test_wav_files:
+            sub_output_dir = output_dir / ("test/raw")
+
+        sub_output_dir.mkdir(parents=True, exist_ok=True)
+
+        with paddle.no_grad():
+            mel = speedyspeech_inference(phone_ids, tone_ids, spk_id=speaker_id)
+        np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
+
+
+def main():
+    # parse args and config and redirect to train_sp
+    parser = argparse.ArgumentParser(
+        description="Synthesize with speedyspeech & parallel wavegan.")
+    parser.add_argument(
+        "--dataset",
+        default="baker",
+        type=str,
+        help="name of dataset, should in {baker, ljspeech, vctk} now")
+    parser.add_argument(
+        "--rootdir", default=None, type=str, help="directory to dataset.")
+    parser.add_argument(
+        "--speedyspeech-config", type=str, help="speedyspeech config file.")
+    parser.add_argument(
+        "--speedyspeech-checkpoint",
+        type=str,
+        help="speedyspeech checkpoint to load.")
+    parser.add_argument(
+        "--speedyspeech-stat",
+        type=str,
+        help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
+    )
+
+    parser.add_argument(
+        "--phones-dict",
+        type=str,
+        default="phone_id_map.txt",
+        help="phone vocabulary file.")
+    parser.add_argument(
+        "--tones-dict",
+        type=str,
+        default="tone_id_map.txt",
+        help="tone vocabulary file.")
+    parser.add_argument(
+        "--speaker-dict",
+        type=str,
+        default=None,
+        help="speaker id map file.")
+
+    parser.add_argument(
+        "--dur-file", default=None, type=str, help="path to durations.txt.")
+    parser.add_argument("--output-dir", type=str, help="output dir.")
+    parser.add_argument(
+        "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
+
+    def str2bool(str):
+        return True if str.lower() == 'true' else False
+
+    parser.add_argument(
+        "--cut-sil",
+        type=str2bool,
+        default=True,
+        help="whether cut sil in the edge of audio")
+
+    args = parser.parse_args()
+
+    if args.ngpu == 0:
+        paddle.set_device("cpu")
+    elif args.ngpu > 0:
+        paddle.set_device("gpu")
+    else:
+        print("ngpu should >= 0 !")
+
+    with open(args.speedyspeech_config) as f:
+        speedyspeech_config = CfgNode(yaml.safe_load(f))
+
+    print("========Args========")
+    print(yaml.safe_dump(vars(args)))
+    print("========Config========")
+    print(speedyspeech_config)
+
+    evaluate(args, speedyspeech_config)
+
+
+if __name__ == "__main__":
+    main()