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@ -21,6 +21,8 @@ import numpy as np
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import paddle
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import yaml
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from yacs.config import CfgNode
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from tqdm import tqdm
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import os
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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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@ -30,6 +32,8 @@ from paddlespeech.t2s.modules.normalizer import ZScore
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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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@ -41,9 +45,16 @@ def evaluate(args, fastspeech2_config):
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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, odim=odim, **fastspeech2_config["model"])
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idim=vocab_size, odim=odim, **fastspeech2_config["model"], 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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@ -65,7 +76,34 @@ def evaluate(args, fastspeech2_config):
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sentences, speaker_set = get_phn_dur(args.dur_file)
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merge_silence(sentences)
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for i, utt_id in enumerate(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 = [os.path.basename(str(str_path)) for str_path in train_wav_files]
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dev_wav_files = [os.path.basename(str(str_path)) for str_path in dev_wav_files]
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test_wav_files = [os.path.basename(str(str_path)) for str_path in test_wav_files]
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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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@ -82,21 +120,30 @@ def evaluate(args, fastspeech2_config):
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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([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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if args.dataset == "baker":
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num_train = 9800
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num_dev = 100
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if i in range(0, num_train):
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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 i in range(num_train, num_train + num_dev):
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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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else:
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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(phone_ids, durations=durations)
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mel = fastspeech2_inference(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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@ -109,6 +156,8 @@ def main():
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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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@ -127,6 +176,12 @@ def main():
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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",
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type=str,
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default=None,
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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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