| import os | |
| import librosa | |
| import numpy as np | |
| from scipy.io import wavfile | |
| from tqdm import tqdm | |
| from text import _clean_text | |
| def prepare_align(config): | |
| in_dir = config["path"]["corpus_path"] | |
| out_dir = config["path"]["raw_path"] | |
| sampling_rate = config["preprocessing"]["audio"]["sampling_rate"] | |
| max_wav_value = config["preprocessing"]["audio"]["max_wav_value"] | |
| cleaners = config["preprocessing"]["text"]["text_cleaners"] | |
| speaker = "EmoV_DB" | |
| with open(os.path.join(in_dir, "metadata.csv"), encoding="utf-8") as f: | |
| for line in tqdm(f): | |
| parts = line.strip().split("|") | |
| base_name = parts[0] | |
| text = parts[1] | |
| text = _clean_text(text, cleaners) | |
| wav_path = os.path.join(in_dir, "wavs", "{}.wav".format(base_name)) | |
| if os.path.exists(wav_path): | |
| os.makedirs(os.path.join(out_dir, speaker), exist_ok=True) | |
| wav, _ = librosa.load(wav_path, sampling_rate) | |
| wav = wav / max(abs(wav)) * max_wav_value | |
| wavfile.write( | |
| os.path.join(out_dir, speaker, "{}.wav".format(base_name)), | |
| sampling_rate, | |
| wav.astype(np.int16), | |
| ) | |
| with open( | |
| os.path.join(out_dir, speaker, "{}.lab".format(base_name)), | |
| "w", | |
| ) as f1: | |
| f1.write(text) | |