import os

import numpy as np

import librosa

from tqdm import tqdm

SAMPLE_RATE = 22050

N_FFT = 1024

HOP_LENGTH = 256

WIN_LENGTH = 1024

N_MELS = 80

FMIN = 0

FMAX = 8000

def wav_to_mel(wav_path):

wav, sr = librosa.load(wav_path, sr=SAMPLE_RATE)

wav, _ = librosa.effects.trim(wav)

mel = librosa.feature.melspectrogram(

y=wav, sr=SAMPLE_RATE, n_fft=N_FFT, hop_length=HOP_LENGTH,

win_length=WIN_LENGTH, n_mels=N_MELS, fmin=FMIN, fmax=FMAX, power=1.0

)

mel = np.log(np.clip(mel, a_min=1e-5, a_max=None))

return mel.astype(np.float32)

def build_mel_dataset(wav_dir, mel_dir):

os.makedirs(mel_dir, exist_ok=True)

wav_files = [f for f in os.listdir(wav_dir) if f.lower().endswith(".wav")]

if not wav_files:

print("No wav files found!")

return

print(f"Processing {len(wav_files)} files")

success = 0

for wav_name in tqdm(wav_files):

try:

mel = wav_to_mel(os.path.join(wav_dir, wav_name))

np.save(os.path.join(mel_dir, f"{os.path.splitext(wav_name)[0]}.npy"), mel)

success += 1

except:

continue

print(f"Success: {success}/{len(wav_files)}")

if __name__ == "__main__":

build_mel_dataset(

wav_dir="/workspace/data/wavs",

mel_dir="/workspace/tts/DCTTS/data/mels"

)