Delete melspec_gen.py

melspec_gen.py

@@ -1,61 +0,0 @@
-from scipy.io.wavfile import read
-import torch
-import numpy as np
-import os
-from multiprocessing import Pool
-#from tqdm import tqdm
-
-# Change here
-base="/mnt/beegfs/home/espinosa/wallon/wallon_ms_02/p02/"
-
-hann_window = {}
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    # data, sampling_rate = librosa.load(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-def get_audio(filename):
-    max_wave_length = 32768.0
-    filter_length = 1024
-    hop_length = 256
-    win_length = 1024
-    audio, sampling_rate = load_wav_to_torch(filename)
-    audio_norm = audio / max_wave_length
-    audio_norm = audio_norm.unsqueeze(0)
-    spec_filename = filename.replace(".wav", ".spec.pt")
-    spec = spectrogram_torch(audio_norm, filter_length,
-        sampling_rate, hop_length, win_length,
-        center=False)
-    spec = torch.squeeze(spec, 0)
-    torch.save(spec, spec_filename)
-
-if __name__=="__main__":
-    waves = []
-    for wav_name in os.listdir(base):
-        wav_path = os.path.join(base, wav_name)
-        if wav_path.endswith(".wav"):
-            waves.append(wav_path)
-            
-    for wav_path in waves:
-        get_audio(wav_path)