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| # Make function to find classes in target directory | |
| import os | |
| import librosa | |
| import torch | |
| import numpy as np | |
| from torchaudio.transforms import Resample | |
| SAMPLE_RATE = 44100 | |
| AUDIO_LEN = 2.90 | |
| # Parameters to control the MelSpec generation | |
| N_MELS = 128 | |
| F_MIN = 20 | |
| F_MAX = 16000 | |
| N_FFT = 1024 | |
| HOP_LEN = 512 | |
| # Make function to find classes in target directory | |
| def find_classes(directory: str): | |
| # 1. Get the class names by scanning the target directory | |
| classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir()) | |
| # 2. Raise an error if class names not found | |
| if not classes: | |
| raise FileNotFoundError(f"Couldn't find any classes in {directory}.") | |
| # 3. Crearte a dictionary of index labels (computers prefer numerical rather than string labels) | |
| class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)} | |
| return classes, class_to_idx | |
| def resample(wav, sample_rate, new_sample_rate): | |
| if wav.shape[0] >= 2: | |
| wav = torch.mean(wav, dim=0) | |
| else: | |
| wav = wav.squeeze(0) | |
| if sample_rate > new_sample_rate: | |
| resampler = Resample(sample_rate, new_sample_rate) | |
| wav = resampler(wav) | |
| return wav | |
| def mono_to_color(X, eps=1e-6, mean=None, std=None): | |
| X = np.stack([X, X, X], axis=-1) | |
| # Standardize | |
| mean = mean or X.mean() | |
| std = std or X.std() | |
| X = (X - mean) / (std + eps) | |
| # Normalize to [0, 255] | |
| _min, _max = X.min(), X.max() | |
| if (_max - _min) > eps: | |
| V = np.clip(X, _min, _max) | |
| V = 255 * (V - _min) / (_max - _min) | |
| V = V.astype(np.uint8) | |
| else: | |
| V = np.zeros_like(X, dtype=np.uint8) | |
| return V | |
| def normalize(image, mean=None, std=None): | |
| image = image / 255.0 | |
| if mean is not None and std is not None: | |
| image = (image - mean) / std | |
| return np.moveaxis(image, 2, 0).astype(np.float32) | |
| def compute_melspec(wav, sample_rate=SAMPLE_RATE): | |
| melspec = librosa.feature.melspectrogram( | |
| y=wav, | |
| sr=sample_rate, | |
| n_fft=N_FFT, | |
| fmin=F_MIN, | |
| fmax=F_MAX, | |
| n_mels=N_MELS, | |
| hop_length=HOP_LEN | |
| ) | |
| melspec = librosa.power_to_db(melspec).astype(np.float32) | |
| return melspec | |
| def audio_preprocess(wav, sample_rate): | |
| wav = wav.numpy() | |
| melspec = compute_melspec(wav, sample_rate) | |
| image = mono_to_color(melspec) | |
| image = normalize(image, mean=None, std=None) | |
| image = torch.from_numpy(image) | |
| return image |