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| import librosa | |
| import numpy as np | |
| import warnings | |
| # Ignore librosa warnings about reading standard wav files to keep console clean | |
| warnings.filterwarnings('ignore') | |
| def preprocess_audio(file_path, target_sr, chunk_duration, step_duration, trim_top_db=20): | |
| """ | |
| Reads a raw audio file, cleans it, and splits it into fixed-size overlapping chunks. | |
| Parameters: | |
| - file_path: Path to the .wav file. | |
| - target_sr: Target sample rate (16000Hz is standard for DL and fast). | |
| - chunk_duration: Length of each slice in seconds (e.g., 3.0). | |
| - step_duration: How far to slide the window in seconds (1.5s means 50% overlap). | |
| Returns: | |
| - np.array of shape (num_chunks, chunk_length) | |
| """ | |
| # STEP 1: Load & Resample (Handles Microphone Variations) | |
| y, sr = librosa.load(file_path, sr=target_sr, res_type='soxr_hq') | |
| # STEP 2: Silence Trimming (Handles Dead Space) | |
| y_trimmed, _ = librosa.effects.trim(y, top_db=trim_top_db) | |
| # STEP 3: Peak Normalization (Handles Volume Variations) | |
| max_amplitude = np.max(np.abs(y_trimmed)) | |
| if max_amplitude > 0: | |
| y_normalized = y_trimmed / max_amplitude | |
| else: | |
| y_normalized = y_trimmed | |
| # STEP 4: Windowing / Slicing (Solution 2 - Prevents Data Loss) | |
| chunk_length = int(target_sr * chunk_duration) | |
| step_length = int(target_sr * step_duration) | |
| chunks = [] | |
| # CASE A: The audio is shorter than our target chunk (e.g., 2 seconds) | |
| if len(y_normalized) < chunk_length: | |
| # Pad with zeros at the end to force it to be exactly 3 seconds | |
| pad_length = chunk_length - len(y_normalized) | |
| y_padded = np.pad(y_normalized, (0, pad_length), mode='constant') | |
| chunks.append(y_padded) | |
| # CASE B: The audio is longer (e.g., 7 seconds) -> Slice it! | |
| else: | |
| start = 0 | |
| while start + chunk_length <= len(y_normalized): | |
| chunks.append(y_normalized[start : start + chunk_length]) | |
| start += step_length | |
| # Catch the "tail" of the audio if it doesn't divide perfectly | |
| # We take the LAST 3 seconds of the file to ensure we don't miss the ending | |
| if start < len(y_normalized): | |
| chunks.append(y_normalized[-chunk_length:]) | |
| # Return as a 2D numpy array: shape = (number_of_chunks, samples_per_chunk) | |
| return np.array(chunks) |