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import os
import torch
import torchaudio
import numpy as np
from utils import load_audio, preprocess_audio, extract_features
def preprocess_dataset(input_dir, output_dir, target_sample_rate=16000, target_length=16000, n_mfcc=13):
"""
Preprocess the entire dataset by loading, processing, and saving the audio files.
Args:
input_dir (str): Directory containing the raw audio files.
output_dir (str): Directory where the processed files will be saved.
target_sample_rate (int): Desired sample rate for the audio.
target_length (int): Desired length of the audio in samples.
n_mfcc (int): Number of MFCC features to extract.
"""
# Ensure output directory exists
os.makedirs(output_dir, exist_ok=True)
# Loop through all files in the input directory
for file_name in os.listdir(input_dir):
if file_name.endswith('.wav'):
# Load audio
file_path = os.path.join(input_dir, file_name)
waveform, sample_rate = load_audio(file_path)
# Preprocess audio
waveform = preprocess_audio(waveform, sample_rate, target_sample_rate, target_length)
# Extract features
features = extract_features(waveform, target_sample_rate, n_mfcc)
# Save processed features
output_path = os.path.join(output_dir, file_name.replace('.wav', '.pt'))
torch.save(features, output_path)
print(f"Processed and saved {file_name} to {output_path}")
def preprocess_single_file(file_path, target_sample_rate=16000, target_length=16000, n_mfcc=13):
"""
Preprocess a single audio file and return the processed features.
Args:
file_path (str): Path to the audio file.
target_sample_rate (int): Desired sample rate for the audio.
target_length (int): Desired length of the audio in samples.
n_mfcc (int): Number of MFCC features to extract.
Returns:
features (Tensor): Preprocessed and feature-extracted audio.
"""
# Load audio
waveform, sample_rate = load_audio(file_path)
# Preprocess audio
waveform = preprocess_audio(waveform, sample_rate, target_sample_rate, target_length)
# Extract features
features = extract_features(waveform, target_sample_rate, n_mfcc)
return features
# Example usage:
if __name__ == "__main__":
input_dir = "path/to/raw/audio"
output_dir = "path/to/processed/audio"
# Preprocess the entire dataset
preprocess_dataset(input_dir, output_dir)
# Preprocess a single file (for testing)
# features = preprocess_single_file("path/to/single/audio/file.wav")
# print(features.shape)
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