Datasets:

Illia56
/

AudioNormalizationDatasetRVC

import os
import librosa
import numpy as np
import soundfile as sf
from tqdm import tqdm  # Import tqdm for progress tracking
from concurrent.futures import ProcessPoolExecutor  # Import for parallel processing

# Define the directory containing the audio files
audio_dir = 'audio'
output_dir = 'processed_audio'

# Create output directory if it doesn't exist
os.makedirs(output_dir, exist_ok=True)

# Function to load audio
def load_audio(file_path):
    audio, sr = librosa.load(file_path, sr=None)  # Load with original sampling rate
    return audio, sr

# Function to normalize audio
def normalize_audio(audio):
    return audio / np.max(np.abs(audio))

# Function to save audio in a compressed format
def save_audio(output_path, audio, sr):
    # Save the processed audio as MP3 to reduce file size
    sf.write(output_path.replace('.wav', '.mp3'), audio, sr, format='MP3')  # Save as MP3

# Function to process a single audio file
def process_audio(file_path):
    audio, sr = load_audio(file_path)  # Load the audio file
    audio = normalize_audio(audio)      # Normalize audio
    output_path = os.path.join(output_dir, os.path.basename(file_path).replace('.mp3', '.wav'))  # Save as .wav
    save_audio(output_path, audio, sr)  # Save the processed audio
    return os.path.basename(file_path)  # Return the filename for logging

if __name__ == '__main__':  # Protect the main execution
    # Get a list of all mp3 files in the audio directory
    audio_files = [os.path.join(audio_dir, filename) for filename in os.listdir(audio_dir) if filename.endswith('.mp3')]

    # Process audio files in parallel
    with ProcessPoolExecutor() as executor:
        for result in tqdm(executor.map(process_audio, audio_files), total=len(audio_files), desc="Processing files", unit="file"):
            print(f'Processed {result}')  # Log the processing

	import os
	import librosa
	import numpy as np
	import soundfile as sf
	from tqdm import tqdm # Import tqdm for progress tracking
	from concurrent.futures import ProcessPoolExecutor # Import for parallel processing

	# Define the directory containing the audio files
	audio_dir = 'audio'
	output_dir = 'processed_audio'

	# Create output directory if it doesn't exist
	os.makedirs(output_dir, exist_ok=True)

	# Function to load audio
	def load_audio(file_path):
	audio, sr = librosa.load(file_path, sr=None) # Load with original sampling rate
	return audio, sr

	# Function to normalize audio
	def normalize_audio(audio):
	return audio / np.max(np.abs(audio))

	# Function to save audio in a compressed format
	def save_audio(output_path, audio, sr):
	# Save the processed audio as MP3 to reduce file size
	sf.write(output_path.replace('.wav', '.mp3'), audio, sr, format='MP3') # Save as MP3

	# Function to process a single audio file
	def process_audio(file_path):
	audio, sr = load_audio(file_path) # Load the audio file
	audio = normalize_audio(audio) # Normalize audio
	output_path = os.path.join(output_dir, os.path.basename(file_path).replace('.mp3', '.wav')) # Save as .wav
	save_audio(output_path, audio, sr) # Save the processed audio
	return os.path.basename(file_path) # Return the filename for logging

	if __name__ == '__main__': # Protect the main execution
	# Get a list of all mp3 files in the audio directory
	audio_files = [os.path.join(audio_dir, filename) for filename in os.listdir(audio_dir) if filename.endswith('.mp3')]

	# Process audio files in parallel
	with ProcessPoolExecutor() as executor:
	for result in tqdm(executor.map(process_audio, audio_files), total=len(audio_files), desc="Processing files", unit="file"):
	print(f'Processed {result}') # Log the processing