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Datasets:
Illia56
/
AudioNormalizationDatasetRVC

Tasks:
Audio-to-Audio
Modalities:
Audio
Languages:
Russian
Size:
1K<n<10K
Tags:
audio
normalization
sound
music
speech
wav
License:
Dataset card Files
xet
Community
Illia56 commited on
Commit
f5ff449
·
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1 Parent(s): 513c44a

Upload 4 files

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Files changed (4) hide show
  1. main.ipynb +66 -0
  2. main.py +44 -0
  3. metadata.csv +303 -0
  4. processed_audio.zip +3 -0
main.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# I have 2 rar files in folder /input\n",
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+ "# I want to extract them to folder /output and merge all files there\n",
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+ "\n",
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+ "import rarfile\n",
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+ "import os\n",
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+ "\n",
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+ "def extract_rar_files(rar_file_path, output_dir):\n",
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+ " with rarfile.RarFile(rar_file_path) as rar:\n",
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+ " rar.extractall(output_dir)\n",
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+ "\n",
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+ "# extract_rar_files('./input/001.rar', './output')\n",
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+ "extract_rar_files('./input/002.rar', './output')"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import shutil\n",
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+ "\n",
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+ "def merge_folders(folder1, folder2, output_folder):\n",
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+ " if not os.path.exists(output_folder):\n",
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+ " os.makedirs(output_folder)\n",
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+ " \n",
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+ " for folder in [folder1, folder2]:\n",
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+ " for filename in os.listdir(folder):\n",
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+ " file_path = os.path.join(folder, filename)\n",
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+ " if os.path.isfile(file_path):\n",
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+ " shutil.copy(file_path, output_folder)\n",
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+ "\n",
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+ "merge_folders('./output/001', './output/002', './audio')\n"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "Python 3",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.9.6"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+ }
main.py ADDED
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+ import os
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+ import librosa
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+ import numpy as np
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+ import soundfile as sf
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+ from tqdm import tqdm # Import tqdm for progress tracking
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+ from concurrent.futures import ProcessPoolExecutor # Import for parallel processing
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+
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+ # Define the directory containing the audio files
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+ audio_dir = 'audio'
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+ output_dir = 'processed_audio'
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+
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+ # Create output directory if it doesn't exist
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+ os.makedirs(output_dir, exist_ok=True)
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+
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+ # Function to load audio
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+ def load_audio(file_path):
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+ audio, sr = librosa.load(file_path, sr=None) # Load with original sampling rate
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+ return audio, sr
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+
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+ # Function to normalize audio
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+ def normalize_audio(audio):
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+ return audio / np.max(np.abs(audio))
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+
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+ # Function to save audio in a compressed format
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+ def save_audio(output_path, audio, sr):
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+ # Save the processed audio as MP3 to reduce file size
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+ sf.write(output_path.replace('.wav', '.mp3'), audio, sr, format='MP3') # Save as MP3
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+
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+ # Function to process a single audio file
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+ def process_audio(file_path):
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+ audio, sr = load_audio(file_path) # Load the audio file
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+ audio = normalize_audio(audio) # Normalize audio
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+ output_path = os.path.join(output_dir, os.path.basename(file_path).replace('.mp3', '.wav')) # Save as .wav
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+ save_audio(output_path, audio, sr) # Save the processed audio
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+ return os.path.basename(file_path) # Return the filename for logging
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+
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+ if __name__ == '__main__': # Protect the main execution
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+ # Get a list of all mp3 files in the audio directory
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+ audio_files = [os.path.join(audio_dir, filename) for filename in os.listdir(audio_dir) if filename.endswith('.mp3')]
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+
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+ # Process audio files in parallel
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+ with ProcessPoolExecutor() as executor:
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+ for result in tqdm(executor.map(process_audio, audio_files), total=len(audio_files), desc="Processing files", unit="file"):
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+ print(f'Processed {result}') # Log the processing
metadata.csv ADDED
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