| import os |
| import json |
| from pydub import AudioSegment |
| from multiprocessing import Pool, cpu_count |
| from functools import partial |
| from tqdm import tqdm |
|
|
| def load_tsv(file_path): |
| with open(file_path, 'r') as file: |
| lines = file.readlines() |
| lines = lines[1:] |
| output = [] |
| for line in lines: |
| splitted = line.strip().split('\t') |
| output.append({ |
| "TRACK_ID": splitted[0], |
| "ARTIST_ID": splitted[1], |
| "ALBUM_ID": splitted[2], |
| "PATH": splitted[3], |
| "DURATION": float(splitted[4]), |
| "TAGS": splitted[5:], |
| }) |
| return output |
|
|
|
|
| def write_jsonl(data, output_file): |
| with open(output_file, 'w') as file: |
| for item in data: |
| json.dump(item, file) |
| file.write('\n') |
|
|
|
|
| def get_audio_info(audio_path): |
| """ |
| Extract duration (seconds), sample_rate (Hz), num_samples (int), |
| bit_depth (bits), and channels (int) from an audio file. |
| """ |
| audio = AudioSegment.from_file(audio_path) |
| duration = audio.duration_seconds |
| sample_rate = audio.frame_rate |
| num_samples = int(audio.frame_count()) |
| sample_width = audio.sample_width |
| bit_depth = sample_width * 8 |
| channels = audio.channels |
| return duration, sample_rate, num_samples, bit_depth, channels |
|
|
|
|
| def process_item(tsv_dict, prefix): |
| path = os.path.join(prefix, tsv_dict["PATH"]).replace(".mp3", ".low.flac") |
| try: |
| duration, sr, num_samples, bit_depth, channels = get_audio_info(path) |
| return { |
| "audio_path": path, |
| "label": tsv_dict["TAGS"], |
| "duration": duration, |
| "sample_rate": sr, |
| "num_samples": num_samples, |
| "bit_depth": bit_depth, |
| "channels": channels |
| } |
| except Exception as e: |
| print(f"Error reading {path}: {e}") |
| return None |
|
|
|
|
| def convert_mtg_to_jsonl(output_dir, task='MTGTop50'): |
| task2tsv = { |
| "MTGTop50": "top50tags", |
| "MTGGenre": "genre", |
| "MTGInstrument": "instrument", |
| "MTGMood": "moodtheme", |
| } |
| tsv_name = task2tsv[task] |
| |
| splits = { |
| 'train': f"mtg-jamendo-dataset/data/splits/split-0/autotagging_{tsv_name}-train.tsv", |
| 'val': f"mtg-jamendo-dataset/data/splits/split-0/autotagging_{tsv_name}-validation.tsv", |
| 'test': f"mtg-jamendo-dataset/data/splits/split-0/autotagging_{tsv_name}-test.tsv", |
| } |
| prefix = os.path.join(output_dir, "audio") |
|
|
| os.makedirs(output_dir, exist_ok=True) |
|
|
| for split, rel_path in splits.items(): |
| tsv_path = os.path.join(output_dir, rel_path) |
| tsv_list = load_tsv(tsv_path) |
| out_list = [] |
|
|
| |
| worker = partial(process_item, prefix=prefix) |
| with Pool(processes=cpu_count()) as pool: |
| for result in tqdm(pool.imap(worker, tsv_list), total=len(tsv_list), desc=f"Processing {task} {split}"): |
| if result: |
| out_list.append(result) |
|
|
| |
| out_file = os.path.join(output_dir, f"{task}.{split}.jsonl") |
| write_jsonl(out_list, out_file) |
| print(f"{split} done: {len(out_list)} items written to {out_file}") |
|
|
|
|
| if __name__ == "__main__": |
| output_base = "data/MTG" |
| tasks = ['MTGTop50', 'MTGGenre', 'MTGInstrument', 'MTGMood'] |
| for task in tasks: |
| convert_mtg_to_jsonl(output_base, task) |
| print("All tasks completed.") |
|
|
