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from datasets import load_dataset, Audio |
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import soundfile as sf, os, re, neologdn, librosa |
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from tqdm import tqdm |
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import shutil |
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def have(a): |
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return a is not None |
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def aorb(a, b): |
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return a if have(a) else b |
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dataset = load_dataset("Sin2pi/JA_audio_JA_text_180k_samples", trust_remote_code=True)["train"].filter(lambda sample: bool(sample["sentence" if "sentence" in sample else aorb("text", "transcription")])) |
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name = "JA_audio_JA_text_180k" |
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ouput_dir = "./datasets/" |
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out_file = 'metadata.csv' |
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os.makedirs(ouput_dir + name, exist_ok=True) |
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folder_path = ouput_dir + name |
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top_db=30 |
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def is_silent(mp3_file, threshold=0.025): |
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if not os.path.exists(mp3_file): |
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return True |
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y, sr = librosa.load(mp3_file, sr=None) |
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rms = librosa.feature.rms(y=y)[0] |
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return all(value < threshold for value in rms) |
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def remove_silence(input_file, output_file, top_db=top_db): |
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y, sr = sf.read(input_file) |
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intervals = librosa.effects.split(y, top_db=top_db) |
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y_trimmed = [] |
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for start, end in intervals: |
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y_trimmed.extend(y[start:end]) |
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if not os.path.exists(output_file): |
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sf.write(output_file, y_trimmed, sr) |
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with open(csv_file2, "a", encoding='utf-8') as f: |
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file_name = os.path.basename(output_file) |
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f.write(file_name + "\n") |
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def process_directory(input_dir, output_dir, top_db=top_db): |
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if not os.path.exists(output_dir): |
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os.makedirs(output_dir) |
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if not os.path.exists(removed_dir): |
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os.makedirs(removed_dir) |
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open(csv_file, 'w', encoding='utf-8').close() |
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open(csv_file2, 'w', encoding='utf-8').close() |
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for filename in os.listdir(input_dir): |
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if filename.endswith(".mp3"): |
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input_file = os.path.join(input_dir, filename) |
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output_file = os.path.join(output_dir, filename) |
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removed_file = os.path.join(removed_dir, filename) |
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if not os.path.exists(output_file): |
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remove_silence(input_file, output_file, top_db) |
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if os.path.exists(output_file) and is_silent(output_file): |
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with open(csv_file, "a", encoding='utf-8') as f: |
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f.write(os.path.basename(output_file) + "\n") |
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shutil.move(output_file, removed_file) |
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if os.path.exists(input_file): |
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os.remove(input_file) |
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input_dir = folder_path |
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output_dir = folder_path + "/trimmed/" |
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removed_dir = folder_path + "/removed/" |
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csv_file = folder_path + "/removed.csv" |
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csv_file2 = folder_path + "/not_removed.csv" |
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min_char = 4 |
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max = 20.0 |
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min = 1.0 |
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char = '[ ０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890♬♪♩♫]' |
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special_characters = '[“%‘”~゛＃＄％＆（）＊＋：；〈＝〉＠＾＿｛｜｝～"█』『.;:<>_()*&^$#@`, ]' |
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dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) |
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sentence_map = {} |
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open(os.path.join(folder_path, out_file), 'w', encoding='utf-8').close() |
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for i, sample in tqdm(enumerate(dataset)): |
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if sample["sentence"] != "": |
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audio_sample_name = name + f'_{i}.mp3' |
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audio_path_original = os.path.join(folder_path, audio_sample_name) |
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patterns = [(r"…",'。'), (r"!!",'!'), (special_characters,""), (r"\s+", "")] |
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for pattern, replace in patterns: |
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sample["sentence"] = re.sub(pattern, replace, sample["sentence"]) |
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sample["sentence"] = (neologdn.normalize(sample["sentence"], repeat=1)) |
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if sample["sentence"][-1] not in ["!", "?", "。"]: |
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sample["sentence"] += "。" |
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sentence_length = len(sample["sentence"]) |
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audio_length = len(sample['file_url' if "file_url" in sample else "audio"]["array"]) / sample['file_url' if "file_url" in sample else "audio"]["sampling_rate"] |
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if max > audio_length > min and not re.search(char, sample["sentence"]) and sentence_length > min_char and bool(sample["sentence"]): |
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if not os.path.exists(audio_path_original): |
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sf.write(audio_path_original, sample['file_url' if "file_url" in sample else "audio"]["array"], sample['file_url' if "file_url" in sample else "audio"]["sampling_rate"]) |
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sentence_map[audio_sample_name] = sample['sentence'] |
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print(f"Downloaded {len(sentence_map)} audio files to {folder_path}. Starting silence trimming...") |
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process_directory(input_dir, output_dir) |
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print(f"Silence trimming complete. Trimmed files are in {output_dir}, silent files moved to {removed_dir}.") |
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print(f"Generating final metadata.csv in {folder_path}...") |
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with open(csv_file2, 'r', encoding='utf-8') as f_not_removed: |
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for line in f_not_removed: |
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trimmed_filename = line.strip() |
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if trimmed_filename in sentence_map: |
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sentence = sentence_map[trimmed_filename] |
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with open(os.path.join(folder_path, out_file), 'a', encoding='utf-8') as transcription_file: |
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transcription_file.write(trimmed_filename + ",") |
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transcription_file.write(sentence) |
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transcription_file.write('\n') |
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print(f"Metadata.csv generated for {os.path.join(folder_path, out_file)}.") |
