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"""Create a Hugging Face dataset from the JamendoLyrics dataset in its original layout.""" |
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import json |
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import logging |
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from pathlib import Path |
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import shutil |
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import datasets |
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import numpy as np |
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from scipy.sparse import csr_array |
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from scipy.sparse.csgraph import connected_components as scipy_connected_components |
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import soundfile as sf |
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logger = logging.getLogger(__name__) |
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logging.basicConfig(level=logging.INFO) |
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logger.setLevel(logging.DEBUG) |
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dataset_src = datasets.load_dataset( |
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"jamendolyrics/jam-alt", revision="v1.4.0", split="test" |
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) |
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language_fixes = ( |
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[json.loads(li) for li in Path("language_fixes.jsonl").read_text().splitlines()] |
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if Path("language_fixes.jsonl").exists() |
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else [] |
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) |
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OVERLAP_SOFT_THRESHOLD = 0.1 |
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OVERLAP_HARD_THRESHOLD = 0.2 |
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PADDING = 0.5 |
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MAX_DURATION = 20.0 |
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SUBSETS_DIR = Path(".") |
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SUBSETS = ["pure", "groups"] |
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features = datasets.Features( |
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{ |
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"song_name": datasets.Value("string"), |
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"file_name": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"song_language": datasets.Value("string"), |
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"line_indices": [datasets.Value("int64")], |
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"start": datasets.Value("float64"), |
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"end": datasets.Value("float64"), |
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"merged": datasets.Value("bool"), |
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"artist": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"genre": datasets.Value("string"), |
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"license_type": datasets.Value("string"), |
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} |
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) |
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def find_connected_components(matrix: np.ndarray) -> list[list[int]]: |
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num_components, labels = scipy_connected_components( |
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csgraph=csr_array(matrix), directed=False, return_labels=True |
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) |
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components = [np.where(labels == i)[0].tolist() for i in range(num_components)] |
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return sorted(components) |
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for subset in SUBSETS: |
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if (SUBSETS_DIR / subset).exists(): |
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shutil.rmtree(SUBSETS_DIR / subset) |
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records = [] |
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stats = [] |
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for item in dataset_src: |
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name = item["name"] |
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song_language = item["language"] |
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lines = item["lines"] |
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audio = item["audio"]["array"] |
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sr = item["audio"]["sampling_rate"] |
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audio_duration = len(audio) / sr |
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starts = np.array([line["start"] for line in lines], dtype=float) |
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ends = np.array([line["end"] for line in lines], dtype=float) |
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texts = [line["text"] for line in lines] |
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overlap_ends = np.minimum(ends[:, None], ends) |
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overlap_starts = np.maximum(starts[:, None], starts) |
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overlaps = np.maximum(0.0, overlap_ends - overlap_starts) |
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overlap_groups = find_connected_components(overlaps > OVERLAP_HARD_THRESHOLD) |
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for indices in overlap_groups: |
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group = [lines[i] for i in indices] |
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group_name = ( |
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f"{min(indices):03d}-{max(indices):03d}" |
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if len(indices) > 1 |
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else f"{indices[0]:03d}" |
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) |
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text = "\n".join(line["text"] for line in group) |
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group_starts = [line["start"] for line in group] |
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group_ends = [line["end"] for line in group] |
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start, end = min(group_starts), max(group_ends) |
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_, small_overlap_indices = np.where( |
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(overlaps[indices] > 0) & (overlaps[indices] <= OVERLAP_HARD_THRESHOLD) |
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) |
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for i in small_overlap_indices: |
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line = lines[i] |
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if line["start"] < end and line["end"] >= end: |
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msg = f"{name}: Group {group_name} has {overlaps[small_overlap_indices][:, indices].max():.2f} s overlap with line {i:03d}." |
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if end - line["start"] < OVERLAP_SOFT_THRESHOLD: |
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msg += " Ignoring" |
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else: |
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new_end = line["start"] + OVERLAP_SOFT_THRESHOLD |
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msg += f"\n Adjusting end from {end:.2f} to {new_end:.2f} ({new_end - end:.2f} s)" |
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end = new_end |
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logger.debug(msg) |
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non_group_indices = [i for i in range(len(lines)) if i not in indices] |
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l_limit = max( |
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[0.0] + [ends[i] + 0.1 for i in non_group_indices if ends[i] < end] |
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) |
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r_limit = min( |
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[audio_duration] |
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+ [starts[i] - 0.1 for i in non_group_indices if starts[i] > start] |
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) |
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max_total_pad = max(0.0, MAX_DURATION - (end - start)) |
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l_pad = min(max(0.0, start - l_limit), PADDING) |
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r_pad = min(max(0.0, r_limit - end), PADDING) |
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if l_pad + r_pad > max_total_pad: |
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l_pad = r_pad = min(l_pad, r_pad, max_total_pad / 2) |
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extra = max(0.0, max_total_pad - (l_pad + r_pad)) - 1e-3 |
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l_pad += extra / 2 |
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r_pad += extra / 2 |
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assert l_pad + r_pad < max_total_pad |
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start, end = start - l_pad, end + r_pad |
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duration = end - start |
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stats.append( |
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{ |
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"name": name, |
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"group_name": group_name, |
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"duration": duration, |
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"group_size": len(indices), |
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"excluded": False, |
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} |
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) |
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if duration > MAX_DURATION: |
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logger.info(f"Excluding segment {name}.{group_name} of duration {duration}") |
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stats[-1]["excluded"] = True |
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continue |
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start_frame, end_frame = round(start * sr), round(end * sr) |
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line_audio = audio[start_frame:end_frame] |
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file_name = f"{name}.{group_name}.flac" |
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language = song_language |
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for fix in language_fixes: |
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if fix["file_name"] == file_name: |
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assert fix["text"] == text, ( |
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f"Text mismatch for {file_name}: {fix['text']} != {text}" |
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) |
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language = fix["language"] |
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logger.debug( |
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f"{name}: Fixing language of group {group_name} to {language}" |
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) |
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if len(group) > 1: |
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subset_dir = SUBSETS_DIR / "groups" / language |
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else: |
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subset_dir = SUBSETS_DIR / "pure" / language |
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out_audio_path = subset_dir / "audio" / file_name |
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out_audio_path.parent.mkdir(parents=True, exist_ok=True) |
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sf.write(out_audio_path, line_audio, sr) |
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records.append( |
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{ |
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"song_name": name, |
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"file_name": str(out_audio_path.relative_to(subset_dir)), |
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"text": "\n".join(line["text"] for line in group), |
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"language": language, |
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"song_language": song_language, |
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"line_indices": indices, |
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"start": start, |
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"end": end, |
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"merged": len(group) > 1, |
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**{k: item[k] for k in ["artist", "title", "genre", "license_type"]}, |
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} |
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) |
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dataset_out = datasets.Dataset.from_list(records, features=features, split="test") |
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for config_name in ["pure", "groups"]: |
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for subset_language in ["en", "es", "de", "fr"]: |
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subset_dir = SUBSETS_DIR / config_name / subset_language |
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subset_dir.mkdir(exist_ok=True) |
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subset = dataset_out.filter(lambda x: x["language"] == subset_language) |
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if config_name == "pure": |
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subset = subset.filter(lambda x: not x["merged"]) |
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elif config_name == "groups": |
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subset = subset.filter(lambda x: x["merged"]) |
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subset.to_json(subset_dir / "metadata.jsonl") |
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