First commit

README.md

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+---
+task_categories:
+- automatic-speech-recognition
+multilinguality:
+- multilingual
+language:
+- en
+- fr
+- de
+- es
+tags:
+- music
+- lyrics
+- evaluation
+- benchmark
+- transcription
+- pnc
+configs:
+- config_name: pure
+  data_files:
+  - split: test
+    path:
+      - pure/*/metadata.jsonl
+      - pure/*/audio/*.flac
+  default: true
+- config_name: pure-de
+  data_files:
+  - split: test
+    path:
+      - pure/de/metadata.jsonl
+      - pure/de/audio/*.flac
+- config_name: pure-en
+  data_files:
+  - split: test
+    path:
+      - pure/en/metadata.jsonl
+      - pure/en/audio/*.flac
+- config_name: pure-es
+  data_files:
+  - split: test
+    path:
+      - pure/es/metadata.jsonl
+      - pure/es/audio/*.flac
+- config_name: pure-fr
+  data_files:
+  - split: test
+    path:
+      - pure/fr/metadata.jsonl
+      - pure/fr/audio/*.flac
+- config_name: merged
+  data_files:
+  - split: test
+    path:
+      - pure/*/metadata.jsonl
+      - pure/*/audio/*.flac
+      - groups/*/metadata.jsonl
+      - groups/*/audio/*.flac
+- config_name: merged-de
+  data_files:
+  - split: test
+    path:
+      - pure/de/metadata.jsonl
+      - pure/de/audio/*.flac
+      - groups/de/metadata.jsonl
+      - groups/de/audio/*.flac
+- config_name: merged-en
+  data_files:
+  - split: test
+    path:
+      - pure/en/metadata.jsonl
+      - pure/en/audio/*.flac
+      - groups/en/metadata.jsonl
+      - groups/en/audio/*.flac
+- config_name: merged-es
+  data_files:
+  - split: test
+    path:
+      - pure/es/metadata.jsonl
+      - pure/es/audio/*.flac
+      - groups/es/metadata.jsonl
+      - groups/es/audio/*.flac
+- config_name: merged-fr
+  data_files:
+  - split: test
+    path:
+      - pure/fr/metadata.jsonl
+      - pure/fr/audio/*.flac
+      - groups/fr/metadata.jsonl
+      - groups/fr/audio/*.flac
+---
+
+# Jam-ALT Lines
+
+Jam-ALT Lines is a line-level version of the [Jam-ALT](https://huggingface.co/datasets/jamendolyrics/jam-alt/) **lyrics transcription** dataset.
+Unlike Jam-ALT, this dataset contains one audio segment for each lyrics line, facilitating research that considers each line as a separate unit.
+
+> [!tip]
+> See the [Jam-ALT project website](https://audioshake.github.io/jam-alt/) for details and the [JamendoLyrics community](https://huggingface.co/jamendolyrics) for related datasets.
+
+## Dataset flavors
+ 
+Lyrics lines may overlap in time, which makes it impossible to have a one-to-one correspondence between lines and audio segments
+while including a complete and accurate transcription for each. For this reason, the dataset comes in two **flavors**:
+- `pure`, containing only lines without significant temporal overlap with other lines;
+- `merged`, where (transitively) overlapping lines are grouped.
+
+Both of these strategies ensure that no two segments overlap, and hence all singing in each segment is completely transcribed.
+
+The default is `pure`, i.e. `load_dataset("jamendolyrics/jam-alt-lines", split="test")` will only contain lines without overlap.
+Use `load_dataset("jamendolyrics/jam-alt-lines", "merged", split="test")` to include segments corresponding to overlapping line groups.
+
+Each flavor is further divided into subsets by language: `pure-en`, `pure-es`, `pure-de`, `pure-fr`, `merged-en`, `merged-es`, `merged-de`, `merged-fr`.
+(This is done according to the [`language` column](#columns), which reflects the language of each _line_.)
+
+## Columns
+
+- `song_name`: the name of the song (`name` in Jam-ALT)
+- `audio`: the audio of the segment
+- `text`: the transcript of the segment
+- `language`: the language of the segment; this can be different from the language of the song, but only if the line is _entirely and clearly_ in a different language
+- `song_language`: the language of the song (`language` in Jam-ALT)
+- `line_indices`: the indices of the lines included in the segment
+- `start_time`: the start time of the segment in the song
+- `end_time`: the end time of the segment in the song
+- `merged`: true if the segment is composed of a group of overlapping lines
+- `artist`, `title`, `genre`, `license_type`: song metadata
+
+## Dataset creation process
+
+The dataset was generated quasi-automatically from Jam-ALT using the [`create_dataset.py`](create_dataset.py) and [`fix_languages.py`](fix_languages.py) scripts.
+
+1. The `start`, `end`, and `text` of each lyrics line was extracted.
+2. Overlaps were handled as follows:
+   - Groups of lines with large overlaps (> 0.2 s) were removed (`pure` flavor) or merged to form a single segment (`merged` flavor).
+   - Small overlaps (≤ 0.2 s) were reduced (to at most 0.1 s) by shortening the first of each pair of overlapping segments.
+3. A padding of up to 0.5 s was added on each side of a segment where possible without creating or increasing overlaps. This was done to counteract inaccuracies in the original timestamps.
+4. The audio between the adjusted start and end time was extracted.
+5. The language of the segment was estimated from the text, and if not matching the language of the song with sufficient confidence, it was checked and corrected manually. The language was changed from the language of the song only if the segment was _entirely and clearly_ in one of the other four languages of the dataset (`en`, `es`, `de`, `fr`). Otherwise, it was set to the language of the song.
+
+## Citation
+
+When using the dataset, please cite the Jam-ALT [ISMIR paper](https://doi.org/10.5281/zenodo.14877443) as well as the [ICME workshop paper](https://arxiv.org/abs/2506.15514), which introduced the line-level timings.
+Consider also citing the [JamendoLyrics paper](https://arxiv.org/abs/2306.07744). 
+```bibtex
+@inproceedings{cifka-2024-jam-alt,
+  author       = {Ond{\v{r}}ej C{\'{\i}}fka and
+                  Hendrik Schreiber and
+                  Luke Miner and
+                  Fabian{-}Robert St{\"{o}}ter},
+  title        = {Lyrics Transcription for Humans: {A} Readability-Aware Benchmark},
+  booktitle    = {Proceedings of the 25th International Society for 
+                  Music Information Retrieval Conference},
+  pages        = {737--744},
+  year         = 2024,
+  publisher    = {ISMIR},
+  doi          = {10.5281/ZENODO.14877443},
+  url          = {https://doi.org/10.5281/zenodo.14877443}
+}
+@inproceedings{syed-2025-mss-alt,
+  author       = {Jaza Syed and
+                  Ivan Meresman-Higgs and
+                  Ond{\v{r}}ej C{\'{\i}}fka and
+                  Mark Sandler},
+  title        = {Exploiting Music Source Separation for Automatic Lyrics Transcription with {Whisper}},
+  booktitle    = {2025 {IEEE} International Conference on Multimedia and Expo Workshops (ICMEW)},
+  publisher    = {IEEE},
+  year         = {2025}
+}
+@inproceedings{durand-2023-contrastive,
+  author={Durand, Simon and Stoller, Daniel and Ewert, Sebastian},
+  booktitle={2023 {IEEE} International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
+  title={Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages}, 
+  year={2023},
+  pages={1-5},
+  address={Rhodes Island, Greece},
+  doi={10.1109/ICASSP49357.2023.10096725}
+}
+```

create_dataset.py

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+"""Create a Hugging Face dataset from the JamendoLyrics dataset in its original layout."""
+
+# %%
+import json
+import logging
+from pathlib import Path
+import shutil
+
+import datasets
+import numpy as np
+from scipy.sparse import csr_array
+from scipy.sparse.csgraph import connected_components as scipy_connected_components
+import soundfile as sf
+
+# %%
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+logger.setLevel(logging.DEBUG)
+
+# %%
+dataset_src = datasets.load_dataset(
+    "jamendolyrics/jam-alt", revision="v1.4.0", split="test"
+)
+
+# %%
+language_fixes = (
+    [json.loads(li) for li in Path("language_fixes.jsonl").read_text().splitlines()]
+    if Path("language_fixes.jsonl").exists()
+    else []
+)
+
+# %%
+OVERLAP_SOFT_THRESHOLD = 0.1
+OVERLAP_HARD_THRESHOLD = 0.2
+PADDING = 0.5
+MAX_DURATION = 20.0
+
+SUBSETS_DIR = Path(".")
+SUBSETS = ["pure", "groups"]
+
+# %%
+features = datasets.Features(
+    {
+        "song_name": datasets.Value("string"),
+        "file_name": datasets.Value("string"),
+        "text": datasets.Value("string"),
+        "language": datasets.Value("string"),
+        "song_language": datasets.Value("string"),
+        "line_indices": [datasets.Value("int64")],
+        "start": datasets.Value("float64"),
+        "end": datasets.Value("float64"),
+        "merged": datasets.Value("bool"),
+        "artist": datasets.Value("string"),
+        "title": datasets.Value("string"),
+        "genre": datasets.Value("string"),
+        "license_type": datasets.Value("string"),
+    }
+)
+
+
+# %%
+def find_connected_components(matrix: np.ndarray) -> list[list[int]]:
+    num_components, labels = scipy_connected_components(
+        csgraph=csr_array(matrix), directed=False, return_labels=True
+    )
+    components = [np.where(labels == i)[0].tolist() for i in range(num_components)]
+    return sorted(components)
+
+
+# $$
+for subset in SUBSETS:
+    if (SUBSETS_DIR / subset).exists():
+        shutil.rmtree(SUBSETS_DIR / subset)
+
+
+# %%
+records = []
+stats = []
+
+for item in dataset_src:
+    name = item["name"]
+    song_language = item["language"]
+    lines = item["lines"]
+    audio = item["audio"]["array"]
+    sr = item["audio"]["sampling_rate"]
+    audio_duration = len(audio) / sr
+
+    starts = np.array([line["start"] for line in lines], dtype=float)
+    ends = np.array([line["end"] for line in lines], dtype=float)
+    texts = [line["text"] for line in lines]
+
+    # Compute pairwise overlaps
+    overlap_ends = np.minimum(ends[:, None], ends)
+    overlap_starts = np.maximum(starts[:, None], starts)
+    overlaps = np.maximum(0.0, overlap_ends - overlap_starts)
+
+    overlap_groups = find_connected_components(overlaps > OVERLAP_HARD_THRESHOLD)
+
+    for indices in overlap_groups:
+        group = [lines[i] for i in indices]
+        group_name = (
+            f"{min(indices):03d}-{max(indices):03d}"
+            if len(indices) > 1
+            else f"{indices[0]:03d}"
+        )
+
+        text = "\n".join(line["text"] for line in group)
+
+        group_starts = [line["start"] for line in group]
+        group_ends = [line["end"] for line in group]
+        start, end = min(group_starts), max(group_ends)
+
+        # Handle soft overlaps
+        _, small_overlap_indices = np.where(
+            (overlaps[indices] > 0) & (overlaps[indices] <= OVERLAP_HARD_THRESHOLD)
+        )
+        for i in small_overlap_indices:
+            line = lines[i]
+            if line["start"] < end and line["end"] >= end:
+                msg = f"{name}: Group {group_name} has {overlaps[small_overlap_indices][:, indices].max():.2f} s overlap with line {i:03d}."
+                if end - line["start"] < OVERLAP_SOFT_THRESHOLD:
+                    msg += " Ignoring"
+                else:
+                    new_end = line["start"] + OVERLAP_SOFT_THRESHOLD
+                    msg += f"\n    Adjusting end from {end:.2f} to {new_end:.2f} ({new_end - end:.2f} s)"
+                    end = new_end
+                logger.debug(msg)
+
+        # Pad segment without exceeding duration and causing overlaps
+        non_group_indices = [i for i in range(len(lines)) if i not in indices]
+        l_limit = max(
+            [0.0] + [ends[i] + 0.1 for i in non_group_indices if ends[i] < end]
+        )
+        r_limit = min(
+            [audio_duration]
+            + [starts[i] - 0.1 for i in non_group_indices if starts[i] > start]
+        )
+
+        max_total_pad = max(0.0, MAX_DURATION - (end - start))
+        l_pad = min(max(0.0, start - l_limit), PADDING)
+        r_pad = min(max(0.0, r_limit - end), PADDING)
+
+        if l_pad + r_pad > max_total_pad:
+            l_pad = r_pad = min(l_pad, r_pad, max_total_pad / 2)
+            extra = max(0.0, max_total_pad - (l_pad + r_pad)) - 1e-3
+            l_pad += extra / 2
+            r_pad += extra / 2
+            assert l_pad + r_pad < max_total_pad
+
+        start, end = start - l_pad, end + r_pad
+        duration = end - start
+
+        stats.append(
+            {
+                "name": name,
+                "group_name": group_name,
+                "duration": duration,
+                "group_size": len(indices),
+                "excluded": False,
+            }
+        )
+
+        if duration > MAX_DURATION:
+            logger.info(f"Excluding segment {name}.{group_name} of duration {duration}")
+            stats[-1]["excluded"] = True
+            continue
+
+        start_frame, end_frame = round(start * sr), round(end * sr)
+        line_audio = audio[start_frame:end_frame]
+
+        file_name = f"{name}.{group_name}.flac"
+
+        language = song_language
+        for fix in language_fixes:
+            if fix["file_name"] == file_name:
+                assert fix["text"] == text, (
+                    f"Text mismatch for {file_name}: {fix['text']} != {text}"
+                )
+                language = fix["language"]
+                logger.debug(
+                    f"{name}: Fixing language of group {group_name} to {language}"
+                )
+
+        if len(group) > 1:
+            subset_dir = SUBSETS_DIR / "groups" / language
+        else:
+            subset_dir = SUBSETS_DIR / "pure" / language
+        out_audio_path = subset_dir / "audio" / file_name
+        out_audio_path.parent.mkdir(parents=True, exist_ok=True)
+        sf.write(out_audio_path, line_audio, sr)
+
+        records.append(
+            {
+                "song_name": name,
+                "file_name": str(out_audio_path.relative_to(subset_dir)),
+                "text": "\n".join(line["text"] for line in group),
+                "language": language,
+                "song_language": song_language,
+                "line_indices": indices,
+                "start": start,
+                "end": end,
+                "merged": len(group) > 1,
+                **{k: item[k] for k in ["artist", "title", "genre", "license_type"]},
+            }
+        )
+
+dataset_out = datasets.Dataset.from_list(records, features=features, split="test")
+
+
+# %%
+for config_name in ["pure", "groups"]:
+    for subset_language in ["en", "es", "de", "fr"]:
+        subset_dir = SUBSETS_DIR / config_name / subset_language
+        subset_dir.mkdir(exist_ok=True)
+
+        subset = dataset_out.filter(lambda x: x["language"] == subset_language)
+        if config_name == "pure":
+            subset = subset.filter(lambda x: not x["merged"])
+        elif config_name == "groups":
+            subset = subset.filter(lambda x: x["merged"])
+
+        subset.to_json(subset_dir / "metadata.jsonl")

fix_languages.py

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+# %%
+from pathlib import Path
+import json
+import logging
+
+import datasets
+import lingua
+
+# %%
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+# %%
+language_detector = lingua.LanguageDetectorBuilder.from_languages(
+    lingua.Language.ENGLISH,
+    lingua.Language.SPANISH,
+    lingua.Language.GERMAN,
+    lingua.Language.FRENCH,
+).build()
+
+# %%
+fixes = []
+language_cache = {}
+for path in sorted(Path(".").glob("**/metadata.jsonl")):
+    dataset = datasets.load_dataset(
+        "json",
+        data_files=str(path),
+        split="train",
+    )
+    for record in dataset:
+        cache_key = (record["song_name"], record["text"].strip())
+        if cache_key in language_cache:
+            language = language_cache[cache_key]
+        else:
+            language = record["language"]
+            detected_languages = {
+                x.language.iso_code_639_1.name.lower(): x.value
+                for x in language_detector.compute_language_confidence_values(
+                    record["text"]
+                )
+            }
+            detected_languages = {
+                lg: p for lg, p in detected_languages.items() if p > 0.4
+            }
+            if record["language"] not in detected_languages:
+                print(
+                    f"{record['song_name']}: Detected languages {detected_languages} instead of "
+                    f"{repr(language)} for line:\n{record['text'].strip()}"
+                )
+                language = input(f"Enter language [{language}]: ").strip() or language
+                print()
+
+            language_cache[cache_key] = language
+
+        if language != record["song_language"]:
+            fixes.append(
+                {
+                    "file_name": record["file_name"],
+                    "text": record["text"],
+                    "language": language,
+                }
+            )
+
+Path("language_fixes.jsonl").write_text(
+    "\n".join(json.dumps(f) for f in fixes) + "\n", encoding="utf-8"
+)

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groups/de/metadata.jsonl

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+{"song_name":"Der_Musiker_-_d-music","file_name":"audio\/Der_Musiker_-_d-music.064-065.flac","text":"Sind der h\u00f6chste Lohn\n\n\n(Uh, ah-ja, eh-jo)\n","language":"de","song_language":"de","line_indices":[64,65],"start":147.01,"end":154.88,"merged":true,"artist":"D.","title":"Der Musiker","genre":"Hip-Hop","license_type":"CC BY-NC-SA"}
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