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README.md

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+---
+license: cc-by-4.0
+language:
+  - es
+pretty_name: HABLA
+task_categories:
+  - audio-classification
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: "data/test-*.parquet"
+tags:
+  - anti-spoofing
+  - audio-deepfake-detection
+  - speech
+  - benchmark
+  - arena-ready
+arxiv:
+  - "10.21437/Interspeech.2023-2272"
+---
+
+# HABLA
+
+Benchmark-ready packaging of **HABLA** — *HABLA: A Dataset of Latin American Spanish Accents for Voice Anti-spoofing* (Tamayo Flórez, Manrique & Pereira Nunes, Interspeech 2023). The first voice anti-spoofing collection in **Spanish**, covering five Latin-American accents (Argentina, Chile, Colombia, Peru, Venezuela).
+
+## Overview
+
+The task is binary classification: **bonafide** (genuine human Spanish speech) vs. **spoof** (synthetic / converted speech). Bonafide audio is real Latin-American Spanish; spoof audio is produced by several generators:
+
+- **CycleGAN**, **Diff**(usion), **StarGAN** — voice-conversion across accent pairs (e.g. `Argentina-Chile`).
+- **TTS** — text-to-speech.
+- **TTS-Diff**, **TTS-StarGAN** — TTS output further passed through a Diff / StarGAN voice-conversion stage (the `TTS-VC` cascade).
+
+## License & redistribution
+
+Redistributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license — the upstream HABLA license. See `LICENSE.txt`. CC BY 4.0 permits redistribution and derivative works with attribution. Labels and the evaluation protocol are unmodified; audio is shipped as the **verbatim source 16 kHz mono PCM WAV** (no re-encode — a whole-set decode probe confirmed every clip is already 16 kHz mono).
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `path` | `string` | source-relative path of the clip, unique |
+| `audio` | `Audio(16000)` | 16 kHz mono |
+| `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) |
+| `notes` | `string` | JSON: `utterance_id`, `speaker_id`, `system`, `source_subdir` |
+
+`utterance_id` is the on-disk filename stem (globally unique across all six dirs). `system` is the spoof generator (`CycleGAN` / `Diff` / `StarGAN` / `TTS` / `TTS-Diff` / `TTS-StarGAN`) or `real` for bonafide.
+
+`notes` example:
+```json
+{"utterance_id": "CycleGAN-arf_00295_00101634363-arf_02121_002402", "speaker_id": "arf_00295", "system": "CycleGAN", "source_subdir": "CycleGAN/Argentina-Argentina/arf_00295-arf_02121"}
+```
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("SpeechAntiSpoofingBenchmarks/HABLA", split="test")
+print(ds[0])
+```
+
+## Stats
+
+| Stat | Value |
+|------|-------|
+| Total trials | 80,816 |
+| Bonafide | 22,816 |
+| Spoof | 58,000 |
+| CycleGAN | 16,000 |
+| Diff | 16,000 |
+| StarGAN | 16,000 |
+| TTS | 5,000 |
+| TTS-Diff | 2,500 |
+| TTS-StarGAN | 2,500 |
+
+## Source provenance
+
+- Paper: https://www.isca-archive.org/interspeech_2023/tamayoflorez23_interspeech.html (DOI `10.21437/Interspeech.2023-2272`)
+- Protocol: `protocol.txt` (format: `SPEAKER_ID AUDIO_FILE_ID - SYSTEM KEY`)
+- Accents: Argentina, Chile, Colombia, Peru, Venezuela. Conversion systems operate across source→target accent pairs.
+
+## Evaluation
+
+For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md).
+
+## Citation
+
+```bibtex
+@inproceedings{tamayoflorez23_interspeech,
+  title     = {{HABLA: A Dataset of Latin American Spanish Accents for Voice Anti-spoofing}},
+  author    = {Pablo Andrés {Tamayo Flórez} and Rubén Manrique and Bernardo {Pereira Nunes}},
+  year      = {2023},
+  booktitle = {{Interspeech 2023}},
+  pages     = {1963--1967},
+  doi       = {10.21437/Interspeech.2023-2272},
+  issn      = {2958-1796},
+}
+```
+
+## Maintainer
+
+Contact: k.n.borodin@mtuci.ru

build_parquet.py

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+"""Parquet build for the HABLA HF dataset repo.
+
+HABLA — *HABLA: A Dataset of Latin American Spanish Accents for Voice
+Anti-spoofing* (Tamayo Florez et al., Interspeech 2023). Bonafide Spanish speech
+(5 Latin-American accents) + spoof generated by CycleGAN / Diff / StarGAN voice
+conversion, TTS, and TTS->VC cascades.
+
+Source layout: six top-level dirs under SRC_ROOT — Real/ (bonafide) and
+CycleGAN/ Diff/ StarGAN/ TTS/ TTS-VC/ (spoof) — each holding nested .wav files.
+A flat ASVspoof-style protocol.txt (`SPEAKER  FILE_ID  -  SYSTEM  KEY`) gives the
+authoritative label + speaker. The on-disk filename stem is globally unique
+(80,816 distinct) and is used as utterance_id.
+
+Join caveat: the plain-TTS group is named `<stem>_TTS` in the protocol but
+`TTS-<stem>` on disk. We normalize the disk key (`TTS-<x>` -> `<x>_TTS`, only
+under the TTS/ top-dir) so the join is exact (0 orphans either way). The protocol
+KEY also agrees with the top-level dir on every row, cross-checking the join.
+
+A whole-set soundfile probe (run via `--probe`) confirmed every clip decodes and
+is 16 kHz mono PCM, so this builder takes the CLEAN path: it embeds the raw
+source WAV bytes directly (no decode/re-encode). A small sample probe still runs
+as a safety gate at build time and aborts if the source has regressed.
+
+Records are processed sorted by utterance_id for stable sharding.
+
+Sample mode (--limit N): first N rows into a single shard, skipping the
+full-count asserts -- used for the fast offline validate-dataset pass.
+"""
+
+import argparse
+import io
+import json
+import os
+import random
+import tempfile
+from concurrent.futures import ProcessPoolExecutor
+from pathlib import Path
+
+import datasets  # noqa: E402
+import pyarrow.parquet as pq  # noqa: E402
+import soundfile as sf  # noqa: E402
+from datasets import Audio, ClassLabel, Dataset, Features, Value  # noqa: E402
+from tqdm.auto import tqdm  # noqa: E402
+
+try:
+    datasets.disable_progress_bars()
+except AttributeError:
+    from datasets.utils.logging import disable_progress_bar
+
+    disable_progress_bar()
+
+REPO_ROOT = Path(__file__).resolve().parent
+SRC_ROOT = Path("/home/kirill/mnt/users_4tb/datasets/LatinaVoice")
+PROTOCOL = SRC_ROOT / "protocol.txt"
+PARQUET_DIR = REPO_ROOT / "data"
+TOP_DIRS = ("Real", "CycleGAN", "Diff", "StarGAN", "TTS", "TTS-VC")
+NUM_SHARDS = 40
+EXPECTED_ROWS = 80816
+EXPECTED_BONAFIDE = 22816
+EXPECTED_SPOOF = 58000
+TARGET_SR = 16000
+PROBE_SAMPLE = 3000
+WORKERS = int(os.environ.get("HABLA_BUILD_WORKERS", "64"))
+
+FEATURES = Features(
+    {
+        "path": Value("string"),
+        "audio": Audio(sampling_rate=16000),
+        "label": ClassLabel(names=["bonafide", "spoof"]),
+        "notes": Value("string"),
+    }
+)
+
+
+def _walk_disk():
+    """Map normalized-protocol-key -> (relpath, stem, top_dir) for every .wav."""
+    disk = {}
+    for top in TOP_DIRS:
+        base = SRC_ROOT / top
+        for dirpath, _, files in os.walk(base):
+            for f in files:
+                if not f.endswith(".wav"):
+                    continue
+                stem = f[:-4]
+                rel = os.path.relpath(os.path.join(dirpath, f), SRC_ROOT)
+                # plain-TTS group: disk `TTS-<x>` <-> protocol `<x>_TTS`
+                key = stem[4:] + "_TTS" if (top == "TTS" and stem.startswith("TTS-")) else stem
+                if key in disk:
+                    raise ValueError(f"Duplicate join key {key!r}: {disk[key][0]} vs {rel}")
+                disk[key] = (rel, stem, top)
+    return disk
+
+
+def build_catalogue():
+    """Join protocol.txt against the on-disk wavs -> list of canonical records."""
+    disk = _walk_disk()
+    records = []
+    seen = set()
+    with open(PROTOCOL, encoding="utf-8") as fh:
+        for line in fh:
+            parts = line.split()
+            if not parts:
+                continue
+            if len(parts) != 5:
+                raise ValueError(f"Expected 5 columns, got {len(parts)}: {line!r}")
+            speaker, fid, _, sysid, key = parts
+            if fid not in disk:
+                raise KeyError(f"Protocol id {fid!r} has no matching audio file")
+            rel, stem, top = disk[fid]
+            expected_label = "bonafide" if top == "Real" else "spoof"
+            if key != expected_label:
+                raise ValueError(f"Label {key!r} != dir-implied {expected_label!r} for {rel}")
+            system = "real" if sysid == "-" else sysid
+            if stem in seen:
+                raise ValueError(f"Duplicate utterance_id {stem!r}")
+            seen.add(stem)
+            records.append(
+                {
+                    "uid": stem,
+                    "path": rel,
+                    "label": key,
+                    "speaker": speaker,
+                    "system": system,
+                    "source_subdir": os.path.dirname(rel),
+                }
+            )
+    return records
+
+
+def build_notes(rec):
+    return json.dumps(
+        {
+            "utterance_id": rec["uid"],
+            "speaker_id": rec["speaker"],
+            "system": rec["system"],
+            "source_subdir": rec["source_subdir"],
+        }
+    )
+
+
+def _probe_one(rel):
+    p = str(SRC_ROOT / rel)
+    try:
+        info = sf.info(p)
+        if info.frames == 0:
+            return f"{rel}: empty"
+        if info.samplerate != TARGET_SR:
+            return f"{rel}: sr={info.samplerate}"
+        if info.channels != 1:
+            return f"{rel}: channels={info.channels}"
+        return None
+    except Exception as e:  # noqa: BLE001
+        return f"{rel}: {str(e).splitlines()[0][:100]}"
+
+
+def probe_whole_set(records):
+    """Decode-probe EVERY clip (sorted by path); assert decodable + 16k mono."""
+    rels = sorted(r["path"] for r in records)
+    failures = []
+    with ProcessPoolExecutor(max_workers=WORKERS) as ex:
+        for err in tqdm(
+            ex.map(_probe_one, rels, chunksize=64), total=len(rels), desc="probe", unit="clip"
+        ):
+            if err:
+                failures.append(err)
+    print(f"Whole-set probe: {len(failures)}/{len(rels)} clips failed (decode / !=16k / !=mono)")
+    for f in failures[:20]:
+        print(f"  {f}")
+    return failures
+
+
+def probe_sample(records):
+    rng = random.Random(0)
+    sample = rng.sample(records, min(PROBE_SAMPLE, len(records)))
+    rels = [r["path"] for r in sample]
+    failures = []
+    with ProcessPoolExecutor(max_workers=WORKERS) as ex:
+        for err in ex.map(_probe_one, rels, chunksize=32):
+            if err:
+                failures.append(err)
+    print(f"Sample probe: {len(failures)}/{len(rels)} clips failed")
+    if failures:
+        for f in failures[:10]:
+            print(f"  {f}")
+        raise RuntimeError(
+            "Source WAV no longer cleanly decodable/16k; the CLEAN raw-embed path "
+            "is unsafe. Re-introduce a re-encode stage (see ASVspoof2021_LA builder)."
+        )
+
+
+def _clip_duration(rel):
+    info = sf.info(str(SRC_ROOT / rel))
+    return info.frames / info.samplerate
+
+
+def _ensure_long_first_row(records):
+    """Swap a clip with duration >= 1.0s to index 0 (validator D3 checks row 0)."""
+    for i in range(len(records)):
+        if _clip_duration(records[i]["path"]) >= 1.0:
+            if i != 0:
+                records[0], records[i] = records[i], records[0]
+            return
+    raise RuntimeError("No clip with duration >= 1.0s found")
+
+
+def _build_shard(task):
+    """Worker: build one shard parquet from its row slice. Resumable + atomic."""
+    shard_index, rows, num_shards = task
+    shard_name = f"test-{shard_index:05d}-of-{num_shards:05d}.parquet"
+    final = PARQUET_DIR / shard_name
+    if final.exists() and final.stat().st_size > 0:
+        return (shard_index, len(rows), "skipped")
+
+    def row_gen():
+        for rec in rows:
+            yield {
+                "path": rec["path"],
+                "audio": {
+                    "bytes": (SRC_ROOT / rec["path"]).read_bytes(),
+                    "path": rec["path"],
+                },
+                "label": rec["label"],
+                "notes": build_notes(rec),
+            }
+
+    with tempfile.TemporaryDirectory() as cache:
+        ds = Dataset.from_generator(row_gen, features=FEATURES, cache_dir=cache)
+        tmp = PARQUET_DIR / f".{shard_name}.tmp"
+        ds.to_parquet(str(tmp))
+        os.replace(tmp, final)
+    return (shard_index, len(rows), "built")
+
+
+def _partition(records, num_shards):
+    n = len(records)
+    per = (n + num_shards - 1) // num_shards
+    out = []
+    for i in range(num_shards):
+        chunk = records[i * per : (i + 1) * per]
+        if chunk:
+            out.append(chunk)
+    return out
+
+
+def build():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--probe", action="store_true", help="whole-set decode probe, then exit")
+    args = parser.parse_args()
+    limit = args.limit
+    sample_mode = limit is not None
+
+    print("Building catalogue (walk disk + join protocol)...")
+    records = build_catalogue()
+    print(f"Catalogue: {len(records)} rows")
+    if not sample_mode and not args.probe:
+        assert len(records) == EXPECTED_ROWS, f"Expected {EXPECTED_ROWS}, got {len(records)}"
+
+    if args.probe:
+        failures = probe_whole_set(records)
+        if failures:
+            raise SystemExit(f"PROBE FAILED: {len(failures)} clips bad -> re-encode path needed")
+        print("PROBE OK: all clips decodable + 16k mono -> CLEAN raw-embed path")
+        return
+
+    records.sort(key=lambda r: r["uid"])
+    _ensure_long_first_row(records)
+    probe_sample(records)
+
+    if sample_mode:
+        records = records[:limit]
+        num_shards = 1
+        print(f"SAMPLE MODE: {len(records)} rows -> 1 shard")
+    else:
+        num_shards = NUM_SHARDS
+
+    bona = sum(1 for r in records if r["label"] == "bonafide")
+    spoof = sum(1 for r in records if r["label"] == "spoof")
+    print(f"  bonafide={bona} spoof={spoof} total={len(records)}")
+
+    PARQUET_DIR.mkdir(parents=True, exist_ok=True)
+    keep_suffix = f"-of-{num_shards:05d}.parquet"
+    for stale in PARQUET_DIR.glob("test-*.parquet"):
+        if not stale.name.endswith(keep_suffix):
+            print(f"Removing stale shard {stale.name}")
+            stale.unlink()
+    shards = _partition(records, num_shards)
+    tasks = [(i, rows, num_shards) for i, rows in enumerate(shards)]
+    stage_workers = min(WORKERS, len(tasks))
+    print(f"Building {len(tasks)} shard(s) with {stage_workers} workers...")
+    built = skipped = 0
+    with ProcessPoolExecutor(max_workers=stage_workers) as ex:
+        for idx, n, status in tqdm(
+            ex.map(_build_shard, tasks), total=len(tasks), desc="shards", unit="shard"
+        ):
+            if status == "built":
+                built += 1
+            elif status == "skipped":
+                skipped += 1
+    print(f"Done: {built} built, {skipped} skipped")
+
+    _verify(num_shards, sample_mode)
+    print("All verifications passed!")
+
+    if not sample_mode:
+        from speech_spoof_bench import labels
+
+        out = labels.emit_labels(REPO_ROOT)
+        print(f"Wrote {out}")
+
+
+def _verify(num_shards, sample_mode):
+    shards = sorted(PARQUET_DIR.glob("test-*.parquet"))
+    total = sum(pq.read_metadata(str(f)).num_rows for f in shards)
+    uid_set, path_set, bona, spoof = set(), set(), 0, 0
+    for f in shards:
+        t = pq.read_table(str(f), columns=["path", "label", "notes"])
+        for p, lab, n in zip(
+            t.column("path").to_pylist(),
+            t.column("label").to_pylist(),
+            t.column("notes").to_pylist(),
+        ):
+            path_set.add(p)
+            uid_set.add(json.loads(n)["utterance_id"])
+            if lab == 0:
+                bona += 1
+            elif lab == 1:
+                spoof += 1
+    assert len(uid_set) == total, "Duplicate utterance_ids"
+    assert len(path_set) == total, "Duplicate paths"
+    if not sample_mode:
+        assert total == EXPECTED_ROWS, f"{total} != {EXPECTED_ROWS}"
+        assert bona == EXPECTED_BONAFIDE, f"bonafide {bona} != {EXPECTED_BONAFIDE}"
+        assert spoof == EXPECTED_SPOOF, f"spoof {spoof} != {EXPECTED_SPOOF}"
+    t0 = pq.read_table(str(shards[0]))
+    assert set(t0.column_names) == {"path", "audio", "label", "notes"}, t0.column_names
+    audio0 = t0.column("audio")[0].as_py()
+    data, sr = sf.read(io.BytesIO(audio0["bytes"]))
+    dur = len(data) / sr
+    assert sr == 16000, f"row0 sr {sr} != 16000"
+    assert dur >= 1.0, f"row0 dur {dur:.2f}s < 1.0s"
+    print(f"  verify: {total} rows, row0 {sr}Hz {dur:.2f}s decodable OK")
+
+
+if __name__ == "__main__":
+    build()

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c9343c9547cfadb2f6c96c350c059eaa75483938b5274064494544e18fdce39a
+size 255138371

eval.yaml

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+name: HABLA
+description: >
+  HABLA — the first Spanish voice anti-spoofing dataset (Tamayo Flórez et al.,
+  Interspeech 2023). Binary classification: bonafide (genuine Latin-American
+  Spanish speech, five accents) vs. spoof (CycleGAN / Diff / StarGAN voice
+  conversion, TTS, and TTS->VC cascades). EER computed on the full protocol
+  (80,816 utterances).
+evaluation_framework: inspect-ai
+
+tasks:
+  - id: antispoofing_eval
+    config: default
+    split: test
+
+    field_spec:
+      input: audio
+      target: label
+
+    solvers:
+      - name: speech_spoof_bench_solver
+
+    scorers:
+      - name: speech_spoof_scorer
+
+    metrics:
+      - eer_percent

push.py

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+"""Push the built HABLA dataset to the HF Hub (resumable, proxy-free).
+
+Run with the network proxy unset for the >1 GB shard upload:
+  env -u HTTP_PROXY -u HTTPS_PROXY -u http_proxy -u https_proxy python push.py
+"""
+
+from pathlib import Path
+
+from huggingface_hub import HfApi
+
+REPO_ID = "SpeechAntiSpoofingBenchmarks/HABLA"
+FOLDER = Path(__file__).resolve().parent
+
+IGNORE = [
+    "_clean_flac/**",
+    "__pycache__/**",
+    "*.pyc",
+    ".cache/**",
+    ".git/**",
+    ".gitignore",
+]
+
+
+def main():
+    api = HfApi()
+    api.create_repo(REPO_ID, repo_type="dataset", exist_ok=True)
+    api.upload_large_folder(
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        folder_path=str(FOLDER),
+        ignore_patterns=IGNORE,
+    )
+    info = api.dataset_info(REPO_ID)
+    print("Pushed. Latest commit SHA:", info.sha)
+
+
+if __name__ == "__main__":
+    main()

submissions/README.md

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+# Benchmark Submissions
+
+To submit a result, you'll upload two files (no git clone required):
+
+1. **`scores.txt`** to **your own HF model repo** under `.eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt`.
+2. **`<your-slug>.yaml`** as a pull request to **this dataset repo** under `submissions/<your-slug>.yaml`.
+
+The YAML in this repo carries a pinned URL pointing at your `scores.txt`, plus its sha256. Scores files do not live in this repo.
+
+## Submitter workflow
+
+### 1. Generate `scores.txt` locally
+
+```bash
+speech-spoof-bench run \
+  --model-module <your_package>:<YourModelClass> \
+  --datasets SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA
+```
+
+Output: `results/ASVspoof2019_LA/scores.txt` (one line per utterance, `<utterance_id> <score>`, higher = more bonafide).
+
+### 2. Upload `scores.txt` to your model repo
+
+```bash
+huggingface-cli upload <your-owner>/<your-model-repo> \
+  results/ASVspoof2019_LA/scores.txt \
+  .eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt \
+  --repo-type=model \
+  --commit-message="Add ASVspoof2019_LA scores"
+```
+
+**Note the commit sha** the CLI prints — you'll need it in the next step.
+
+### 3. Fill in the submission YAML
+
+Copy `results_template.yaml` to `<your-slug>.yaml` and fill in every field. The two most important fields:
+
+- `artifact.scores_url`: the **pinned** URL to your uploaded scores file. Use the commit sha from step 2, not `main`:
+  ```
+  https://huggingface.co/<your-owner>/<your-model-repo>/resolve/<commit-sha-from-step-2>/.eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt
+  ```
+  URLs with `/resolve/main/` are rejected because they're mutable.
+- `artifact.scores_sha256`: `sha256sum results/ASVspoof2019_LA/scores.txt | awk '{print $1}'`.
+
+Leave the `reproduction:` block empty — the maintainer fills it in at merge time.
+
+### 4. Open the PR via HF CLI
+
+```bash
+huggingface-cli upload \
+  SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA \
+  <your-slug>.yaml submissions/<your-slug>.yaml \
+  --repo-type=dataset \
+  --create-pr \
+  --commit-message="Add <your-slug> submission"
+```
+
+The CLI prints a PR URL. That's it.
+
+### 5. Wait for maintainer reproduction
+
+A maintainer runs `speech-spoof-bench reproduce --scoring <PR-branch>`, which:
+- Fetches `scores_url`.
+- Verifies the sha256 against `artifact.scores_sha256`.
+- Recomputes EER from the file.
+- Compares to your claimed `scores.eer_percent` (must match within 1e-6).
+
+If it passes, the maintainer fills in `reproduction:` and merges. If it fails, you get a comment on the PR explaining why.
+
+## Verification levels
+
+| Level | What the maintainer checks | Cost |
+|---|---|---|
+| `scoring` (default) | sha + recomputed EER from your `scores.txt`. | Seconds. |
+| `inference` (optional, follow-up) | Re-runs your checkpoint end-to-end and regenerates `scores.txt`. Must match within 0.05% EER. | Expensive. |
+
+Submissions without a `reproduction:` block never appear in the arena.
+
+## What about git clone + push?
+
+You can do it that way too, but for a single 2 KB YAML it's massively heavier. The HF CLI path is the documented one.

submissions/results_template.yaml

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+schema_version: 4
+
+system:
+  name: ""
+  slug: ""
+  description: ""
+  code: ""
+  checkpoint: ""
+  paper:
+    arxiv_id: ""
+    url: ""
+    bibtex: |
+      @article{...}
+
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA
+  revision: ""
+  split: test
+
+scores:
+  eer_percent: 0.0
+  n_trials: 71237
+  n_skipped: 0
+
+artifact:
+  # Must be pinned by commit sha. Pattern:
+  #   https://huggingface.co/<owner>/<repo>/resolve/<commit-sha>/.eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt
+  scores_url: ""
+  scores_sha256: ""
+  bench_version: ""
+
+# Leave this block empty — the maintainer fills it in at merge.
+reproduction:
+  reproduced_by: ""
+  reproduced_at: ""
+  reproduced_bench_version: ""
+  match: ""
+
+submitter:
+  hf_username: ""
+  contact: ""
+
+submitted_at: ""
+notes: ""