Luciole Audio LLM
Collection
5 items • Updated
Datasets:
id stringlengths 15 15 | conversations listlengths 3 3 | language stringclasses 1
value | extra stringlengths 59 572 |
|---|---|---|---|
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},
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"value": "audio/music/MusicCaps/0OYlHvyfNk4.wav",
"type": "audio",
"duration": 10.008,
"offset": null
},
{
"from": "Assistant",
... | en | {"custom_metadata": {"aspect_list": "['ska', 'acoustic guitars', 'shaker', 'e-bass', 'acoustic drums', 'male voice singing/rapping', 'male backing voices', 'uptempo']"}} |
0Olm321vgk8.wav | [
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{
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},
{
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{
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"value": "audio/music/MusicCaps/0PMFAO4TIU4.wav",
"type": "audio",
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},
{
"from": "Assistant",
"v... | en | {"custom_metadata": {"aspect_list": "['new ethiopian music', 'digital drums', 'male voice sample', 'accordion-like sound', 'e-bass', 'medium tempo', 'uplifting energy']"}} |
0RcMzUdXDRQ.wav | [
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{
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"type": "audio",
"duration": 10.008,
"offset": null
},
{
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0TV9zvfwFhs.wav | [
{
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},
{
"from": "User",
"value": "audio/music/MusicCaps/0TV9zvfwFhs.wav",
... | en | {"custom_metadata": {"aspect_list": "['male voice', 'male backing vocals', 'r&b genre', 'electric piano', 'drums', 'bass guitar', 'tubular bell']"}} |
0TiEO149Ydc.wav | [
{
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},
{
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"type": "audio",
"duration": 10,
"offset": null
},
{
"from": "Assistant",
... | en | {"custom_metadata": {"aspect_list": "['low quality', 'noisy', 'alternative/indie', 'wooden percussive element', 'acoustic guitar strummed chord', 'passionate female vocal', 'emotional', 'passionate', 'live performance', 'unbalanced stereo image']"}} |
0Ubu4BqSWmU.wav | [
{
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"type": "text",
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{
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},
{
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0VjPCd62oKg.wav | [
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{
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"type": "audio",
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},
{
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0XxinBtKouQ.wav | [
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{
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{
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{
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"type": "audio",
"duration": 10,
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},
{
"from": "Assistan... | en | {"custom_metadata": {"aspect_list": "['rock/pop', 'e-guitars', 'e-bass', 'acoustic drums', 'male voices singing', 'higher register', 'uptempo']"}} |
0a91szM1Ivw.wav | [
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"duration": null,
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{
"from": "User",
"value": "audio/music/MusicCaps/0a91szM1Ivw.wav",
"type": "audio",
"duration": 10,
"offset": null
},
{
"from": "Assista... | en | {"custom_metadata": {"aspect_list": "['low quality', 'distorted passionate female vocal', 'noisy', 'live performance', 'traditional', 'emotional', 'subtle sitar melody', 'folk']"}} |
0abzD7hBTRk.wav | [
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{
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"type": "audio",
"duration": 10,
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},
{
"from": "Assistant",
"value": "... | en | {"custom_metadata": {"aspect_list": "['vocalisation', 'medium fast tempo', 'male vocalist', 'punchy', 'boomy', 'buoyant', 'rap', 'dance groove', 'vocal percussionist', 'youthful', 'rhythmic', 'skilled', 'engaging', 'beat boxing', 'turn table imitation', 'vigorous', 'youthful', 'vocal percussion', 'vocal turntablism', '... |
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},
{
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{
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{
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End of preview. Expand in Data Studio
Luciole Audio Evaluation Dataset
Dataset description
Luciole Audio Evaluation Dataset is the held-out evaluation collection used to benchmark the OpenLLM-France Luciole audio-language models. It is the evaluation counterpart of the Luciole Audio Training Dataset: a multilingual, multi-task set of audio–text conversations, restricted to the test splits of each source dataset, covering transcription, speech translation, spoken and music question answering, music captioning, speaker attributes (gender, age, emotion), spoken language identification, temporal localization, format following, and speaker diarization.
Each record is one evaluation example: the user turn(s) hold the instruction and the audio, and the assistant turn holds the reference (gold) answer to score against.
The repository holds only the text and metadata (the conversations and their references to audio files). The audio itself is not hosted on the Hub — see Getting the audio.
⚠️ Licensing / redistribution. This dataset aggregates many source datasets under different licenses. See Datasets and licenses: every split maps to exactly one source dataset, with its license and where its audio can be obtained. Datasets whose license forbids redistribution (e.g. NoDerivatives, or research-only agreements) are not included here and must be obtained from their original source.
Dataset structure
Luciole-Audio-Evaluation-Dataset/
├── README.md
├── dataset_stats.csv # per-split #examples / #audio / hours / license / audio_hosted
├── dataset_index.json # split → source dataset, license, provenance
└── data/ # the conversations (this is what lives on the Hub)
├── speech/ (asr, ast, qa, summarization, diarization, temporal, format_following,
│ emotion_reco, gender_reco, age_reco, language_reco)
└── music/ (qa, captioning)
The two top levels are <domain>/<task> (e.g. speech/asr, music/qa). Each
<domain>/<task> is exposed as the configuration <domain>.<task>, and each
source dataset (per language) is a split within it. For example, French ASR from
FLEURS is the FLEURS_fr split of the speech.asr config.
Data fields
Every record, in every split, has exactly these four fields:
| field | type | description |
|---|---|---|
id |
string | unique example id. |
conversations |
list | the dialogue turns (see below). |
language |
string | "fr", "en", … ; "multilingual" for language-identification; for translation, source-target like "fr-en". |
extra |
string | a JSON-serialized object with any extra source metadata; "{}" when there is none. |
Each turn in conversations has:
| field | type | description |
|---|---|---|
from |
string | "User" or "Assistant" |
type |
string | "text" or "audio" |
value |
string | the text (for the assistant turn, the reference answer), or a relative path to an audio file (for type: "audio") |
duration |
float | audio length in seconds (audio turns only; null otherwise) |
offset |
float | start offset in the audio file, in seconds (when the turn uses a segment; null otherwise) |
The reference answer to score against is the Assistant turn's value.
Audio is referenced by a relative path of the form audio/<domain>/<Dataset>/<file>,
task-independent, with the audio's own language in <Dataset> for speech
(FLEURS_fr, …) but not for music.
Example record (FLEURS fr ASR):
{
"id": "1829",
"conversations": [
{"from": "User", "value": "Écris le texte de cet enregistrement.", "type": "text"},
{"from": "User", "value": "audio/speech/FLEURS_fr/7105431834829365765.wav", "type": "audio", "duration": 9.42},
{"from": "Assistant", "value": "Les voyageurs à destination de pays où les taxes sont élevées …", "type": "text"}
],
"language": "fr",
"extra": "{\"dataset_name\": \"fleurs\"}"
}
Tasks
The evaluation covers the following tasks, organised by domain as <domain>/<task> (each
exposed as the configuration <domain>.<task>). Most instruction prompts were generated
by OpenLLM-France; the marks in the Datasets and licenses table
flag, per task line, where OpenLLM-France produced the audio (🎙️) or the text (✍️).
Speech
asr— Automatic Speech Recognition (FLEURS, Common Voice, VoxPopuli, SUMM-RE).ast— Automatic Speech Translation (source-target pairs, e.g.fr-en).qa— Spoken Question Answering (VoxPopuli-QA, Aya, Vigogne, National Speech Corpus, SpokenWOZ).summarization— Dialogue Summarization (National Speech Corpus SDS).temporal— Temporal Localization: word↔time queries (SLUE-SQA-5).format_following— Format Following: produce the answer in a required (e.g. JSON) format (SLUE-SQA-5).language_reco— Spoken Language Identification (CoVoST 2).emotion_reco— Emotion Recognition (MELD).gender_reco— Speaker Gender Recognition (Common Voice).age_reco— Speaker Age Recognition (Common Voice).diarization— Speaker Diarization ("who spoke when", with timestamps; AMI).
Music
qa— Music Question Answering (MusicCaps, MTG-Jamendo).captioning— Music Captioning (MusicCaps).
Loading
Because each <domain>/<task> is a separate config, load a config and pick a split:
from datasets import load_dataset
asr = load_dataset("OpenLLM-France/Luciole-Audio-Evaluation-Dataset", "speech.asr")
fleurs_fr = load_dataset("OpenLLM-France/Luciole-Audio-Evaluation-Dataset",
"speech.asr", split="FLEURS_fr")
See dataset_stats.csv for the size of every split and
dataset_index.json for the exact source dataset/license behind each.
Getting the audio
The audio is not part of this Hub repository. Most of it is served separately at:
https://dl.labs.linagora.com/files/datasets/OpenLLM-France/Luciole-Audio-Evaluation-Dataset/audio/
Download that audio/ folder and place it next to data/; its layout mirrors the audio
paths in the records (audio/<domain>/<Dataset>/…).
Some datasets' audio is not redistributable by us (copyrighted source, YouTube-only, NoDerivatives, or research-only agreements). For those, the conversations are still published (when permitted) but you must download the audio from the original source listed below. See Audio availability.
Datasets and licenses
This dataset is an aggregation of third-party datasets, each under its own license. The table below lists every source dataset used, its license, what is published here, where to get the audio, and its content (examples and audio duration, per task and language).
The instruction prompts (the user-turn wording that states each task) were written by OpenLLM-France for every task except question answering (qa).
Marks in the Content column flag data produced by OpenLLM-France, on the specific task line(s) concerned: 🎙️ = audio synthesized by OpenLLM-France · ✍️ = text (questions/answers, translations, or captions) generated or modified by OpenLLM-France — see each dataset's note below for details.
| Dataset | License | In this release | Audio | Content |
|---|---|---|---|---|
| AMI Meeting Corpus | CC-BY-4.0 | ✓ text + audio | hosted | 1,326 samples · 16h — ✍️ diarization, en |
| aya_collection | Apache-2.0 (text) Synthesized by OpenLLM-France (audio) |
✓ text + audio | hosted | 31 samples · 2min — 🎙️ qa, fr |
| Common Voice | CC0-1.0 | ✓ text + audio | hosted | 40,209 samples · 66h • asr, en — 16,401 · 27h • asr, fr — 16,186 · 26h • ✍️ gender_reco, en — 2,290 · 3h42 • ✍️ age_reco, en — 2,077 · 3h21 • ✍️ gender_reco, fr — 1,758 · 2h48 • ✍️ age_reco, fr — 1,497 · 2h23 |
| CoVoST2 (via AIR-Bench) | CC0-1.0 | ✓ text + audio | hosted | 1,000 samples · 1h35 — language_reco, multilingual |
| Fisher (via AIR-Bench) | LDC User Agreement (text) Copyrighted / LDC-restricted (audio) |
✗ excluded (external only) | source | qa, en |
| FLEURS | CC-BY-4.0 | ✓ text + audio | hosted | 5,654 samples · 19h • asr, pt — 913 · 3h11 • asr, es — 908 · 3h05 • asr, it — 862 · 3h30 • asr, de — 858 · 3h08 • asr, fr — 676 · 1h57 • asr, en — 645 · 1h46 • asr, ar — 428 · 1h18 • asr, nl — 364 · 58min |
| MELD | GPL-3.0 (text) Copyrighted (audio) |
✓ text only | source | 5,220 samples · 4h55 • emotion_reco, en — 2,610 · 2h27 • emotion_reco, fr — 2,610 · 2h27 |
| MTJ-Jamendo (via AIR-Bench) | CC-BY-NC-SA-4.0 (text) Mixed per-track CC (audio) |
✓ text only | source | 3,000 samples · 187h — music.qa, en |
| Multilingual TEDx | CC-BY-NC-ND-4.0 | ✗ excluded (external only) | source | 0 samples • asr, ar • asr, de • asr, es • asr, fr • asr, it • asr, pt • ast, es-fr • ast, es-it • ast, fr-en • ast, fr-es |
| MusicCaps | CC-BY-SA-4.0 (text) Copyrighted (audio) |
✓ text only | source | 8,316 samples · 23h • music.captioning, en — 2,772 · 7h42 • ✍️ music.qa, en — 2,772 · 7h42 • ✍️ music.qa, fr — 2,772 · 7h42 |
| National Speech Corpus | Singapore Open Data Licence v1.0 | ✓ text + audio | hosted | 700 samples · 5h24 • qa, en — 400 · 3h05 • summarization, en — 300 · 2h19 |
| SLUE-SQA-5 (SLUE phase-2) | Apache-2.0 / CC-BY-SA-4.0 | ✓ text + audio | hosted | 1,162 samples · 13h • ✍️ temporal, en — 797 · 8h49 • ✍️ format_following, en — 365 · 4h02 |
| SpokenWOZ (via AIR-Bench) | CC-BY-NC-4.0 | ✓ text + audio | hosted | 193 samples · 1h25 — qa, en |
| SUMM-RE | CC-BY-SA-4.0 | ✓ text + audio | hosted | 17,121 samples · 12h — asr, fr |
| Vigogne (French Alpaca) | CC-BY-NC-4.0 (text) Synthesized by OpenLLM-France (audio) |
✓ text + audio | hosted | 502 samples · 47min — 🎙️ qa, fr |
| VoxPopuli | CC0-1.0 | ✓ text + audio | hosted | 3,584 samples · 9h49 • asr, en — 1,842 · 4h55 • asr, fr — 1,742 · 4h53 |
| VoxPopuli-QA | CC0-1.0 (text) Synthesized by OpenLLM-France (audio) |
✓ text + audio | hosted | 570 samples · 4h34 — 🎙️ qa, fr |
| YouTubeFr | Copyrighted | ✗ excluded (external only) | source | asr, fr |
Audio availability
Where to obtain the audio for each source dataset, grouped by what is published in this repository.
Text and audio
Both the conversations and the audio are published. The audio is hosted at https://dl.labs.linagora.com/files/datasets/OpenLLM-France/Luciole-Audio-Evaluation-Dataset/audio/; each record's audio path is relative to that folder.
- AMI Meeting Corpus — CC-BY-4.0 — hosted
AMI meeting recordings (CC-BY-4.0). Speaker-diarization evaluation: OpenLLM-France generated the prompts and assembled the reference "who spoke when" timelines.
Source: AMI Meeting Corpus (University of Edinburgh) — CC-BY-4.0 — https://groups.inf.ed.ac.uk/ami/corpus/ - aya_collection — Apache-2.0 (text), Synthesized by OpenLLM-France (Chatterbox TTS) (audio) — hosted
French QA from the Aya Collection (Apache-2.0). The spoken questions were synthesized by OpenLLM-France (Chatterbox TTS).
Source: [text] Aya Collection (Cohere Labs) — Apache-2.0 — https://huggingface.co/datasets/CohereLabs/aya_collection - Common Voice — CC0-1.0 — hosted
Mozilla Common Voice (CC0-1.0). Used for ASR (generated prompts) and for speaker gender/age recognition, where OpenLLM-France generated the prompts and assembled the answer phrasings from Common Voice metadata.
Source: Mozilla Common Voice — CC0-1.0 — https://commonvoice.mozilla.org/en/datasets - CoVoST2 (via AIR-Bench) — CC0-1.0 — hosted
CoVoST 2 (audio derived from Common Voice, CC0), used for spoken language identification via AIR-Bench.
Source: CoVoST 2 (audio from Common Voice, CC0) — CC0-1.0 — https://github.com/facebookresearch/covost - FLEURS — CC-BY-4.0 — hosted
Multilingual read speech (FLoRes-based). Evaluation prompts generated by OpenLLM-France; the reference transcripts are the original FLEURS data (CC-BY-4.0).
Source: FLEURS (Google) — CC-BY-4.0 — https://huggingface.co/datasets/google/fleurs - National Speech Corpus — Singapore Open Data Licence v1.0 — hosted
Singapore IMDA National Speech Corpus, used for spoken QA (SQA) and dialogue summarization (SDS) via the AudioLLMs repackaging. Distributed under the Singapore Open Data Licence v1.0 (redistribution permitted with attribution). [Confidence: medium — IMDA page unreachable at audit time; AudioLLMs repo is access-gated.]
Sources:- IMDA National Speech Corpus (Singapore) — Singapore Open Data Licence v1.0 — https://www.imda.gov.sg/how-we-can-help/national-speech-corpus
- AudioLLMs Multitask National Speech Corpus (repackaging) — Singapore Open Data Licence v1.0 — https://huggingface.co/datasets/AudioLLMs
- SLUE-SQA-5 (SLUE phase-2) — Apache-2.0 / CC-BY-SA-4.0 — hosted
SLUE-SQA-5, used for temporal localization (time2word/word2time) and JSON-format following. OpenLLM-France generated the prompts and assembled the reference answers.
Source: SLUE-SQA-5 (SLUE phase-2) — Apache-2.0 / CC-BY-SA-4.0 — https://huggingface.co/datasets/asapp/slue-phase-2 - SpokenWOZ (via AIR-Bench) — CC-BY-NC-4.0 — hosted
Task-oriented spoken dialogue (CC-BY-NC-4.0, first-party recordings), used for spoken QA via AIR-Bench. Non-commercial.
Source: SpokenWOZ (Alibaba DAMO / Tianjin Univ.) — CC-BY-NC-4.0 — https://spokenwoz.github.io/ - SUMM-RE — CC-BY-SA-4.0 — hosted
French meeting corpus by LINAGORA (CC-BY-SA-4.0, first-party recordings). ASR evaluation prompts generated by OpenLLM-France. ShareAlike: this subset stays CC-BY-SA-compatible.
Source: SUMM-RE (LINAGORA) — CC-BY-SA-4.0 — https://huggingface.co/datasets/linagora/SUMM-RE - Vigogne (French Alpaca) — CC-BY-NC-4.0 (text), Synthesized by OpenLLM-France (Chatterbox TTS) (audio) — hosted
French instruction QA (Vigogne/Alpaca, CC-BY-NC-4.0). The spoken questions were synthesized by OpenLLM-France (Chatterbox TTS).
Source: [text] Vigogne / Alpaca (French) — CC-BY-NC-4.0 — https://github.com/bofenghuang/vigogne - VoxPopuli — CC0-1.0 — hosted
European Parliament recordings (CC0-1.0). ASR evaluation prompts generated by OpenLLM-France.
Source: VoxPopuli (European Parliament) — CC0-1.0 — https://github.com/facebookresearch/voxpopuli - VoxPopuli-QA — CC0-1.0 (transcripts; Q&A by OpenLLM-France) (text), Synthesized by OpenLLM-France (Chatterbox TTS) (audio) — hosted
Spoken QA over VoxPopuli (CC0). OpenLLM-France synthesized the spoken questions (Chatterbox TTS) and generated the question/answer text.
Source: [text] VoxPopuli transcripts (source) — CC0-1.0 — https://github.com/facebookresearch/voxpopuli
Text only
The conversations are published here, but the audio is not redistributable by us — download it from the original source(s):
- MELD — GPL-3.0 (text), Copyrighted (Friends TV series) (audio) — source
Multimodal emotion dataset from the Friends TV series. Emotion-recognition evaluation prompts generated by OpenLLM-France; the audio is copyrighted TV content and is not hosted here.
Source: [text] MELD (annotations) — GPL-3.0 — https://affective-meld.github.io/ - MTJ-Jamendo (via AIR-Bench) — CC-BY-NC-SA-4.0 (metadata) (text), Mixed per-track CC (Jamendo) — authorization required (audio) — source
Music tagging/QA over Jamendo tracks via AIR-Bench. Metadata is CC-BY-NC-SA-4.0; the audio has mixed per-track CC licenses plus a Jamendo clause requiring prior written authorization for redistribution, so the audio is not hosted here.
Sources:- [text] MTG-Jamendo (tags/metadata) — CC-BY-NC-SA-4.0 — https://github.com/MTG/mtg-jamendo-dataset
- [audio] Jamendo tracks (per-track CC; non-commercial research clause) — Mixed CC / Jamendo authorization — https://github.com/MTG/mtg-jamendo-dataset
- MusicCaps — CC-BY-SA-4.0 (text), Copyrighted (YouTube / music industry rights) (audio) — source
Music captioning + QA over MusicCaps clips (captions CC-BY-SA-4.0; audio is copyrighted YouTube content, not hosted). OpenLLM-France generated the English music Q&A from the captions and translated the French Q&A; captioning prompts were generated.
Sources:- [text] MusicCaps (captions) — CC-BY-SA-4.0 — https://www.kaggle.com/datasets/googleai/musiccaps
- [audio] YouTube / AudioSet (underlying music) — Copyrighted — https://research.google.com/audioset/
For YouTube-sourced audio (e.g. MusicCaps, YouTubeFr): the original video/clip ID is preserved in the audio file name (and in each record's extra field). Fetch the audio from YouTube with the source dataset's tooling or a downloader such as yt-dlp, then cut each segment using the offset / duration of the corresponding audio turn.
Not included
Neither the audio nor the conversations are published (the license forbids redistribution) — get the whole dataset from the source(s):
- Fisher (via AIR-Bench) — LDC User Agreement (research-only) (text), Copyrighted / LDC-restricted (audio) — source
Fisher English telephone speech, distributed by LDC under a non-transferable research-only agreement; neither audio nor transcripts may be redistributed. NOT included here. Obtain from LDC.
Source: Fisher English Training Speech (LDC2004S13 / LDC2005S13) — LDC User Agreement for Non-Members (research-only) — https://catalog.ldc.upenn.edu/LDC2004S13 - Multilingual TEDx — CC-BY-NC-ND-4.0 — source
TEDx talks for ASR and speech translation. The license is CC-BY-NC-ND-4.0: NoDerivatives forbids redistributing our re-segmented/reformatted version, so it is NOT included here (as in the training set). Obtain from the source. [DECISION FLAGGED: publishing eval references would be a derivative.]
Source: Multilingual TEDx (OpenSLR 100) — CC-BY-NC-ND-4.0 — https://www.openslr.org/100/ - YouTubeFr — Copyrighted (YouTube) — source
French YouTube speech. The audio is copyrighted YouTube content, so it is NOT included here (as in the training set). [DECISION FLAGGED.]
Source: YouTube (source videos) — Copyrighted (YouTube) — https://www.youtube.com/
Licensing
No single license covers the whole collection — it is a collection in which each source dataset retains its own license (see the table above). The curator's added layer (the evaluation prompts, the assembled reference answers, and the formatting) and the collection as a whole are offered under CC-BY-NC-SA-4.0, the most permissive single license consistent with the NonCommercial + ShareAlike terms of the included sources.
NoDerivatives (ND) and research-only sources (e.g. Multilingual TEDx, Fisher) may not be redistributed here; YouTube/broadcast-sourced audio (e.g. MELD, MusicCaps) is not hosted. Download those from their original source (see the table).
When in doubt, comply with the most restrictive applicable terms for the subset you use. If you are a rights holder and believe something should not be included, please open an issue.
Attribution
This dataset builds on the work of many others. Several sources are under attribution (CC-BY)* licenses and require credit. Please attribute every source dataset you use; the attribution-required ones are highlighted here:
The following datasets carry a CC-BY / attribution license on their text and/or audio — you must credit them (and cite their papers) when you use this dataset:
- AMI Meeting Corpus (CC-BY-4.0) — source
- FLEURS (CC-BY-4.0) — source
- MTJ-Jamendo (via AIR-Bench) (CC-BY-NC-SA-4.0 (metadata) (text), Mixed per-track CC (Jamendo) — authorization required (audio)) — source
- MusicCaps (CC-BY-SA-4.0 (text), Copyrighted (YouTube / music industry rights) (audio)) — source
- SLUE-SQA-5 (SLUE phase-2) (Apache-2.0 / CC-BY-SA-4.0) — source
- SpokenWOZ (via AIR-Bench) (CC-BY-NC-4.0) — source
- SUMM-RE (CC-BY-SA-4.0) — source
- Vigogne (French Alpaca) (CC-BY-NC-4.0 (text), Synthesized by OpenLLM-France (Chatterbox TTS) (audio)) — source
These also require keeping their text license/attribution notice (Apache-2.0 / MIT / CDLA):
Citation
If you use this dataset, please cite the OpenLLM-France / Luciole project and the individual source datasets you rely on (see the table above for sources).
Acknowledgements
Built by LINAGORA / OpenLLM-France. This evaluation collection would not exist without the authors of the many source datasets it builds upon.
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