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LLM Stats
FLEURS
Few-shot Learning Evaluation of Universal Representations of Speech - a parallel speech dataset in 102 languages built on FLoRes-101 with approximately 12 hours of speech supervision per language for tasks including ASR, speech language identification, translation and retrieval. Scores are shown as speech recognition accuracy (1 - word error rate), so higher is better.
Evaluation context and evidence
- Source
- LLM Stats
- Publisher
- Not recorded
- Benchmark release
- Not recorded
- Collected
- 2026-08-17
- Modality
- audio
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 6
Shown in source observation order, not ranked. Dates retain the precision provided by the source.
| Model | Value | Unit | Protocol | Reported date and basis | Source |
|---|---|---|---|---|---|
| Qwen2.5-Omni-7B | 0.959 | Not recorded | Not recorded | 2025-03-27 · model_announcement | LLM Stats |
| Gemini 1.0 Pro | 0.936 | Not recorded | Not recorded | 2024-02-15 · model_announcement | LLM Stats |
| Gemini 1.5 Pro | 0.933 | Not recorded | Not recorded | 2024-05-01 · model_announcement | LLM Stats |
| Gemma 4 12B | 0.931 | Not recorded | Not recorded | 2026-05-23 · model_announcement | LLM Stats |
| Gemini 1.5 Flash | 0.904 | Not recorded | Not recorded | 2024-05-01 · model_announcement | LLM Stats |
| Gemini 1.5 Flash 8B | 0.864 | Not recorded | Not recorded | 2024-03-15 · model_announcement | LLM Stats |