Codesota · Speech · Speech Recognition · FLEURSTasks/Speech/Speech Recognition
Speech Recognition · benchmark dataset · 2022 · EN

Few-shot Learning Evaluation of Universal Representations of Speech.

Multilingual speech benchmark covering 100+ languages. Commonly used for ASR and speech-language model evaluation.

Paper ↗Download datasetSubmit a result ↵
§ 01 · Leaderboard

Best published scores.

1 result indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary
wer · lower is better
wer· primary
1 row
#ModelOrgSubmittedPaper / codewer
01Phi-4-Multimodal 5.6B—Mar 2025Phi-4-Mini Technical Report: Compact yet Powerful Multim…4.00
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 03 · Progress

1 steps
of state of the art.

Each row below marks a model that broke the previous record on wer. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.

Lower scores win. Each subsequent entry improved upon the previous best.

SOTA line · wer
  1. Mar 3, 2025Phi-4-Multimodal 5.6B4.00
Fig 3 · SOTA-setting models only. 1 entries span Mar 2025 → Mar 2025.
§ 04 · Literature

1 paper
tied to this benchmark.

Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.

§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result ↵Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies