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Codesota · Speech · Speech Translation · MuST-C En-De tst-COMMONTasks/Speech/Speech Translation
Speech Translation · benchmark dataset · 2019 · EN

MuST-C English-German tst-COMMON.

Multilingual Speech Translation Corpus built from TED talks. The English-German tst-COMMON split is the de-facto benchmark for end-to-end speech translation. BLEU on tst-COMMON is the primary metric.

Paper ↗Submit a result ↵
§ 01 · Leaderboard

Best published scores.

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

Primary
bleu · higher is better
bleu· primary
3 rows
#ModelOrgSubmittedPaper / codebleu
01SeamlessM4T v2 LargeOSSMeta AIApr 2026editorial37.10
02Whisper Large-v2OSSOpenAIApr 2026editorial29
03Fairseq S2T (MuST-C)OSSMeta AIApr 2026editorial22.70
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 bleu. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.

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

SOTA line · bleu
  1. Apr 5, 2026SeamlessM4T v2 LargeMeta AI37.10
Fig 3 · SOTA-setting models only. 1 entries span Apr 2026 → Apr 2026.
§ 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