DatasetLwaziANV

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Modelfacebook/omniASR-CTC-1BOpen10facebook/omniASR-CTC-300MOpen11facebook/omniASR-CTC-3BOpen11facebook/omniASR-CTC-7BOpen10facebook/omniASR-LLM-1BOpen11facebook/omniASR-LLM-300MOpen11facebook/omniASR-LLM-3BOpen10facebook/omniASR-LLM-7BOpen10aadel4/omniASR-CTC-1B-v2Open12aadel4/omniASR-CTC-300M-v2Open13badrex/w2v-bert-2.0-zulu-asrOpen12dsfsi/anv-whisper-large-v3-turbo-anv-zulOpen11dsfsi/anv-whisper-small-anv-zulu-first-batchOpen12facebook/mms-1b-allOpen12openai/whisper-large-v3Open15openai/whisper-smallOpen16sitwala/whisper-large-anv-sotOpen15sitwala/whisper-large-v3-anv-sotOpen15sitwala/whisper-large-v3-turbo-anv-zul-150hOpen12sitwala/whisper-large-v3-turbo-anv-zul-250hOpen12sitwala/whisper-large-v3-turbo-anv-zul-50hOpen12

What did the model get wrong?

facebook/omniASR-LLM-1BOpen   Example 48 of 100  ·  sample index 47

✅ What was actually said (isiZulu reference)
ngabeunem-[n],[s]una-ey-a-[n]unaloisu,olicabangayo[s]yini,Taj-[n]Tajewo.
🤖 What this model heard
ngabeunemi-una-eyiangiyanaloisuolicabangayoyinita-ta-jewu
Correct
Wrong (different word)
Missed (skipped)
Extra (added)
5
Correct
7
Wrong
4
Missed
0
Extra
31%
Words right
0.833
WER
0.250
CER
16.43s
Duration
0.5s
Latency