DatasetLwaziANV

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Modelfacebook/omniASR-CTC-1BOpen9facebook/omniASR-CTC-300MOpen12facebook/omniASR-CTC-3BOpen10facebook/omniASR-LLM-1BOpen8facebook/omniASR-LLM-300MOpen8facebook/omniASR-LLM-3BOpen8facebook/omniASR-LLM-7BOpen3dsfsi/anv-whisper-large-v3-turbo-anv-zulOpen6dsfsi/anv-whisper-small-anv-zulu-first-batchOpen9openai/whisper-large-v3Open23openai/whisper-smallOpen32sitwala/whisper-large-anv-sotOpen36sitwala/whisper-large-v3-anv-sotOpen36sitwala/whisper-large-v3-turbo-anv-zul-150hOpen7sitwala/whisper-large-v3-turbo-anv-zul-250hOpen7sitwala/whisper-large-v3-turbo-anv-zul-50hOpen10

What did the model get wrong?

sitwala/whisper-large-anv-sotOpen   Example 30 of 100  ·  sample index 29

✅ What was actually said (isiZulu reference)
Ngenxayalokho,iNcwadika-Abrahamaibengumthombowezingxabanoezibalulekile,ngokugxekwakwezazizase-EgyptkanyenabaxolisibamaMormonbevikelaubuqinisobayo.
🤖 What this model heard
kengayalokoenngwatekaeprahaebengomthongbowezengkabanoezebalolekilengokokaeqakwezaasesaseegyptkanyelehabakeolesebamangmongmongbafekelaoboqenisoba
Correct
Wrong (different word)
Missed (skipped)
Extra (added)
0
Correct
17
Wrong
0
Missed
19
Extra
0%
Words right
2.118
WER
0.463
CER
18.75s
Duration
0.2s
Latency