Dataset
Lwazi
ANV
← All examples
Model summary →
Model
facebook/omniASR-CTC-1B
Open
10
facebook/omniASR-CTC-300M
Open
11
facebook/omniASR-CTC-3B
Open
11
facebook/omniASR-CTC-7B
Open
10
facebook/omniASR-LLM-1B
Open
11
facebook/omniASR-LLM-300M
Open
11
facebook/omniASR-LLM-3B
Open
10
facebook/omniASR-LLM-7B
Open
10
aadel4/omniASR-CTC-1B-v2
Open
12
aadel4/omniASR-CTC-300M-v2
Open
13
badrex/w2v-bert-2.0-zulu-asr
Open
12
dsfsi/anv-whisper-large-v3-turbo-anv-zul
Open
11
dsfsi/anv-whisper-small-anv-zulu-first-batch
Open
12
facebook/mms-1b-all
Open
12
openai/whisper-large-v3
Open
15
openai/whisper-small
Open
16
sitwala/whisper-large-anv-sot
Open
15
sitwala/whisper-large-v3-anv-sot
Open
15
sitwala/whisper-large-v3-turbo-anv-zul-150h
Open
12
sitwala/whisper-large-v3-turbo-anv-zul-250h
Open
12
sitwala/whisper-large-v3-turbo-anv-zul-50h
Open
12
What did the model get wrong?
badrex/w2v-bert-2.0-zulu-asr
Open
Example 48 of 100 · sample index 47
✅ What was actually said (isiZulu reference)
ngabe
unem-
[n],
[s]
una-
ey-
a-
[n]
unalo
isu,
olicabangayo
[s]
yini,
Taj-
[n]
Tajewo.
🤖 What this model heard
ngabe
pase
uyi
ginalo
isu
olicabangayo
yini
se
dajeu
Correct
Wrong (different word)
Missed (skipped)
Extra (added)
4
Correct
5
Wrong
7
Missed
0
Extra
25%
Words right
0.833
WER
0.397
CER
16.43s
Duration
0.1s
Latency