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MegaBNSpeech Test Data
To evaluate the performance of the models, we used four test sets. Two of these were developed as part of the MegaBNSpeech corpus, while the remaining two (Fleurs and Common Voice) are commonly used test sets that are widely recognized by the speech community.
Use dataset library:
from datasets import load_dataset
dataset = load_dataset("hishab/MegaBNSpeech_Test_Data")
Reported Word error rate (WER) /character error rate (CER) on four test sets using four ASR systems
| Category | Duration (hr) | Hishab BN Fastconformer | Google MMS | OOD-speech |
|---|---|---|---|---|
| MegaBNSpeech-YT | 8.1 | 6.4/3.39 | 28.3/18.88 | 51.1/23.49 |
| MegaBNSpeech-Tel | 1.9 | ∗40.7/24.38 | ∗59/41.26 | ∗76.8/39.36 |
Reported Word error rate (WER) /character error rate (CER) on different categories present in Hishab BN FastConformer
| Category | Duration (hr) | Hishab BN FastConformer | Google MMS | OOD-speech |
|---|---|---|---|---|
| News | 1.21 | 2.5/1.21 | 18.9/10.46 | 52.2/21.65 |
| Talkshow | 1.39 | 6/3.29 | 28/18.71 | 48.8/21.5 |
| Courses | 3.81 | 6.8/3.79 | 30.8/21.64 | 50.2/23.52 |
| Drama | 0.03 | 10.3/7.47 | 37.3/27.43 | 64.3/32.74 |
| Science | 0.26 | 5/1.92 | 20.6/11.4 | 45.3/19.93 |
| Vlog | 0.18 | 11.3/6.69 | 33/22.9 | 57.9/27.18 |
| Recipie | 0.58 | 7.5/3.29 | 26.4/16.6 | 53.3/26.89 |
| Waz | 0.49 | 9.6/5.45 | 33.3/23.1 | 57.3/27.46 |
| Movie | 0.1 | 8/4.64 | 35.2/23.88 | 64.4/34.96 |
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