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---
language:
- bn
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: duration
dtype: float64
- name: category
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 219091915.875
num_examples: 1753
download_size: 214321460
dataset_size: 219091915.875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 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:
```python
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 |
|