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---
license: cc-by-nc-4.0
language:
- bn
library_name: nemo
pipeline_tag: automatic-speech-recognition
---
## Hishab BN FastConformer
__Hishab BN FastConformer__ is a [fastconformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer) based model trained on ~18K Hours [MegaBNSpeech]() corpus.
## Using method
This model can be used for transcribing Bangla audio and also can be used as pre-trained model to fine-tuning on custom datasets using [NeMo](https://github.com/NVIDIA/NeMo) framework.
### Installation
To install [NeMo](https://github.com/NVIDIA/NeMo) check NeMo documentation.
### Inferencing
```py
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("hishab/hishab_bn_fastconformer")
transcriptions = asr_model.transcribe(["file.wav"])
```
## Training Datasets
| Channels Category | Hours |
| ----------------- | ----------- |
| News | 17,640.00 |
| Talkshow | 688.82 |
| Vlog | 0.02 |
| Crime Show | 4.08 |
| Total | 18,332.92 |
## Training Details
For training the model, the dataset we selected comprises 17.64k hours of news chan- nel content, 688.82 hours of talk shows, 0.02 hours of vlogs, and 4.08 hours of crime shows.
## Evaluation
## Citation
|