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--- |
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license: cc-by-nc-4.0 |
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language: |
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- bn |
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library_name: nemo |
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pipeline_tag: automatic-speech-recognition |
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--- |
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## Hishab BN FastConformer |
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__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. |
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## Using method |
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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. |
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### Installation |
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To install [NeMo](https://github.com/NVIDIA/NeMo) check NeMo documentation. |
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### Inferencing |
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```py |
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import nemo.collections.asr as nemo_asr |
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asr_model = nemo_asr.models.ASRModel.from_pretrained("hishab/hishab_bn_fastconformer") |
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transcriptions = asr_model.transcribe(["file.wav"]) |
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``` |
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## Training Datasets |
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| Channels Category | Hours | |
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| ----------------- | ----------- | |
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| News | 17,640.00 | |
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| Talkshow | 688.82 | |
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| Vlog | 0.02 | |
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| Crime Show | 4.08 | |
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| Total | 18,332.92 | |
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## Training Details |
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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. |
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## Evaluation |
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## Citation |
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