---
language:
- bn
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: Whisper Small finetuned on Bengali
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15
      type: mozilla-foundation/common_voice_15_0
      config: bn
      split: validation
      args: bn
    metrics:
    - name: Wer
      type: wer
      value: 33.68672144182348
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Small finetuned on Bengali

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2886
- Wer Ortho: 66.3996
- Wer: 33.6867

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.5075        | 0.8   | 100  | 0.4573          | 77.5920   | 45.3485 |
| 0.257         | 1.6   | 200  | 0.2886          | 66.3996   | 33.6867 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0