transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer bn dataset:mozilla-foundation/common_voice_16_0 base_model:arun100/whisper-base-bn-6 license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- bn
license: apache-2.0
base_model: arun100/whisper-base-bn-6
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Bengali
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 bn
      type: mozilla-foundation/common_voice_16_0
      config: bn
      split: test
      args: bn
    metrics:
    - name: Wer
      type: wer
      value: 27.7209560369744
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Base Bengali

This model is a fine-tuned version of [arun100/whisper-base-bn-6](https://huggingface.co/arun100/whisper-base-bn-6) on the mozilla-foundation/common_voice_16_0 bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2028
- Wer: 27.7210

## 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: 5e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1388        | 1.72  | 500   | 0.2076          | 28.8529 |
| 0.1335        | 3.43  | 1000  | 0.2068          | 28.6171 |
| 0.1297        | 5.15  | 1500  | 0.2061          | 28.4462 |
| 0.13          | 6.86  | 2000  | 0.2050          | 28.4274 |
| 0.126         | 8.58  | 2500  | 0.2046          | 28.3500 |
| 0.122         | 10.29 | 3000  | 0.2044          | 28.1872 |
| 0.1205        | 12.01 | 3500  | 0.2039          | 28.1715 |
| 0.1187        | 13.72 | 4000  | 0.2038          | 28.0136 |
| 0.1152        | 15.44 | 4500  | 0.2035          | 28.0503 |
| 0.1133        | 17.15 | 5000  | 0.2035          | 28.0395 |
| 0.1167        | 18.87 | 5500  | 0.2031          | 27.9071 |
| 0.1119        | 20.58 | 6000  | 0.2032          | 27.8288 |
| 0.1168        | 22.3  | 6500  | 0.2030          | 27.8306 |
| 0.1129        | 24.01 | 7000  | 0.2029          | 27.7778 |
| 0.112         | 25.73 | 7500  | 0.2030          | 27.7415 |
| 0.1105        | 27.44 | 8000  | 0.2030          | 27.7482 |
| 0.1114        | 29.16 | 8500  | 0.2028          | 27.7608 |
| 0.1102        | 30.87 | 9000  | 0.2029          | 27.7357 |
| 0.1115        | 32.59 | 9500  | 0.2028          | 27.7210 |
| 0.1083        | 34.31 | 10000 | 0.2028          | 27.7353 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
