transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer hi dataset:mozilla-foundation/common_voice_16_0 base_model:vasista22/whisper-hindi-small license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- hi
license: apache-2.0
base_model: vasista22/whisper-hindi-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Small Hindi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 hi
      type: mozilla-foundation/common_voice_16_0
      config: hi
      split: test
      args: hi
    metrics:
    - name: Wer
      type: wer
      value: 10.110413057544196
---

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# Whisper Base Bengali

This model is a fine-tuned version of [vasista22/whisper-hindi-small](https://huggingface.co/vasista22/whisper-hindi-small) on the mozilla-foundation/common_voice_16_0 hi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1836
- Wer: 10.1104

## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1889        | 0.5   | 50   | 0.1993          | 10.3734 |
| 0.1597        | 1.24  | 100  | 0.1836          | 10.1104 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
