transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer dataset:google/fleurs base_model:arun100/whisper-base-bn license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: arun100/whisper-base-bn
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Bengali
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs bn_in
      type: google/fleurs
      config: bn_in
      split: test
      args: bn_in
    metrics:
    - name: Wer
      type: wer
      value: 43.67604267701261
---

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# Whisper Base Bengali

This model is a fine-tuned version of [arun100/whisper-base-bn](https://huggingface.co/arun100/whisper-base-bn) on the google/fleurs bn_in dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2509
- Wer: 43.6760

## 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-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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2889        | 79.0  | 1000 | 0.2730          | 45.0242 |
| 0.2527        | 159.0 | 2000 | 0.2593          | 44.4617 |
| 0.2306        | 239.0 | 3000 | 0.2539          | 44.0616 |
| 0.2191        | 319.0 | 4000 | 0.2515          | 43.7367 |
| 0.2164        | 399.0 | 5000 | 0.2509          | 43.6760 |


### Framework versions

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