transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer hi dataset:mozilla-foundation/common_voice_16_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base 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: 28.648953267516852
---

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# Whisper Base Hindi

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 hi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4679
- Wer: 28.6490

## 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: 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6425        | 6.01  | 500  | 0.7025          | 41.4477 |
| 0.3973        | 13.0  | 1000 | 0.5367          | 33.9692 |
| 0.3125        | 19.01 | 1500 | 0.4927          | 31.4458 |
| 0.2848        | 26.0  | 2000 | 0.4739          | 30.1037 |
| 0.2201        | 32.01 | 2500 | 0.4675          | 29.4859 |
| 0.2257        | 39.01 | 3000 | 0.4637          | 28.9933 |
| 0.1837        | 46.0  | 3500 | 0.4657          | 28.9140 |
| 0.1897        | 52.01 | 4000 | 0.4658          | 28.7450 |
| 0.1764        | 59.0  | 4500 | 0.4676          | 28.7178 |
| 0.1681        | 65.01 | 5000 | 0.4679          | 28.6490 |


### Framework versions

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