---
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: 27.434200914195074
---

<!-- 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 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: 0.4926
- Wer: 27.4342

## 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: 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: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.563         | 2.02  | 200  | 0.6270          | 38.2146 |
| 0.3107        | 5.01  | 400  | 0.4695          | 30.0641 |
| 0.1535        | 7.03  | 600  | 0.4548          | 27.7139 |
| 0.0841        | 10.02 | 800  | 0.4926          | 27.4342 |
| 0.0357        | 13.01 | 1000 | 0.5585          | 28.1772 |
| 0.0152        | 15.03 | 1200 | 0.6247          | 28.0687 |
| 0.0063        | 18.02 | 1400 | 0.6796          | 28.1856 |
| 0.0036        | 21.0  | 1600 | 0.7097          | 28.2670 |
| 0.0029        | 23.03 | 1800 | 0.7270          | 28.1960 |
| 0.0024        | 26.01 | 2000 | 0.7336          | 28.2649 |


### Framework versions

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