---
base_model: openai/whisper-small
datasets:
- common_voice_11_0
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-hi
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: hi
      split: test
      args: hi
    metrics:
    - type: wer
      value: 46.643528316261744
      name: Wer
---

<!-- 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-small-hi

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4191
- Wer: 46.6435

## 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: 16
- eval_batch_size: 8
- seed: 42
- 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3565        | 0.2445 | 100  | 0.4191          | 46.6435 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1