---
library_name: transformers
language:
- eng
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- kavyamanohar/supreme-court-speech
metrics:
- wer
model-index:
- name: kavyamanohar/whisper-supreme-court-asr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Supreme Court Hearing Corpus - Subset
      type: kavyamanohar/supreme-court-speech
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 69.86706056129985
---

<!-- 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. -->

# kavyamanohar/whisper-supreme-court-asr

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Supreme Court Hearing Corpus - Subset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8703
- Wer: 69.8671

## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.8763        | 5.5714  | 100  | 1.8904          | 162.7770 |
| 0.7801        | 11.1143 | 200  | 1.5403          | 55.0222  |
| 0.0716        | 16.6857 | 300  | 1.5505          | 56.7947  |
| 0.0118        | 22.2286 | 400  | 1.6573          | 56.8685  |
| 0.0076        | 27.8    | 500  | 1.7218          | 83.3087  |
| 0.0014        | 33.3429 | 600  | 1.7909          | 79.2467  |
| 0.0005        | 38.9143 | 700  | 1.8290          | 67.8730  |
| 0.0003        | 44.4571 | 800  | 1.8555          | 69.4239  |
| 0.0002        | 50.0    | 900  | 1.8669          | 69.7932  |
| 0.0002        | 55.5714 | 1000 | 1.8703          | 69.8671  |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0