---
library_name: transformers
language:
- en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- WillHeld/india_accent_cv
metrics:
- wer
model-index:
- name: Whisper Indian English Acccent
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Indian English Accent
      type: WillHeld/india_accent_cv
      args: 'split: train'
    metrics:
    - type: wer
      value: 7.5056000168263415
      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 Indian English Acccent

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Indian English Accent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2065
- Wer: 7.5056

## 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: 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
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.342         | 0.1943 | 1000  | 0.3226          | 14.1310 |
| 0.2741        | 0.3885 | 2000  | 0.3130          | 13.9553 |
| 0.2576        | 0.5828 | 3000  | 0.2967          | 12.9931 |
| 0.2825        | 0.7770 | 4000  | 0.2692          | 12.3390 |
| 0.2295        | 0.9713 | 5000  | 0.2565          | 11.8331 |
| 0.1489        | 1.1655 | 6000  | 0.2498          | 11.6933 |
| 0.1485        | 1.3598 | 7000  | 0.2452          | 11.1411 |
| 0.1385        | 1.5540 | 8000  | 0.2346          | 10.4428 |
| 0.1253        | 1.7483 | 9000  | 0.2254          | 10.1852 |
| 0.1297        | 1.9425 | 10000 | 0.2144          | 9.7109  |
| 0.0594        | 2.1368 | 11000 | 0.2174          | 9.5363  |
| 0.0629        | 2.3310 | 12000 | 0.2136          | 9.8276  |
| 0.0654        | 2.5253 | 13000 | 0.2102          | 9.4301  |
| 0.0625        | 2.7195 | 14000 | 0.2075          | 8.9432  |
| 0.0574        | 2.9138 | 15000 | 0.2009          | 8.7802  |
| 0.0276        | 3.1080 | 16000 | 0.2050          | 8.4594  |
| 0.0251        | 3.3023 | 17000 | 0.2046          | 8.5951  |
| 0.0246        | 3.4965 | 18000 | 0.2035          | 8.1187  |
| 0.0259        | 3.6908 | 19000 | 0.2002          | 8.0588  |
| 0.021         | 3.8850 | 20000 | 0.1951          | 7.9147  |
| 0.0072        | 4.0793 | 21000 | 0.2053          | 7.7548  |
| 0.0067        | 4.2735 | 22000 | 0.2085          | 7.4972  |
| 0.0067        | 4.4678 | 23000 | 0.2094          | 7.6970  |
| 0.0062        | 4.6620 | 24000 | 0.2071          | 7.7433  |
| 0.0046        | 4.8563 | 25000 | 0.2065          | 7.5056  |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.2.0a0+81ea7a4
- Datasets 3.3.2
- Tokenizers 0.21.0