---
language:
- hi
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- pranetk/paraspeak-data-v3
metrics:
- wer
model-index:
- name: Whisper Large V3 Paraspeak V2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Paraspeak Dataset 3.0
      type: pranetk/paraspeak-data-v3
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 62.121212121212125
---

<!-- 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 Large V3 Paraspeak V2

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Paraspeak Dataset 3.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0662
- Wer: 62.1212

## 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: 3e-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
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.1361        | 2.9412  | 50   | 0.8743          | 77.2727 |
| 0.0603        | 5.8824  | 100  | 1.0115          | 65.1515 |
| 0.0452        | 8.8235  | 150  | 1.0837          | 71.2121 |
| 0.0042        | 11.7647 | 200  | 1.0400          | 78.7879 |
| 0.023         | 14.7059 | 250  | 1.0296          | 71.2121 |
| 0.0023        | 17.6471 | 300  | 0.9761          | 69.6970 |
| 0.0005        | 20.5882 | 350  | 1.0758          | 71.2121 |
| 0.0098        | 23.5294 | 400  | 1.1036          | 71.2121 |
| 0.0006        | 26.4706 | 450  | 1.0662          | 65.1515 |
| 0.0001        | 29.4118 | 500  | 1.0563          | 62.1212 |
| 0.0           | 32.3529 | 550  | 1.0521          | 62.1212 |
| 0.0           | 35.2941 | 600  | 1.0541          | 62.1212 |
| 0.0           | 38.2353 | 650  | 1.0563          | 62.1212 |
| 0.0           | 41.1765 | 700  | 1.0587          | 62.1212 |
| 0.0           | 44.1176 | 750  | 1.0609          | 62.1212 |
| 0.0           | 47.0588 | 800  | 1.0628          | 62.1212 |
| 0.0           | 50.0    | 850  | 1.0641          | 62.1212 |
| 0.0           | 52.9412 | 900  | 1.0653          | 62.1212 |
| 0.0           | 55.8824 | 950  | 1.0659          | 62.1212 |
| 0.0           | 58.8235 | 1000 | 1.0662          | 62.1212 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1