---
language:
- hi
base_model: nurzhanit/whisper-enhanced-ml
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: default
      split: None
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.0
---

<!-- 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 - Sanchit Gandhi

This model is a fine-tuned version of [nurzhanit/whisper-enhanced-ml](https://huggingface.co/nurzhanit/whisper-enhanced-ml) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 0.0

## 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: 200
- training_steps: 4000

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:---:|
| 0.0           | 16.6667  | 100  | 0.0000          | 0.0 |
| 0.0           | 33.3333  | 200  | 0.0000          | 0.0 |
| 0.0           | 50.0     | 300  | 0.0000          | 0.0 |
| 0.0           | 66.6667  | 400  | 0.0000          | 0.0 |
| 0.0           | 83.3333  | 500  | 0.0000          | 0.0 |
| 0.0           | 100.0    | 600  | 0.0000          | 0.0 |
| 0.0           | 116.6667 | 700  | 0.0000          | 0.0 |
| 0.0           | 133.3333 | 800  | 0.0000          | 0.0 |
| 0.0           | 150.0    | 900  | 0.0000          | 0.0 |
| 0.0           | 166.6667 | 1000 | 0.0000          | 0.0 |
| 0.0           | 183.3333 | 1100 | 0.0000          | 0.0 |
| 0.0           | 200.0    | 1200 | 0.0000          | 0.0 |
| 0.0           | 216.6667 | 1300 | 0.0000          | 0.0 |
| 0.0           | 233.3333 | 1400 | 0.0000          | 0.0 |
| 0.0           | 250.0    | 1500 | 0.0000          | 0.0 |
| 0.0           | 266.6667 | 1600 | 0.0000          | 0.0 |
| 0.0           | 283.3333 | 1700 | 0.0000          | 0.0 |
| 0.0           | 300.0    | 1800 | 0.0000          | 0.0 |
| 0.0           | 316.6667 | 1900 | 0.0000          | 0.0 |
| 0.0           | 333.3333 | 2000 | 0.0000          | 0.0 |
| 0.0           | 350.0    | 2100 | 0.0000          | 0.0 |
| 0.0           | 366.6667 | 2200 | 0.0000          | 0.0 |
| 0.0           | 383.3333 | 2300 | 0.0000          | 0.0 |
| 0.0           | 400.0    | 2400 | 0.0000          | 0.0 |
| 0.0           | 416.6667 | 2500 | 0.0000          | 0.0 |
| 0.0           | 433.3333 | 2600 | 0.0000          | 0.0 |
| 0.0           | 450.0    | 2700 | 0.0000          | 0.0 |
| 0.0           | 466.6667 | 2800 | 0.0000          | 0.0 |
| 0.0           | 483.3333 | 2900 | 0.0000          | 0.0 |
| 0.0           | 500.0    | 3000 | 0.0000          | 0.0 |
| 0.0           | 516.6667 | 3100 | 0.0000          | 0.0 |
| 0.0           | 533.3333 | 3200 | 0.0000          | 0.0 |
| 0.0           | 550.0    | 3300 | 0.0000          | 0.0 |
| 0.0           | 566.6667 | 3400 | 0.0000          | 0.0 |
| 0.0           | 583.3333 | 3500 | 0.0000          | 0.0 |
| 0.0           | 600.0    | 3600 | 0.0000          | 0.0 |
| 0.0           | 616.6667 | 3700 | 0.0000          | 0.0 |
| 0.0           | 633.3333 | 3800 | 0.0000          | 0.0 |
| 0.0           | 650.0    | 3900 | 0.0000          | 0.0 |
| 0.0           | 666.6667 | 4000 | 0.0000          | 0.0 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.2
- Tokenizers 0.19.1