---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small Tr - CV 43h - Linear Decay
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: tr
      split: None
      args: 'config: tr, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 21.631772845718395
---

<!-- 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 Tr - CV 43h - Linear Decay

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2554
- Wer: 21.6318

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2413        | 0.37  | 500  | 0.2845          | 23.9500 |
| 0.2005        | 0.73  | 1000 | 0.2615          | 22.3219 |
| 0.1583        | 1.1   | 1500 | 0.2493          | 21.5365 |
| 0.1513        | 1.46  | 2000 | 0.2485          | 21.5455 |
| 0.1502        | 1.83  | 2500 | 0.2407          | 21.0639 |
| 0.0959        | 2.19  | 3000 | 0.2485          | 21.1987 |
| 0.1074        | 2.56  | 3500 | 0.2539          | 21.8762 |
| 0.105         | 2.92  | 4000 | 0.2554          | 21.6318 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2