---
library_name: transformers
language:
- sw
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small sw - Arindam Bose
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: sw
      split: None
      args: 'config: sw, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 28.8790031496425
---

<!-- 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 sw - Arindam Bose

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4499
- Wer: 28.8790

## 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: 500
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4791        | 0.4342 | 1000 | 0.5847          | 37.9256 |
| 0.396         | 0.8684 | 2000 | 0.4834          | 31.9206 |
| 0.2344        | 1.3026 | 3000 | 0.4631          | 31.2366 |
| 0.2237        | 1.7369 | 4000 | 0.4402          | 29.6515 |
| 0.1096        | 2.1711 | 5000 | 0.4468          | 29.0480 |
| 0.1219        | 2.6053 | 6000 | 0.4431          | 28.6169 |
| 0.0743        | 3.0395 | 7000 | 0.4428          | 28.3318 |
| 0.0691        | 3.4737 | 8000 | 0.4499          | 28.8790 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1