---
language:
- sw
license: apache-2.0
base_model: openai/whisper-large
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper small  - Denis Musinguzi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 14.0
      type: mozilla-foundation/common_voice_14_0
      config: lg
      split: None
      args: 'config: sw, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.2992427862915644
---

<!-- 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  - Denis Musinguzi

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co./openai/whisper-large) on the Common Voice 14.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3365
- Wer: 0.2992
- Cer: 0.0886

## 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: 32
- eval_batch_size: 32
- 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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 1.1439        | 0.3   | 800  | 0.1092 | 0.5335          | 0.4676 |
| 0.3861        | 0.61  | 1600 | 0.1112 | 0.4259          | 0.4185 |
| 0.3195        | 0.91  | 2400 | 0.0818 | 0.3794          | 0.3365 |
| 0.2447        | 1.22  | 3200 | 0.0898 | 0.3637          | 0.3310 |
| 0.2168        | 1.52  | 4000 | 0.0905 | 0.3473          | 0.3250 |
| 0.2099        | 1.82  | 4800 | 0.0874 | 0.3354          | 0.3205 |
| 0.1793        | 2.13  | 5600 | 0.0849 | 0.3376          | 0.3013 |
| 0.1437        | 2.43  | 6400 | 0.0823 | 0.3356          | 0.2985 |
| 0.14          | 2.74  | 7200 | 0.0833 | 0.3322          | 0.2953 |
| 0.1351        | 3.04  | 8000 | 0.0873 | 0.3328          | 0.2979 |
| 0.0994        | 3.34  | 8800 | 0.0699 | 0.3374          | 0.2838 |
| 0.0986        | 3.65  | 9600 | 0.3365 | 0.2992          | 0.0886 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2