---
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 Large  - 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: 0.24669449134992194
---

<!-- 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  - 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: 0.2966
- Wer: 0.2467
- Cer: 0.0700

## 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: 16
- 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: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 0.6329        | 0.61  | 1600 | 0.0878 | 0.3515          | 0.3385 |
| 0.2241        | 1.22  | 3200 | 0.0589 | 0.3045          | 0.2517 |
| 0.1618        | 1.82  | 4800 | 0.0707 | 0.2801          | 0.2645 |
| 0.1109        | 2.43  | 6400 | 0.0774 | 0.2870          | 0.2580 |
| 0.0837        | 3.04  | 8000 | 0.0597 | 0.2900          | 0.2333 |
| 0.045         | 3.65  | 9600 | 0.2966 | 0.2467          | 0.0700 |


### Framework versions

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