---
language:
- sn
license: apache-2.0
library_name: peft
tags:
- whisper-event and peft-lora
- generated_from_trainer
base_model: openai/whisper-tiny
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Tiny Sn - Bright Chirindo
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: sn_zw
      split: None
      args: sn_zw
    metrics:
    - type: wer
      value: 95.27619047619048
      name: Wer
---

<!-- 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 Tiny Sn - Bright Chirindo

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9909
- Wer: 95.2762

## 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: 8
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.087         | 3.0164  | 1000 | 2.4026          | 109.4095 |
| 1.8305        | 6.0328  | 2000 | 2.1613          | 101.2419 |
| 1.7145        | 9.0492  | 3000 | 2.0536          | 99.8705  |
| 1.6314        | 13.0044 | 4000 | 2.0050          | 99.0095  |
| 1.665         | 16.0208 | 5000 | 1.9909          | 95.2762  |


### Framework versions

- PEFT 0.10.1.dev0
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.2.dev0
- Tokenizers 0.19.1