---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- razhan/common_voice_ckb_16
metrics:
- wer
model-index:
- name: whisper-tiny-ckb
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: razhan/common_voice_ckb_16
      type: razhan/common_voice_ckb_16
    metrics:
    - name: Wer
      type: wer
      value: 0.47801004237740824
---

<!-- 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-ckb

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the razhan/common_voice_ckb_16 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2612
- Wer: 0.4780

## 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: 192
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 768
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 600
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4502        | 0.72  | 100  | 0.4988          | 0.7166 |
| 0.2977        | 1.45  | 200  | 0.3557          | 0.5859 |
| 0.2494        | 2.17  | 300  | 0.3096          | 0.5315 |
| 0.2224        | 2.9   | 400  | 0.2817          | 0.5008 |
| 0.2148        | 3.62  | 500  | 0.2666          | 0.4819 |
| 0.2096        | 4.35  | 600  | 0.2612          | 0.4780 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0