---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- razhan/common_voice_ckb_16
metrics:
- wer
model-index:
- name: whisper-base-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: 0.2917510463075469
---

<!-- 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-base-ckb

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the razhan/common_voice_ckb_16 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1420
- Wer: 0.2918

## 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: 6
- total_train_batch_size: 1152
- total_eval_batch_size: 768
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3434        | 1.09  | 100  | 0.3840          | 0.6054 |
| 0.2089        | 2.17  | 200  | 0.2654          | 0.4740 |
| 0.167         | 3.26  | 300  | 0.2246          | 0.4190 |
| 0.1452        | 4.35  | 400  | 0.1964          | 0.3803 |
| 0.1287        | 5.43  | 500  | 0.1788          | 0.3542 |
| 0.1163        | 6.52  | 600  | 0.1650          | 0.3326 |
| 0.1068        | 7.61  | 700  | 0.1560          | 0.3155 |
| 0.1015        | 8.7   | 800  | 0.1489          | 0.3059 |
| 0.0968        | 9.78  | 900  | 0.1440          | 0.2954 |
| 0.0939        | 10.87 | 1000 | 0.1420          | 0.2918 |


### Framework versions

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