---
library_name: transformers
language:
- th
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small Th Combined Finetuned
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: th
      split: test
      args: 'config: th, split: validated'
    metrics:
    - name: Wer
      type: wer
      value: 0.4430396682052311
---

<!-- 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 Th Combined Finetuned

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0858
- Wer: 0.4430

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.2913        | 0.2175 | 1000 | 0.2658          | 0.7758 |
| 0.2112        | 0.4349 | 2000 | 0.1918          | 0.6780 |
| 0.1733        | 0.6524 | 3000 | 0.1544          | 0.6206 |
| 0.1485        | 0.8698 | 4000 | 0.1279          | 0.5651 |
| 0.1029        | 1.0873 | 5000 | 0.1102          | 0.5119 |
| 0.0989        | 1.3047 | 6000 | 0.0983          | 0.4775 |
| 0.0935        | 1.5222 | 7000 | 0.0901          | 0.4566 |
| 0.0863        | 1.7396 | 8000 | 0.0858          | 0.4430 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0