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whisper-th-small-bio-finetuned

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Thai
whisper
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whisper-th-small-bio-finetuned
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---
library_name: transformers
language:
- th
license: apache-2.0
base_model: biodatlab/whisper-th-small-combined
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: 0.41320489664860527
---

<!-- 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 [biodatlab/whisper-th-small-combined](https://huggingface.co/biodatlab/whisper-th-small-combined) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0702
- Wer: 0.4132

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.3362        | 0.2175 | 1000 | 0.1439          | 0.6061 |
| 0.2993        | 0.4349 | 2000 | 0.1230          | 0.5645 |
| 0.2523        | 0.6524 | 3000 | 0.1080          | 0.5299 |
| 0.2823        | 0.8698 | 4000 | 0.0939          | 0.4914 |
| 0.2459        | 1.0873 | 5000 | 0.0840          | 0.4570 |
| 0.2005        | 1.3047 | 6000 | 0.0776          | 0.4364 |
| 0.2081        | 1.5222 | 7000 | 0.0724          | 0.4157 |
| 0.1918        | 1.7396 | 8000 | 0.0702          | 0.4132 |


### Framework versions

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