---
library_name: transformers
language:
- tg
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- whisper-event
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: Whisper Tajik
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: CUSTOM
      type: fleurs
      config: tg_tj
      split: None
      args: 'config: tg, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 18.951830443159924
---

<!-- 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 Tajik

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the CUSTOM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4538
- Wer: 18.9518

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0245        | 6.2893  | 1000 | 0.3726          | 20.9634 |
| 0.0043        | 12.5786 | 2000 | 0.4167          | 20.5318 |
| 0.0003        | 18.8679 | 3000 | 0.4431          | 19.2062 |
| 0.0002        | 25.1572 | 4000 | 0.4538          | 18.9518 |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0