---
library_name: transformers
language:
- tg
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Tajik
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: tg_tj
      split: None
      args: 'config: tg, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 24.260635774157837
---

<!-- 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 Tajik

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4141
- Wer: 24.2606

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 2.7687        | 1.0   | 79   | 0.5778          | 39.6568 |
| 0.7193        | 2.0   | 158  | 0.3890          | 28.3568 |
| 0.3659        | 3.0   | 237  | 0.3611          | 26.0636 |
| 0.2021        | 4.0   | 316  | 0.3629          | 25.1068 |
| 0.1099        | 5.0   | 395  | 0.3740          | 25.3044 |
| 0.0597        | 6.0   | 474  | 0.3887          | 24.3081 |
| 0.0339        | 7.0   | 553  | 0.4005          | 24.6639 |
| 0.0213        | 8.0   | 632  | 0.4082          | 24.3239 |
| 0.0158        | 9.0   | 711  | 0.4131          | 24.2685 |
| 0.014         | 10.0  | 790  | 0.4141          | 24.2606 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0