---
license: apache-2.0
language:
- tg
tags:
- generated_from_trainer
- whisper-event
- hf-asr-leaderboard
metrics:
- wer
model-index:
- name: whisper-small-tg
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: tg_tj
      split: test
      args: tg_tj
    metrics:
    - name: Wer
      type: wer
      value: 28.3622
---

<!-- 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-tg

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [google/fleurs](https://huggingface.co/datasets/google/fleurs) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6917
- Wer: 28.3622

## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0011        | 25.0  | 1000 | 0.5801          | 28.1310 |
| 0.0004        | 50.0  | 2000 | 0.6423          | 28.2620 |
| 0.0002        | 75.0  | 3000 | 0.6796          | 28.3931 |
| 0.0002        | 100.0 | 4000 | 0.6917          | 28.3622 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2