---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium Tajik
  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: 23.153018764230197
---

<!-- 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 Medium Tajik

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the google/fleurs tg_tj dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9217
- Wer: 23.1530

## 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: 32
- eval_batch_size: 16
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0016        | 66.0  | 1000 | 0.6929          | 24.2993 |
| 0.0001        | 133.0 | 2000 | 0.8054          | 23.3022 |
| 0.0001        | 199.0 | 3000 | 0.8652          | 23.2237 |
| 0.0           | 266.0 | 4000 | 0.9019          | 23.2394 |
| 0.0           | 333.0 | 5000 | 0.9217          | 23.1530 |


### Framework versions

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