---
language:
- tt
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small TT
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: tt
      split: None
      args: 'config: tt, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 34.84448939782538
---

<!-- 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 fine-tuned for Tatar language

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2809
- Wer: 34.8445

## Training and evaluation data

Training data was taken from Common Voice 16.1 dataset

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1399        | 1.2293 | 1000 | 0.3081          | 38.2040 |
| 0.0639        | 2.4585 | 2000 | 0.2809          | 34.8445 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1