---
language:
- ru
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- tbkazakova/even_speech_biblical
metrics:
- wer
model-index:
- name: Whisper Small Even - Sanchit Gandhi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Even Speech Biblical
      type: tbkazakova/even_speech_biblical
      config: default
      split: None
      args: 'config: ru, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 53.88015717092338
---

<!-- 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 Even - Sanchit Gandhi

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Even Speech Biblical dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4591
- Wer: 53.8802

## 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: 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: 300
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.05          | 5.9880  | 500  | 0.3920          | 63.7525 |
| 0.0022        | 11.9760 | 1000 | 0.4307          | 57.3674 |
| 0.0003        | 17.9641 | 1500 | 0.4528          | 51.7682 |
| 0.0003        | 23.9521 | 2000 | 0.4591          | 53.8802 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1