---
language:
- ru
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- bond005/sberdevices_golos_10h_crowd
metrics:
- wer
model-index:
- name: my_model - Val123val
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Sberdevices_golos_10h_crowd
      type: bond005/sberdevices_golos_10h_crowd
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 42.241139818232334
---

<!-- 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. -->

# my_model - Val123val

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Sberdevices_golos_10h_crowd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1761
- Wer: 42.2411

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1521        | 0.91  | 500  | 0.1824          | 29.3408 |
| 0.0824        | 1.82  | 1000 | 0.1702          | 27.5291 |
| 0.0304        | 2.73  | 1500 | 0.1726          | 45.1046 |
| 0.0114        | 3.64  | 2000 | 0.1704          | 40.1238 |
| 0.0039        | 4.55  | 2500 | 0.1692          | 32.1903 |
| 0.0013        | 5.45  | 3000 | 0.1704          | 34.0298 |
| 0.0029        | 6.36  | 3500 | 0.1712          | 39.8976 |
| 0.0007        | 7.27  | 4000 | 0.1738          | 39.4273 |
| 0.0006        | 8.18  | 4500 | 0.1755          | 41.0664 |
| 0.0005        | 9.09  | 5000 | 0.1761          | 42.2411 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.0
- Tokenizers 0.15.0