---
language:
- ru
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: whisper-small-fine_tuned-ru
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: mozilla-foundation/common_voice_13_0
      args: 'config: ru, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 17.724332
---

<!-- 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-fine_tuned-ru

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [common_voice_13_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.22031
- Wer: 17.724332

## Model description

Same as original model (see [whisper-small](https://huggingface.co/openai/whisper-small)). ***But! This model has been fine-tuned for the task of transcribing the Russian language.***

## Intended uses & limitations

Same as original model (see [whisper-small](https://huggingface.co/openai/whisper-small)).

## Training and evaluation data

More information needed

## Training procedure

The model is fine-tuned using the following notebook (available only in the Russian version): https://github.com/blademoon/Whisper_Train

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Pytorch Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 50000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.344         | 0.22  | 500   | 0.3936          | 58.4474 |
| 0.1948        | 0.44  | 1000  | 0.2391          | 57.0232 |
| 0.1853        | 0.66  | 1500  | 0.2255          | 66.1826 |
| 0.186         | 0.88  | 2000  | 0.2180          | 65.3833 |
| 0.1532        | 1.1   | 2500  | 0.2135          | 50.6050 |
| 0.1374        | 1.32  | 3000  | 0.2107          | 47.9428 |
| 0.1359        | 1.54  | 3500  | 0.2082          | 60.0693 |
| 0.1387        | 1.76  | 4000  | 0.2052          | 58.8674 |
| 0.1212        | 1.97  | 4500  | 0.2027          | 51.9571 |
| 0.111         | 2.19  | 5000  | 0.2027          | 50.0780 |
| 0.1108        | 2.41  | 5500  | 0.2013          | 42.9664 |
| 0.1148        | 2.63  | 6000  | 0.2000          | 40.7882 |
| 0.114         | 2.85  | 6500  | 0.2002          | 32.6050 |
| 0.092         | 3.07  | 7000  | 0.2000          | 32.9307 |
| 0.0783        | 3.29  | 7500  | 0.2001          | 33.1413 |
| 0.0989        | 3.51  | 8000  | 0.1986          | 32.0313 |
| 0.0919        | 3.73  | 8500  | 0.1991          | 28.7199 |
| 0.0928        | 3.95  | 9000  | 0.1982          | 26.1798 |
| 0.0721        | 4.17  | 9500  | 0.2007          | 22.4960 |
| 0.078         | 4.39  | 10000 | 0.2012          | 26.0774 |
| 0.0764        | 4.61  | 10500 | 0.2004          | 24.7906 |
| 0.0812        | 4.83  | 11000 | 0.2003          | 24.8022 |
| 0.0531        | 5.05  | 11500 | 0.2022          | 21.3837 |
| 0.0587        | 5.27  | 12000 | 0.2038          | 21.1638 |
| 0.0553        | 5.48  | 12500 | 0.2039          | 21.9224 |
| 0.0537        | 5.7   | 13000 | 0.2042          | 20.9671 |
| 0.0608        | 5.92  | 13500 | 0.2049          | 21.1068 |
| 0.0467        | 6.14  | 14000 | 0.2073          | 18.6528 |
| 0.0533        | 6.36  | 14500 | 0.2088          | 18.7843 |
| 0.048         | 6.58  | 15000 | 0.2092          | 18.5609 |
| 0.0479        | 6.8   | 15500 | 0.2101          | 19.1648 |
| 0.0383        | 7.02  | 16000 | 0.2105          | 18.9379 |
| 0.0384        | 7.24  | 16500 | 0.2147          | 18.8018 |
| 0.0451        | 7.46  | 17000 | 0.2156          | 18.9170 |
| 0.0399        | 7.68  | 17500 | 0.2163          | 18.3806 |
| 0.0387        | 7.9   | 18000 | 0.2159          | 17.9605 |
| ***0.0347***        | ***8.12***  | ***18500*** | ***0.2203***          | ***17.7243*** |
| 0.0324        | 8.34  | 19000 | 0.2231          | 17.8163 |
| 0.035         | 8.56  | 19500 | 0.2231          | 17.8954 |
| 0.0338        | 8.78  | 20000 | 0.2234          | 17.7371 |
| 0.0305        | 9.0   | 20500 | 0.2244          | 17.8035 |
| 0.0244        | 9.21  | 21000 | 0.2305          | 17.8942 |
| 0.0249        | 9.43  | 21500 | 0.2321          | 17.9024 |
| 0.0242        | 9.65  | 22000 | 0.2328          | 18.2212 |
| 0.0269        | 9.87  | 22500 | 0.2327          | 17.8104 |
| 0.0198        | 10.09 | 23000 | 0.2380          | 17.7301 |
| 0.0191        | 10.31 | 23500 | 0.2396          | 17.8861 |
| 0.0218        | 10.53 | 24000 | 0.2412          | 17.7464 |
| 0.0219        | 10.75 | 24500 | 0.2406          | 17.7453 |
| 0.0206        | 10.97 | 25000 | 0.2427          | 17.9128 |
| 0.0182        | 11.19 | 25500 | 0.2482          | 18.0676 |
| 0.0143        | 11.41 | 26000 | 0.2506          | 17.9245 |
| 0.0162        | 11.63 | 26500 | 0.2501          | 18.1572 |
| 0.0172        | 11.85 | 27000 | 0.2535          | 18.1164 |
| 0.0148        | 12.07 | 27500 | 0.2558          | 18.1130 |
| 0.0123        | 12.29 | 28000 | 0.2573          | 18.4085 |
| 0.0129        | 12.51 | 28500 | 0.2603          | 18.0978 |
| 0.0136        | 12.72 | 29000 | 0.2615          | 18.1793 |
| 0.011         | 12.94 | 29500 | 0.2617          | 18.2247 |
| 0.0096        | 13.16 | 30000 | 0.2666          | 18.2712 |
| 0.01          | 13.38 | 30500 | 0.2667          | 18.4457 |
| 0.0122        | 13.6  | 31000 | 0.2690          | 18.1095 |
| 0.0121        | 13.82 | 31500 | 0.2700          | 18.1653 |
| 0.0088        | 14.04 | 32000 | 0.2720          | 18.4539 |
| 0.0076        | 14.26 | 32500 | 0.2746          | 18.2956 |
| 0.0086        | 14.48 | 33000 | 0.2764          | 18.5644 |
| 0.0086        | 14.7  | 33500 | 0.2771          | 18.5260 |
| 0.0085        | 14.92 | 34000 | 0.2788          | 18.4481 |
| 0.008         | 15.14 | 34500 | 0.2803          | 18.4923 |
| 0.0074        | 15.36 | 35000 | 0.2824          | 18.6028 |
| 0.0069        | 15.58 | 35500 | 0.2838          | 18.7692 |
| 0.008         | 15.8  | 36000 | 0.2848          | 18.6901 |
| 0.0065        | 16.02 | 36500 | 0.2864          | 18.7413 |
| 0.006         | 16.24 | 37000 | 0.2885          | 18.5458 |
| 0.0061        | 16.45 | 37500 | 0.2885          | 18.6470 |
| 0.0056        | 16.67 | 38000 | 0.2898          | 18.3736 |
| 0.0061        | 16.89 | 38500 | 0.2912          | 18.8064 |
| 0.0048        | 17.11 | 39000 | 0.2933          | 18.9018 |
| 0.0053        | 17.33 | 39500 | 0.2939          | 18.6168 |
| 0.006         | 17.55 | 40000 | 0.2954          | 18.7238 |
| 0.0045        | 17.77 | 40500 | 0.2952          | 18.8099 |
| 0.0059        | 17.99 | 41000 | 0.2964          | 18.5551 |
| 0.0053        | 18.21 | 41500 | 0.2980          | 18.7157 |
| 0.004         | 18.43 | 42000 | 0.2988          | 18.6412 |
| 0.0049        | 18.65 | 42500 | 0.2990          | 18.7099 |
| 0.0048        | 18.87 | 43000 | 0.3004          | 18.7552 |
| 0.0041        | 19.09 | 43500 | 0.3015          | 18.8169 |
| 0.0048        | 19.31 | 44000 | 0.3018          | 18.8518 |
| 0.0039        | 19.53 | 44500 | 0.3022          | 18.9437 |
| 0.0041        | 19.75 | 45000 | 0.3029          | 18.8239 |
| 0.0041        | 19.96 | 45500 | 0.3036          | 18.8169 |
| 0.004         | 20.18 | 46000 | 0.3045          | 18.8274 |
| 0.0044        | 20.4  | 46500 | 0.3048          | 18.8867 |
| 0.0042        | 20.62 | 47000 | 0.3054          | 18.8425 |
| 0.0044        | 20.84 | 47500 | 0.3058          | 18.8448 |
| 0.004         | 21.06 | 48000 | 0.3057          | 18.8425 |
| 0.0038        | 21.28 | 48500 | 0.3062          | 18.7029 |
| 0.0038        | 21.5  | 49000 | 0.3063          | 18.8413 |
| 0.0046        | 21.72 | 49500 | 0.3063          | 18.8227 |
| 0.0036        | 21.94 | 50000 | 0.3064          | 18.8483 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3