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---
language:
- ru
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-base-fine_tuned-ru
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: mozilla-foundation/common_voice_11_0
args: 'config: ru, split: test'
metrics:
- name: Wer
type: wer
value: 41.216909250757055
---
<!-- 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-base-fine_tuned-ru
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the [common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4553
- Wer: 41.2169
## Model description
Same as original model (see [whisper-base](https://huggingface.co/openai/whisper-base)). ***But! This model has been fine-tuned for the task of transcribing the Russian language.***
## Intended uses & limitations
Same as original model (see [whisper-base](https://huggingface.co/openai/whisper-base)).
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 20000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.702 | 0.25 | 500 | 0.8245 | 71.6653 |
| 0.5699 | 0.49 | 1000 | 0.6640 | 55.7048 |
| 0.5261 | 0.74 | 1500 | 0.6127 | 50.6215 |
| 0.4997 | 0.98 | 2000 | 0.5834 | 47.4541 |
| 0.4681 | 1.23 | 2500 | 0.5638 | 46.6262 |
| 0.4651 | 1.48 | 3000 | 0.5497 | 47.5090 |
| 0.4637 | 1.72 | 3500 | 0.5379 | 46.5700 |
| 0.4185 | 1.97 | 4000 | 0.5274 | 45.3160 |
| 0.3856 | 2.22 | 4500 | 0.5205 | 45.5871 |
| 0.4078 | 2.46 | 5000 | 0.5122 | 45.7190 |
| 0.4132 | 2.71 | 5500 | 0.5066 | 45.5004 |
| 0.3914 | 2.96 | 6000 | 0.4998 | 47.0011 |
| 0.3822 | 3.2 | 6500 | 0.4959 | 44.9570 |
| 0.3596 | 3.45 | 7000 | 0.4916 | 45.5578 |
| 0.3877 | 3.69 | 7500 | 0.4870 | 45.2476 |
| 0.3687 | 3.94 | 8000 | 0.4832 | 45.2159 |
| 0.3514 | 4.19 | 8500 | 0.4809 | 46.0254 |
| 0.3202 | 4.43 | 9000 | 0.4779 | 48.1306 |
| 0.3229 | 4.68 | 9500 | 0.4751 | 45.5724 |
| 0.3285 | 4.93 | 10000 | 0.4717 | 45.9436 |
| 0.3286 | 5.17 | 10500 | 0.4705 | 45.0510 |
| 0.3294 | 5.42 | 11000 | 0.4689 | 47.2111 |
| 0.3384 | 5.66 | 11500 | 0.4666 | 47.3393 |
| 0.316 | 5.91 | 12000 | 0.4650 | 43.2536 |
| 0.2988 | 6.16 | 12500 | 0.4638 | 42.9789 |
| 0.3046 | 6.4 | 13000 | 0.4629 | 42.4331 |
| 0.2962 | 6.65 | 13500 | 0.4614 | 40.2437 |
| 0.3008 | 6.9 | 14000 | 0.4602 | 39.5734 |
| 0.2749 | 7.14 | 14500 | 0.4593 | 40.1497 |
| 0.3001 | 7.39 | 15000 | 0.4588 | 42.6248 |
| 0.3054 | 7.64 | 15500 | 0.4580 | 40.3707 |
| 0.2926 | 7.88 | 16000 | 0.4574 | 39.4232 |
| 0.2938 | 8.13 | 16500 | 0.4569 | 40.9532 |
| 0.3105 | 8.37 | 17000 | 0.4566 | 40.4379 |
| 0.2799 | 8.62 | 17500 | 0.4562 | 40.3622 |
| 0.2793 | 8.87 | 18000 | 0.4557 | 41.3451 |
| 0.2819 | 9.11 | 18500 | 0.4555 | 41.4184 |
| 0.2907 | 9.36 | 19000 | 0.4555 | 39.9348 |
| 0.3113 | 9.61 | 19500 | 0.4553 | 41.0289 |
| 0.2867 | 9.85 | 20000 | 0.4553 | 41.2169 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1
- Datasets 2.7.1
- Tokenizers 0.13.1
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