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--- |
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language: |
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- ru |
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license: apache-2.0 |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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base_model: openai/whisper-base |
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model-index: |
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- name: whisper-base-fine_tuned-ru |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: common_voice_11_0 |
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type: mozilla-foundation/common_voice_11_0 |
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args: 'config: ru, split: test' |
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metrics: |
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- type: wer |
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value: 41.216909250757055 |
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name: Wer |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# whisper-base-fine_tuned-ru |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4553 |
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- Wer: 41.2169 |
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## Model description |
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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.*** |
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## Intended uses & limitations |
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Same as original model (see [whisper-base](https://huggingface.co/openai/whisper-base)). |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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The model is fine-tuned using the following notebook (available only in the Russian version): https://github.com/blademoon/Whisper_Train |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 250 |
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- training_steps: 20000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:| |
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| 0.702 | 0.25 | 500 | 0.8245 | 71.6653 | |
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| 0.5699 | 0.49 | 1000 | 0.6640 | 55.7048 | |
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| 0.5261 | 0.74 | 1500 | 0.6127 | 50.6215 | |
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| 0.4997 | 0.98 | 2000 | 0.5834 | 47.4541 | |
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| 0.4681 | 1.23 | 2500 | 0.5638 | 46.6262 | |
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| 0.4651 | 1.48 | 3000 | 0.5497 | 47.5090 | |
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| 0.4637 | 1.72 | 3500 | 0.5379 | 46.5700 | |
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| 0.4185 | 1.97 | 4000 | 0.5274 | 45.3160 | |
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| 0.3856 | 2.22 | 4500 | 0.5205 | 45.5871 | |
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| 0.4078 | 2.46 | 5000 | 0.5122 | 45.7190 | |
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| 0.4132 | 2.71 | 5500 | 0.5066 | 45.5004 | |
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| 0.3914 | 2.96 | 6000 | 0.4998 | 47.0011 | |
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| 0.3822 | 3.2 | 6500 | 0.4959 | 44.9570 | |
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| 0.3596 | 3.45 | 7000 | 0.4916 | 45.5578 | |
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| 0.3877 | 3.69 | 7500 | 0.4870 | 45.2476 | |
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| 0.3687 | 3.94 | 8000 | 0.4832 | 45.2159 | |
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| 0.3514 | 4.19 | 8500 | 0.4809 | 46.0254 | |
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| 0.3202 | 4.43 | 9000 | 0.4779 | 48.1306 | |
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| 0.3229 | 4.68 | 9500 | 0.4751 | 45.5724 | |
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| 0.3285 | 4.93 | 10000 | 0.4717 | 45.9436 | |
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| 0.3286 | 5.17 | 10500 | 0.4705 | 45.0510 | |
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| 0.3294 | 5.42 | 11000 | 0.4689 | 47.2111 | |
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| 0.3384 | 5.66 | 11500 | 0.4666 | 47.3393 | |
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| 0.316 | 5.91 | 12000 | 0.4650 | 43.2536 | |
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| 0.2988 | 6.16 | 12500 | 0.4638 | 42.9789 | |
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| 0.3046 | 6.4 | 13000 | 0.4629 | 42.4331 | |
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| 0.2962 | 6.65 | 13500 | 0.4614 | 40.2437 | |
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| 0.3008 | 6.9 | 14000 | 0.4602 | 39.5734 | |
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| 0.2749 | 7.14 | 14500 | 0.4593 | 40.1497 | |
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| 0.3001 | 7.39 | 15000 | 0.4588 | 42.6248 | |
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| 0.3054 | 7.64 | 15500 | 0.4580 | 40.3707 | |
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| 0.2926 | 7.88 | 16000 | 0.4574 | 39.4232 | |
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| 0.2938 | 8.13 | 16500 | 0.4569 | 40.9532 | |
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| 0.3105 | 8.37 | 17000 | 0.4566 | 40.4379 | |
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| 0.2799 | 8.62 | 17500 | 0.4562 | 40.3622 | |
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| 0.2793 | 8.87 | 18000 | 0.4557 | 41.3451 | |
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| 0.2819 | 9.11 | 18500 | 0.4555 | 41.4184 | |
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| 0.2907 | 9.36 | 19000 | 0.4555 | 39.9348 | |
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| 0.3113 | 9.61 | 19500 | 0.4553 | 41.0289 | |
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| 0.2867 | 9.85 | 20000 | 0.4553 | 41.2169 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.1 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.1 |
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