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--- |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: whisper-small-uz-en-ru-lang-id |
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results: [] |
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datasets: |
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- mozilla-foundation/common_voice_16_1 |
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language: |
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- uz |
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- en |
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- ru |
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pipeline_tag: audio-classification |
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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-small-uz-en-ru-lang-id |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the "mozilla-foundation/common_voice_16_1"(uz/en/ru) dataset. |
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It achieves the following results on the validation set during training: |
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- Loss: 0.2065 |
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- Accuracy: 0.9747 |
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- F1: 0.9746 |
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Accuracy on the test (evaluation) dataset: 92.4%. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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```python |
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# datasets for each language from the set {uz: Uzbek, en: English, ru: Russian} |
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common_voice_train_uz = load_dataset("mozilla-foundation/common_voice_16_1", "uz", split='train', trust_remote_code=True, token=env('HUGGING_TOKEN'), streaming=True) |
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common_voice_train_ru = load_dataset("mozilla-foundation/common_voice_16_1", "ru", split='train', trust_remote_code=True, token=env('HUGGING_TOKEN'), streaming=True) |
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common_voice_train_en = load_dataset("mozilla-foundation/common_voice_16_1", "en", split='train', trust_remote_code=True, token=env('HUGGING_TOKEN'), streaming=True) |
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common_voice_valid_uz = load_dataset("mozilla-foundation/common_voice_16_1", "uz", split='validation', trust_remote_code=True, token=env('HUGGING_TOKEN'), streaming=True) |
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common_voice_valid_ru = load_dataset("mozilla-foundation/common_voice_16_1", "ru", split='validation', trust_remote_code=True, token=env('HUGGING_TOKEN'), streaming=True) |
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common_voice_valid_en = load_dataset("mozilla-foundation/common_voice_16_1", "en", split='validation', trust_remote_code=True, token=env('HUGGING_TOKEN'), streaming=True) |
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# code to shuffle and to take limited size of data. Rows per set: Train-24000, Validation-3000. |
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... |
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# concatenate 3 datasets |
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common_voice['train'] = concatenate_datasets([common_voice_train_uz, common_voice_train_ru, common_voice_train_en]) |
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``` |
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## Training procedure |
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Used Trainer from transformers. |
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Training and evaluation process are described in the Jupyter notebook, storing in the following github repository: |
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https://github.com/fitlemon/whisper-small-uz-en-ru-lang-id |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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_ratio: 0.1 |
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- training_steps: 9000 |
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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 | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.0252 | 1 | 3000 | 0.3089 | 0.953 | 0.9525 | |
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| 0.0357 | 2 | 6000 | 0.1732 | 0.964 | 0.9637 | |
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| 0.0 | 3 | 9000 | 0.2065 | 0.9747 | 0.9746 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
