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--- |
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language: |
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- uz |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_16_1 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large v3 Turbo - Bahriddin Muminov |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 16.1 |
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type: mozilla-foundation/common_voice_16_1 |
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config: uz |
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split: test |
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args: 'config: uz, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 28.258182136033867 |
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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 Large v3 Turbo - Bahriddin Muminov |
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 16.1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2958 |
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- Wer: 28.2582 |
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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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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 1000 |
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- training_steps: 10000 |
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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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| 0.429 | 0.66 | 2000 | 0.4073 | 38.0018 | |
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| 0.2671 | 1.32 | 4000 | 0.3378 | 31.0778 | |
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| 0.2511 | 1.98 | 6000 | 0.3102 | 29.2484 | |
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| 0.1539 | 2.64 | 8000 | 0.3022 | 30.0763 | |
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| 0.111 | 3.3 | 10000 | 0.2958 | 28.2582 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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