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--- |
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language: |
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- mn |
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license: apache-2.0 |
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base_model: openai/whisper-tiny |
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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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- audiofolder |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small MN with custom data - Zagi |
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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: audiofolder |
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type: audiofolder |
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config: default |
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split: None |
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args: 'config: mn, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 9.378407851690294 |
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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 MN with custom data - Zagi |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0917 |
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- Wer: 9.3784 |
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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: 500 |
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- training_steps: 4000 |
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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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| 0.0653 | 0.61 | 500 | 0.1102 | 13.5820 | |
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| 0.054 | 1.21 | 1000 | 0.1002 | 11.9380 | |
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| 0.0523 | 1.82 | 1500 | 0.0966 | 11.5903 | |
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| 0.0366 | 2.43 | 2000 | 0.0954 | 10.9710 | |
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| 0.0168 | 3.03 | 2500 | 0.0909 | 10.3866 | |
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| 0.0204 | 3.64 | 3000 | 0.0912 | 9.7817 | |
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| 0.0067 | 4.25 | 3500 | 0.0910 | 9.4936 | |
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| 0.0078 | 4.85 | 4000 | 0.0917 | 9.3784 | |
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### Framework versions |
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- Transformers 4.39.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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