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--- |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small English |
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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: google/fleurs en_us |
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type: google/fleurs |
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config: en_us |
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split: test |
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args: en_us |
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metrics: |
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- name: Wer |
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type: wer |
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value: 7.990755655157924 |
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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 English |
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This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the google/fleurs en_us dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6007 |
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- Wer: 7.9908 |
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## Model description |
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This model was created as part of the Whisper Fine-Tune Event. This is my first attempt at fine-tuning the Whisper neural network. |
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Honestly, it's my second time ever trying anything related to training a neural network, and my first time was pretty bad (but I did |
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get a lot of rather funny images out of it, so perhaps it wasn't entirely fruitless?), and it seems like the WER only went up after step 2000, |
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so... I'm not sure if I did a good job or if I just wasted GPU cycles, but maybe I can try again and get a better score? |
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I'm learning. |
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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: 64 |
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- eval_batch_size: 32 |
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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: 5000 |
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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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| 0.0005 | 24.0 | 1000 | 0.5092 | 7.5566 | |
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| 0.0002 | 48.01 | 2000 | 0.5528 | 7.7526 | |
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| 0.0001 | 73.0 | 3000 | 0.5785 | 7.8507 | |
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| 0.0001 | 97.0 | 4000 | 0.5936 | 7.9908 | |
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| 0.0001 | 121.01 | 5000 | 0.6007 | 7.9908 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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