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--- |
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language: |
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- en |
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license: mit |
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base_model: distil-whisper/distil-small.en |
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tags: |
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- generated_from_trainer |
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datasets: |
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- PolyAI/minds14 |
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metrics: |
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- wer |
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model-index: |
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- name: Distil Whisper Small finetuned on PolyAI Minds14 English US. |
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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: Speech Transcription in English from e-banking domain. |
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type: PolyAI/minds14 |
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config: en-US |
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split: train |
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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: 0.3318442884492661 |
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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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# Distil Whisper Small finetuned on PolyAI Minds14 English US. |
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This model is a fine-tuned version of [distil-whisper/distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) on the Speech Transcription in English from e-banking domain. dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0182 |
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- Wer Ortho: 0.3371 |
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- Wer: 0.3318 |
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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: 16 |
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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: constant_with_warmup |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 400 |
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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 Ortho | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| |
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| 0.2325 | 3.57 | 100 | 0.6222 | 0.3557 | 0.3472 | |
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| 0.0196 | 7.14 | 200 | 0.8475 | 0.3757 | 0.3689 | |
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| 0.0014 | 10.71 | 300 | 0.9729 | 0.3630 | 0.3555 | |
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| 0.0006 | 14.29 | 400 | 1.0182 | 0.3371 | 0.3318 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.0 |
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