80 lines
2.2 KiB
Markdown
80 lines
2.2 KiB
Markdown
---
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license: apache-2.0
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base_model: openai/whisper-medium
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tags:
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- generated_from_trainer
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datasets:
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- yashtiwari/PaulMooney-Medical-ASR-Data
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metrics:
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- wer
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model-index:
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- name: Whisper Medium Medical
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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: Medical ASR
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type: yashtiwari/PaulMooney-Medical-ASR-Data
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metrics:
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- name: Wer
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type: wer
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value: 16.051170649287954
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jutsu-labs/huggingface/runs/nnp3wvhl)
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# Whisper Medium Medical
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Medical ASR dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0567
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- Wer: 16.0512
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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: 32
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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: 50
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- training_steps: 500
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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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| 0.4817 | 0.5405 | 100 | 0.1982 | 12.8651 |
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| 0.104 | 1.0811 | 200 | 0.0839 | 10.3065 |
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| 0.0549 | 1.6216 | 300 | 0.0643 | 15.9063 |
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| 0.0245 | 2.1622 | 400 | 0.0610 | 14.0961 |
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| 0.012 | 2.7027 | 500 | 0.0567 | 16.0512 |
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### Framework versions
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- Transformers 4.42.4
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- Pytorch 2.2.0+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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