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README.md
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# hamsa-medium
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the nadsoft/Jordan-Audio dataset.
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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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: 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: 10
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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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| No log | 0.07 | 10 | 3.0803 | 0.4207 | 0.3471 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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# hamsa-medium
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) using [ARBML](https://github.com/ARBML/whisperar) on the [**nadsoft/Jordan-Audio dataset.**](https://huggingface.co/datasets/nadsoft/Jordan-Audio)
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## Model description
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Hamsa is a Whisper Medium model that has been fine-tuned using the ARBML method in this repository. We have also added some Jordanian data to the model to adapt it to a few shot learning tasks.
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## Intended uses & limitations
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## Training and evaluation data
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nadsoft/Jordan-Audio
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## Training procedure
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### Training hyperparameters
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