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End of training

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  1. README.md +13 -10
  2. model.safetensors +1 -1
README.md CHANGED
@@ -11,7 +11,7 @@ datasets:
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  metrics:
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  - wer
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  model-index:
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- - name: Whisper Small acholi
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  results:
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  - task:
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  name: Automatic Speech Recognition
@@ -22,18 +22,18 @@ model-index:
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  metrics:
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  - name: Wer
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  type: wer
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- value: 121.88961646398504
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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 acholi
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  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Sunbird dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.8028
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- - Wer: 121.8896
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  ## Model description
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@@ -52,26 +52,29 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-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: 1000
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- - training_steps: 1000
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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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- | 2.8441 | 3.32 | 1000 | 2.8028 | 121.8896 |
 
 
 
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  ### Framework versions
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  - Transformers 4.38.0.dev0
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  - Pytorch 2.1.0+cu121
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- - Datasets 2.16.1
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- - Tokenizers 0.15.0
 
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  metrics:
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  - wer
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  model-index:
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+ - name: Whisper base acholi
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  results:
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  - task:
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  name: Automatic Speech Recognition
 
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 122.26379794200186
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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 base acholi
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  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Sunbird dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.8895
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+ - Wer: 122.2638
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  ## Model description
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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: 1000
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+ - training_steps: 4000
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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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+ | 3.2321 | 3.32 | 1000 | 2.9610 | 140.3181 |
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+ | 2.5056 | 6.64 | 2000 | 2.7358 | 116.9317 |
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+ | 2.0671 | 9.97 | 3000 | 2.7957 | 144.9953 |
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+ | 1.7382 | 13.29 | 4000 | 2.8895 | 122.2638 |
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  ### Framework versions
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  - Transformers 4.38.0.dev0
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  - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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