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whisper-base-finetuned-common_voice

Audio Classification
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Inference Endpoints
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  1. README.md +19 -19
  2. model.safetensors +1 -1
README.md CHANGED
@@ -20,13 +20,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0319
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- - Accuracy: 0.9925
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- - F1: 0.9925
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- - Recall: 0.9925
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- - Precision: 0.9927
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- - Mcc: 0.9907
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- - Auc: 0.9999
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  ## Model description
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@@ -57,18 +57,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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- | 0.1765 | 1.0 | 200 | 0.1455 | 0.9775 | 0.9775 | 0.9775 | 0.9776 | 0.9719 | 0.9996 |
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- | 0.0067 | 2.0 | 400 | 0.0734 | 0.98 | 0.9800 | 0.9800 | 0.9804 | 0.9751 | 0.9996 |
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- | 0.0218 | 3.0 | 600 | 0.0719 | 0.9875 | 0.9875 | 0.9875 | 0.9879 | 0.9845 | 0.9999 |
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- | 0.002 | 4.0 | 800 | 0.1055 | 0.975 | 0.9752 | 0.975 | 0.9762 | 0.9690 | 0.9997 |
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- | 0.0014 | 5.0 | 1000 | 0.0312 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 0.9999 |
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- | 0.0011 | 6.0 | 1200 | 0.0305 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 0.9999 |
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- | 0.0009 | 7.0 | 1400 | 0.0310 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 0.9999 |
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- | 0.0008 | 8.0 | 1600 | 0.0315 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9999 |
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- | 0.0008 | 9.0 | 1800 | 0.0318 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9999 |
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- | 0.0007 | 10.0 | 2000 | 0.0319 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9999 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0001
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+ - Accuracy: 1.0
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+ - F1: 1.0
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+ - Recall: 1.0
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+ - Precision: 1.0
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+ - Mcc: 1.0
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+ - Auc: 1.0
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:---:|
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+ | 0.0012 | 1.0 | 200 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0874 | 2.0 | 400 | 0.1391 | 0.975 | 0.9748 | 0.975 | 0.9773 | 0.9694 | 1.0 |
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+ | 0.0003 | 3.0 | 600 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0002 | 4.0 | 800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0001 | 5.0 | 1000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0001 | 6.0 | 1200 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0001 | 7.0 | 1400 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0001 | 8.0 | 1600 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0001 | 9.0 | 1800 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0001 | 10.0 | 2000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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  ### Framework versions
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