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End of training

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README.md CHANGED
@@ -10,7 +10,7 @@ datasets:
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  metrics:
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  - wer
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  model-index:
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- - name: "Whisper Small Espa\xF1ol"
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  results:
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  - task:
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  name: Automatic Speech Recognition
@@ -19,23 +19,23 @@ model-index:
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  name: Common Voice 11.0
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  type: mozilla-foundation/common_voice_11_0
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  config: es
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- split: None
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  args: 'config: es, split: test'
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  metrics:
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  - name: Wer
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  type: wer
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- value: 20.74384494499738
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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 Español
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  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5638
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- - Wer: 20.7438
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  ## Model description
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@@ -66,12 +66,12 @@ The following hyperparameters were used during training:
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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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- | 0.0012 | 15.8730 | 1000 | 0.5054 | 20.3772 |
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- | 0.0003 | 31.7460 | 2000 | 0.5381 | 20.5343 |
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- | 0.0002 | 47.6190 | 3000 | 0.5559 | 20.7962 |
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- | 0.0001 | 63.4921 | 4000 | 0.5638 | 20.7438 |
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  ### Framework versions
 
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  metrics:
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  - wer
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  model-index:
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+ - name: "Whisper Small Espa\xF1ol 2.0"
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  results:
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  - task:
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  name: Automatic Speech Recognition
 
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  name: Common Voice 11.0
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  type: mozilla-foundation/common_voice_11_0
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  config: es
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+ split: test
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  args: 'config: es, split: test'
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 16.209450830140483
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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 Español 2.0
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  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3763
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+ - Wer: 16.2095
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  ## Model description
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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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+ | 0.1289 | 1.6 | 1000 | 0.3013 | 18.0128 |
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+ | 0.0243 | 3.2 | 2000 | 0.3383 | 16.2401 |
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+ | 0.0079 | 4.8 | 3000 | 0.3608 | 16.2146 |
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+ | 0.0027 | 6.4 | 4000 | 0.3763 | 16.2095 |
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  ### Framework versions
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