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--- |
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language: |
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- eu |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Basque |
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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: mozilla-foundation/common_voice_13_0 eu |
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type: mozilla-foundation/common_voice_13_0 |
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config: eu |
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split: test |
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args: eu |
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metrics: |
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- name: Wer |
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type: wer |
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value: 13.179958686054519 |
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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 Basque |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_13_0 eu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2201 |
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- Wer: 13.1800 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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If you need to use this model with [whisper.cpp](https://github.com/ggerganov/whisper.cpp), you can download the ggml file: [ggml-medium-eu.bin](https://huggingface.co/xezpeleta/whisper-medium-eu/blob/main/ggml-medium.eu.bin) |
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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: 4 |
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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: 500 |
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- training_steps: 7000 |
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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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| 0.4203 | 0.14 | 1000 | 0.4128 | 28.2656 | |
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| 0.2693 | 0.29 | 2000 | 0.3240 | 22.0523 | |
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| 0.2228 | 0.43 | 3000 | 0.2737 | 18.1437 | |
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| 0.1002 | 1.1 | 4000 | 0.2554 | 16.3534 | |
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| 0.0863 | 1.24 | 5000 | 0.2351 | 14.7880 | |
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| 0.0636 | 1.39 | 6000 | 0.2251 | 13.5971 | |
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| 0.0271 | 2.06 | 7000 | 0.2201 | 13.1800 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.1.dev0 |
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- Tokenizers 0.13.2 |
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