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--- |
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license: apache-2.0 |
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base_model: openai/whisper-medium |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_16_0 |
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language: |
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- hu |
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widget: |
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- example_title: Sample 1 |
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src: https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample1.flac |
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- example_title: Sample 2 |
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src: https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample2.flac |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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model-index: |
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- name: Whisper Medium Hungarian |
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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: Common Voice 16.0 - Hungarian |
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type: mozilla-foundation/common_voice_16_0 |
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config: hu |
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split: test |
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args: hu |
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metrics: |
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- name: Wer |
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type: wer |
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value: 5.55 |
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verified: true |
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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 medium Hu |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 16.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0875 |
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- Wer Ortho: 6.6934 |
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- Wer: 5.5500 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 6.25e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 500 |
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- training_steps: 15000 |
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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 | Wer Ortho | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:| |
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| 0.1877 | 0.33 | 1000 | 0.2104 | 17.8832 | 20.5799 | |
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| 0.136 | 0.67 | 2000 | 0.1561 | 13.4717 | 16.2140 | |
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| 0.1117 | 1.0 | 3000 | 0.1245 | 13.4198 | 10.9487 | |
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| 0.0673 | 1.34 | 4000 | 0.1148 | 12.0107 | 9.7836 | |
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| 0.0657 | 1.67 | 5000 | 0.1006 | 10.3547 | 8.4702 | |
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| 0.0264 | 2.01 | 6000 | 0.0905 | 9.0931 | 7.2250 | |
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| 0.0284 | 2.34 | 7000 | 0.0916 | 8.7137 | 7.2221 | |
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| 0.0311 | 2.68 | 8000 | 0.0879 | 8.0242 | 6.6914 | |
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| 0.0177 | 3.01 | 9000 | 0.0841 | 7.6960 | 6.3860 | |
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| 0.0177 | 3.35 | 10000 | 0.0844 | 7.2173 | 6.0125 | |
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| 0.0126 | 3.68 | 11000 | 0.0848 | 7.2052 | 5.9739 | |
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| 0.0078 | 4.02 | 12000 | 0.0865 | 7.1179 | 6.0629 | |
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| 0.0113 | 4.35 | 13000 | 0.0863 | 6.9312 | 5.7990 | |
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| 0.0115 | 4.69 | 14000 | 0.0853 | 7.0185 | 5.8968 | |
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| 0.0071 | 5.02 | 15000 | 0.0875 | 6.6934 | 5.5500 | |
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### Framework versions |
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- Transformers 4.36.2 |
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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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