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--- |
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license: apache-2.0 |
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base_model: openai/whisper-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- razhan/common_voice_ckb_16 |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-base-ckb |
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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: razhan/common_voice_ckb_16 |
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type: razhan/common_voice_ckb_16 |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.12623194275685162 |
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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-ckb |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the razhan/common_voice_ckb_16 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0641 |
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- Wer: 0.1262 |
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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: 1e-05 |
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- train_batch_size: 192 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 6 |
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- total_train_batch_size: 1152 |
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- total_eval_batch_size: 768 |
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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: 200 |
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- training_steps: 2300 |
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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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| 0.3434 | 1.09 | 100 | 0.3840 | 0.6054 | |
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| 0.2089 | 2.17 | 200 | 0.2654 | 0.4740 | |
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| 0.167 | 3.26 | 300 | 0.2246 | 0.4190 | |
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| 0.1452 | 4.35 | 400 | 0.1964 | 0.3803 | |
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| 0.1287 | 5.43 | 500 | 0.1788 | 0.3542 | |
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| 0.1163 | 6.52 | 600 | 0.1650 | 0.3326 | |
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| 0.1068 | 7.61 | 700 | 0.1560 | 0.3155 | |
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| 0.1015 | 8.7 | 800 | 0.1489 | 0.3059 | |
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| 0.0968 | 9.78 | 900 | 0.1440 | 0.2954 | |
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| 0.0939 | 10.87 | 1000 | 0.1420 | 0.2918 | |
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| 0.0919 | 11.96 | 1100 | 0.1315 | 0.2742 | |
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| 0.0839 | 13.04 | 1200 | 0.1217 | 0.2597 | |
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| 0.0713 | 14.13 | 1300 | 0.1132 | 0.2371 | |
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| 0.0687 | 15.22 | 1400 | 0.1091 | 0.2372 | |
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| 0.0647 | 16.3 | 1500 | 0.1022 | 0.2173 | |
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| 0.059 | 17.39 | 1600 | 0.0967 | 0.2043 | |
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| 0.0539 | 18.48 | 1700 | 0.0897 | 0.1929 | |
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| 0.0518 | 19.57 | 1800 | 0.0827 | 0.1718 | |
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| 0.0495 | 20.65 | 1900 | 0.0787 | 0.1667 | |
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| 0.0444 | 21.74 | 2000 | 0.0718 | 0.1469 | |
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| 0.0392 | 22.83 | 2100 | 0.0671 | 0.1368 | |
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| 0.0335 | 23.91 | 2200 | 0.0645 | 0.1263 | |
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| 0.0292 | 25.0 | 2300 | 0.0641 | 0.1262 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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