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--- |
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library_name: transformers |
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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/DOLMA-speech |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-base-hac-telegram |
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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/DOLMA-speech gilaki |
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type: razhan/DOLMA-speech |
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args: gilaki |
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metrics: |
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- name: Wer |
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type: wer |
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value: 1.0472082810539523 |
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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-hac-telegram |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the razhan/DOLMA-speech gilaki dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6806 |
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- Wer: 1.0472 |
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- Cer: 0.5468 |
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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: 256 |
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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: 2 |
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- total_train_batch_size: 512 |
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- total_eval_batch_size: 256 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 5.0 |
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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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| No log | 1.0 | 6 | 3.5224 | 1.1311 | 0.5560 | |
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| 2.4889 | 2.0 | 12 | 3.4807 | 1.0566 | 0.5018 | |
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| 2.4889 | 3.0 | 18 | 3.2108 | 1.0561 | 0.4986 | |
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| 2.3707 | 4.0 | 24 | 2.9445 | 1.0583 | 0.5155 | |
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| 2.0528 | 5.0 | 30 | 2.6806 | 1.0472 | 0.5468 | |
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### Framework versions |
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- Transformers 4.49.0.dev0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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