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--- |
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library_name: transformers |
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language: |
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- dk |
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license: apache-2.0 |
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base_model: openai/whisper-large |
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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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- alexandrainst/ftspeech |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large FTSpeech - Your Name |
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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: ftspeech |
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type: alexandrainst/ftspeech |
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args: 'split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 24.476331512025737 |
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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 Large FTSpeech - Your Name |
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This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the ftspeech dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3820 |
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- Wer: 24.4763 |
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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: 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: Use OptimizerNames.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: 200 |
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- training_steps: 1000 |
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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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| 0.5793 | 0.0032 | 200 | 0.5536 | 30.4519 | |
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| 0.4187 | 0.0064 | 400 | 0.4508 | 27.5208 | |
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| 0.3587 | 0.0096 | 600 | 0.4125 | 25.5569 | |
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| 0.3477 | 0.0129 | 800 | 0.3907 | 24.9318 | |
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| 0.3786 | 0.0161 | 1000 | 0.3820 | 24.4763 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.21.0 |
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