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README.md
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---
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language:
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- be
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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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metrics:
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- wer
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model-index:
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- 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:
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type:
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config: be
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split: validation
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args: be
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metrics:
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- name: Wer
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type: wer
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value: 55.
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---
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#
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 be dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Wer: 55.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 32
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- eval_batch_size: 32
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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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-
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- training_steps: 150
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- mixed_precision_training: Native AMP
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### Training results
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: whisper-tiny-be-test
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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_11_0
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type: common_voice_11_0
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config: be
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split: validation
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args: be
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metrics:
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- name: Wer
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type: wer
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value: 55.311355311355314
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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-tiny-be-test
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_11_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5342
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- Wer: 55.3114
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## Model description
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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: 32
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- eval_batch_size: 32
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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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- training_steps: 200
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- mixed_precision_training: Native AMP
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### Training results
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train.log
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{'loss': 0.3533, 'learning_rate': 8.050000000000001e-06, 'epoch': 0.2}
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{'eval_loss': 0.530021071434021, 'eval_wer': 56.59340659340659, 'eval_runtime': 18.1912, 'eval_samples_per_second': 3.518, 'eval_steps_per_second': 0.11, 'epoch': 0.2}
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{'loss': 0.2844, 'learning_rate': 7.5500000000000006e-06, 'epoch': 0.25}
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{'loss': 0.3533, 'learning_rate': 8.050000000000001e-06, 'epoch': 0.2}
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{'eval_loss': 0.530021071434021, 'eval_wer': 56.59340659340659, 'eval_runtime': 18.1912, 'eval_samples_per_second': 3.518, 'eval_steps_per_second': 0.11, 'epoch': 0.2}
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{'loss': 0.2844, 'learning_rate': 7.5500000000000006e-06, 'epoch': 0.25}
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{'eval_loss': 0.5341857671737671, 'eval_wer': 55.311355311355314, 'eval_runtime': 17.7172, 'eval_samples_per_second': 3.612, 'eval_steps_per_second': 0.113, 'epoch': 0.25}
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{'train_runtime': 406.2172, 'train_samples_per_second': 15.755, 'train_steps_per_second': 0.492, 'train_loss': 0.0719480574131012, 'epoch': 0.25}
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