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README.md
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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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- librispeech_asr
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metrics:
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- wer
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model-index:
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- name: whisper-small-libirClean-vs-commonNative-en
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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: librispeech_asr
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type: librispeech_asr
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config: clean
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split: train
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args: clean
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metrics:
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- name: Wer
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type: wer
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value: 84.71153846153847
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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-small-libirClean-vs-commonNative-en
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3887
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- Wer: 84.7115
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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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- 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: 10
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- training_steps: 50
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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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| 1.2459 | 0.26 | 10 | 3.6972 | 20.6731 |
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| 0.83 | 0.53 | 20 | 2.9120 | 33.1731 |
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| 0.5312 | 0.79 | 30 | 2.4692 | 76.6346 |
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| 0.445 | 1.05 | 40 | 2.3355 | 65.8654 |
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| 0.3173 | 1.32 | 50 | 2.3887 | 84.7115 |
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### Framework versions
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- Transformers 4.25.0.dev0
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- Pytorch 1.12.1+cu113
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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