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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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- multilingual_librispeech
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metrics:
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- wer
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model-index:
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- name: openai/whisper-large-v2
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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: multilingual_librispeech
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type: multilingual_librispeech
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config: french
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split: test
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args: french
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metrics:
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- name: Wer
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type: wer
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value: 4.561620226935377
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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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# openai/whisper-large-v2
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the multilingual_librispeech dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0903
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- Wer: 4.5616
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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: 8
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- total_train_batch_size: 64
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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: 500
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- training_steps: 4000
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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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| 0.1303 | 0.25 | 1000 | 0.1219 | 6.3618 |
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| 0.0751 | 0.5 | 2000 | 0.1033 | 5.3905 |
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| 0.0613 | 0.75 | 3000 | 0.0970 | 4.9193 |
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| 0.0796 | 1.0 | 4000 | 0.0903 | 4.5616 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.0+cu117
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- Datasets 2.7.1.dev0
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- Tokenizers 0.13.2
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