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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-medium
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tags:
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- generated_from_trainer
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datasets:
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- fsicoli/cv18-fleurs
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metrics:
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- wer
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model-index:
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- name: whisper-medium-pt-cv18-fleurs2-lr
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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: fsicoli/cv18-fleurs default
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type: fsicoli/cv18-fleurs
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 0.10594170403587444
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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-medium-pt-cv18-fleurs2-lr
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv18-fleurs default dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1662
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- Wer: 0.1059
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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: 6.25e-06
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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: 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: 5000
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- training_steps: 25000
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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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| 0.0876 | 2.3004 | 5000 | 0.1662 | 0.1059 |
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| 0.0371 | 4.6009 | 10000 | 0.1839 | 0.0999 |
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| 0.0246 | 6.9013 | 15000 | 0.2027 | 0.0997 |
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| 0.0072 | 9.2017 | 20000 | 0.2152 | 0.0967 |
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| 0.0074 | 11.5022 | 25000 | 0.2163 | 0.0929 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.4.1
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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