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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-large-v3
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tags:
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- generated_from_trainer
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datasets:
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- fsicoli/common_voice_18_0
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metrics:
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- wer
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model-index:
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- name: whisper-large-v3-pt-3000h-3
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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/common_voice_18_0 pt
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type: fsicoli/common_voice_18_0
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config: pt
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split: None
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args: pt
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metrics:
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- name: Wer
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type: wer
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value: 0.10736707238949392
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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-v3-pt-3000h-3
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the fsicoli/common_voice_18_0 pt dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1501
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- Wer: 0.1074
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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: 4
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- total_train_batch_size: 32
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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: 1000
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- num_epochs: 10.0
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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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| 0.1388 | 0.9996 | 691 | 0.1501 | 0.1074 |
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| 0.108 | 1.9993 | 1382 | 0.1619 | 0.1153 |
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| 0.091 | 2.9989 | 2073 | 0.1697 | 0.1124 |
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| 0.0461 | 4.0 | 2765 | 0.1764 | 0.1120 |
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| 0.0264 | 4.9996 | 3456 | 0.2024 | 0.1133 |
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| 0.0203 | 5.9993 | 4147 | 0.2200 | 0.1099 |
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| 0.0129 | 6.9989 | 4838 | 0.2277 | 0.1114 |
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| 0.0091 | 8.0 | 5530 | 0.2552 | 0.1067 |
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| 0.0063 | 8.9996 | 6221 | 0.2565 | 0.1054 |
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| 0.0019 | 9.9964 | 6910 | 0.2671 | 0.1042 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.4.0+cu124
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- Datasets 2.18.1.dev0
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- Tokenizers 0.19.1
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