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End of training
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README.md
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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: facebook/wav2vec2-large-960h-lv60-self
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- wer
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model-index:
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- name: teste_finetunning
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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: audiofolder
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type: audiofolder
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config: default
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split: train
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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: 0.64
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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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# teste_finetunning
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This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2220
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- Wer: 0.64
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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: 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: 500
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- training_steps: 2000
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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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| 0.4245 | 1000.0 | 1000 | 0.4122 | 0.8133 |
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| 0.2904 | 2000.0 | 2000 | 0.2220 | 0.64 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.5.0+cu121
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- Datasets 3.1.0
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- Tokenizers 0.19.1
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