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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: jonatasgrosman/wav2vec2-large-xlsr-53-english |
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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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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: wav2vec2-large-xlsr-53-english-finetuned-babycry-v3 |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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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: Accuracy |
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type: accuracy |
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value: |
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accuracy: 0.8152173913043478 |
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- name: F1 |
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type: f1 |
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value: 0.7322311897943244 |
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- name: Precision |
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type: precision |
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value: 0.6645793950850661 |
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- name: Recall |
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type: recall |
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value: 0.8152173913043478 |
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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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# wav2vec2-large-xlsr-53-english-finetuned-babycry-v3 |
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This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7337 |
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- Accuracy: {'accuracy': 0.8152173913043478} |
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- F1: 0.7322 |
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- Precision: 0.6646 |
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- Recall: 0.8152 |
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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: 0.001 |
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- train_batch_size: 4 |
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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: 8 |
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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_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:| |
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| 0.949 | 0.5435 | 25 | 0.7351 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.7488 | 1.0870 | 50 | 0.7795 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.6911 | 1.6304 | 75 | 0.7066 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.8113 | 2.1739 | 100 | 0.8012 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.634 | 2.7174 | 125 | 0.7801 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.6503 | 3.2609 | 150 | 0.7712 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.7523 | 3.8043 | 175 | 0.7078 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.5493 | 4.3478 | 200 | 0.7484 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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| 0.7253 | 4.8913 | 225 | 0.7341 | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646 | 0.8152 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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