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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base |
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tags: |
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- audio-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-base_down_on |
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results: [] |
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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-base_down_on |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the MatsRooth/down_on dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1385 |
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- Accuracy: 0.9962 |
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## Model description |
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Binary classifier using facebook/wav2vec2/base for the words "down" and "on". |
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## Intended uses & limitations |
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This is a demo of binary audio classification that illustrates data layout, training and evaluation using python and slurm. |
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## Training and evaluation data |
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The data are utterances of "down" and "on" in `superb ks`. See `down_on_copy.py` for the subsetting. This puts wav files in locations |
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like `down_on/data/train/on` and `down_on/data/train/down` |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 0 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6089 | 1.0 | 29 | 0.1385 | 0.9962 | |
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| 0.1289 | 2.0 | 58 | 0.0510 | 0.9962 | |
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| 0.0835 | 3.0 | 87 | 0.0433 | 0.9885 | |
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| 0.0605 | 4.0 | 116 | 0.0330 | 0.9923 | |
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| 0.0479 | 5.0 | 145 | 0.0273 | 0.9904 | |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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