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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-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- balbus-classifier |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: miosipof/whisper-small-ft-balbus-sep28k-v1.5 |
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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: Apple dataset |
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type: balbus-classifier |
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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: 0.8111877154497023 |
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- name: Precision |
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type: precision |
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value: |
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precision: 0.8133174791914387 |
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- name: Recall |
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type: recall |
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value: |
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recall: 0.7365398420674802 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.7730269353927294 |
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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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# miosipof/whisper-small-ft-balbus-sep28k-v1.5 |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Apple dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1083 |
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- Accuracy: {'accuracy': 0.8111877154497023} |
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- Precision: {'precision': 0.8133174791914387} |
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- Recall: {'recall': 0.7365398420674802} |
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- F1: {'f1': 0.7730269353927294} |
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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: 3e-06 |
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- train_batch_size: 16 |
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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: 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_ratio: 0.5 |
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- training_steps: 1000 |
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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 | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:---------------------------------:|:-------------------------------:|:----------------------------:| |
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| 0.1718 | 0.1253 | 100 | 0.1705 | {'accuracy': 0.564243183954873} | {'precision': 0.6190476190476191} | {'recall': 0.00466618808327351} | {'f1': 0.009262557890986818} | |
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| 0.1683 | 0.2506 | 200 | 0.1653 | {'accuracy': 0.6118771544970228} | {'precision': 0.7677642980935875} | {'recall': 0.15900933237616655} | {'f1': 0.26345524829021705} | |
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| 0.1595 | 0.3759 | 300 | 0.1494 | {'accuracy': 0.6847383265434033} | {'precision': 0.6486175115207373} | {'recall': 0.6062455132806892} | {'f1': 0.6267161410018552} | |
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| 0.1299 | 0.5013 | 400 | 0.1266 | {'accuracy': 0.7608900031338138} | {'precision': 0.7008928571428571} | {'recall': 0.7889447236180904} | {'f1': 0.7423167848699763} | |
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| 0.1174 | 0.6266 | 500 | 0.1140 | {'accuracy': 0.7977123158884363} | {'precision': 0.7800674409891345} | {'recall': 0.747307968413496} | {'f1': 0.7633363886342804} | |
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| 0.1117 | 0.7519 | 600 | 0.1155 | {'accuracy': 0.7919147602632404} | {'precision': 0.7362281270252754} | {'recall': 0.8155061019382628} | {'f1': 0.773841961852861} | |
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| 0.1072 | 0.8772 | 700 | 0.1074 | {'accuracy': 0.8096208085239737} | {'precision': 0.8282490597576264} | {'recall': 0.7114142139267767} | {'f1': 0.765398725622707} | |
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| 0.106 | 1.0025 | 800 | 0.1078 | {'accuracy': 0.8077405202130994} | {'precision': 0.8175152749490835} | {'recall': 0.7203876525484566} | {'f1': 0.7658843732112193} | |
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| 0.1001 | 1.1278 | 900 | 0.1079 | {'accuracy': 0.810404261986838} | {'precision': 0.8174858984689767} | {'recall': 0.7282842785355348} | {'f1': 0.7703113135914958} | |
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| 0.092 | 1.2531 | 1000 | 0.1083 | {'accuracy': 0.8111877154497023} | {'precision': 0.8133174791914387} | {'recall': 0.7365398420674802} | {'f1': 0.7730269353927294} | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.2.0 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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