best_checkpoint2

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Finetuning Whisper on Tunisian custom dataset

Model Details

Model Description

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This model is a fine-tuned version of openai/whisper-small on the tunisian_custom dataset (2h and 30mn). It achieves the following results on the evaluation set:

• Train Loss: 0.0029

• Evaluation Loss: 1.1970311403274536

• Wer: 51.80948419301165

• Cer: 25.522807875749052

• Developed by: [Ameni Khabthani]

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• Model type: [ASR system]

• Language(s) (NLP): [More Information Needed]

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• Finetuned from model [optional]: [whisper small]

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

per_device_train_batch_size=8
gradient_accumulation_steps=8
learning_rate= 5e-5
warmup_steps=100
max_steps=3000
gradient_checkpointing=True
fp16=True
save_steps=500
eval_steps=500
per_device_eval_batch_size=8
predict_with_generate=True
generation_max_length=225
logging_steps=50,
optim="adamw_bnb_8bit"
weight_decay=0.01 dropout=0.1

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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Glossary [optional]

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