metadata
base_model: openai/whisper-small
language:
- ar
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Tunisian Checkpoint12
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: custom_tunisian_dataset
type: dataset
args: 'config: ar, split: test'
metrics:
- type: wer
value: 54.52300785634119
name: Wer
- type: cer
value: 25.538666370797735
name: Cer
Model Card for Model ID
Model Details
Model Description
Model Card for Model ID
Finetuning Whisper on Tunisian custom dataset
Model Details
Model Description
This model is a fine-tuned version of openai/whisper-small on the tunisian_custom dataset =more than 4h(/doumawl4+/doumaw02+Data3+dataset1+dataset2). It achieves the following results on the evaluation set:
- Train Loss: 0.0109
- Evaluation Loss: 1.1608097553253174
- Wer: 54.52300785634119
- Cer: 25.538666370797735 -max_audio_length=15 for the preprocessing i used padding+VAD filter
- Developed by: [Ameni Khabthani]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [ASR system]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [whisper small]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Training Hyperparameters
per_device_train_batch_size=4
gradient_accumulation_steps=8
learning_rate= 5e-5
warmup_steps=100
max_steps=4000
gradient_checkpointing=True
fp16=True
save_steps=500
eval_steps=500
per_device_eval_batch_size=8
predict_with_generate=True
generation_max_length=249
logging_steps=50
weight_decay=0.001
dropout=0.1
optim="adamw_bnb_8bit"
seed=42
save_total_limit=5
save_steps=500,
eval_steps=500,