Whisper Medium Malay (12/6 batch size) - Gab
This model is a fine-tuned version of openai/whisper-medium on the malay-conversational-speech-corpus dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.0942
- eval_wer: 52.0822
- eval_runtime: 338.3155
- eval_samples_per_second: 1.918
- eval_steps_per_second: 0.322
- epoch: 6.9444
- step: 750
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 12
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 1500
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for M00dler/whisper-medium-malay
Base model
openai/whisper-medium