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vanilla-whisper-medium_evaluated_on_condenser – AI Model by jethrowang | AlphaNeural AI

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jethrowang/
vanilla-whisper-medium_evaluated_on_condenser

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formospeech/hat_asr_aligned

openai/whisper-medium

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Whisper Medium Hakka Condenser

This model is a fine-tuned version of openai/whisper-medium on the HAT ASR Aligned dataset. It achieves the following results on the evaluation set:

eval_loss: 0.0185
eval_cer: 0.5953
eval_runtime: 2397.8299
eval_samples_per_second: 1.901
eval_steps_per_second: 0.119
step: 0

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: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1521
training_steps: 15215
mixed_precision_training: Native AMP

Framework versions

Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1