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whisper – AI Model by hyojin99 | AlphaNeural AI

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hyojin99/
whisper

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automatic-speech-recognition

hf-asr-leaderboard

generated_from_trainer

openai/whisper-base

endpoints_compatible

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ft_model

This model is a fine-tuned version of openai/whisper-base on the EBRC dataset. It achieves the following results on the evaluation set:

eval_loss: 0.2518
eval_cer: 16.5091
eval_runtime: 1356.705
eval_samples_per_second: 3.685
eval_steps_per_second: 0.461
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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 4000
mixed_precision_training: Native AMP

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2