whisper-medium-mupe-1 – AI Model by lejonck | AlphaNeural AI

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whisper-medium-mupe-1

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automatic-speech-recognition

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openai/whisper-medium

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whisper-small-mupe-1

This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9038
Wer: 0.3488
Cer: 0.5846

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: 8
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.4507	1.0	500	0.7181	0.3314	0.5808
0.2228	2.0	1000	0.7651	0.3602	0.5947
0.0408	3.0	1500	0.8454	0.3473	0.5875
0.0258	4.0	2000	0.8905	0.3284	0.5842
0.009	5.0	2500	0.8925	0.3359	0.6071
0.0097	6.0	3000	0.9411	0.3296	0.5859
0.002	7.0	3500	0.9892	0.3302	0.5882

Framework versions

Transformers 4.55.2
Pytorch 2.7.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4