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whisper-large-v3-mfm – AI Model by rse-mfm | AlphaNeural AI
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whisper-large-v3-mfm
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
fr
bastiendechamps/px-corpus
openai/whisper-large-v3
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper Large V3 - MFM
This model is a fine-tuned version of
openai/whisper-large-v3
on the PX Corpus dataset. It achieves the following results on the evaluation set:
Loss: 0.3224
Wer: 8.5976
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: 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: 500
training_steps: 4000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0035
14.0845
1000
0.2718
11.4635
0.0001
28.1690
2000
0.2975
8.5010
0.0002
42.2535
3000
0.3095
8.6137
0.0
56.3380
4000
0.3224
8.5976
Framework versions
Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0
