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whisper-ft-test – AI Model by rishabhjain16 | AlphaNeural AI
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whisper-ft-test
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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openai/whisper-small
This model is a fine-tuned version of
openai/whisper-small
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.5636
Wer: 13.4646
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: 64
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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
0.1789
4.02
1000
0.3421
13.1199
0.0264
8.04
2000
0.4579
13.5155
0.0023
13.01
3000
0.5479
13.6539
0.0011
17.03
4000
0.5636
13.4646
Framework versions
Transformers 4.27.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.8.1.dev0
Tokenizers 0.12.1
