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whispertest – AI Model by guqun | AlphaNeural AI
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guqun
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whispertest
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
zh
apache-2.0
endpoints_compatible
us
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whispertest
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: 0.2991
Wer: 167.4731
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: 500
training_steps: 4000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.2588
0.4
1000
0.2991
167.4731
0.275
0.81
2000
0.2991
167.4731
0.2793
1.21
3000
0.2991
167.4731
0.2511
1.61
4000
0.2991
167.4731
Framework versions
Transformers 4.30.0.dev0
Pytorch 2.0.0+cu117
Datasets 2.12.0
Tokenizers 0.13.3
