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whisper-large-test-music – AI Model by VMadalina | AlphaNeural AI
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whisper-large-test-music
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
ro
openai/whisper-large
finetune
apache-2.0
endpoints_compatible
us
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Whisper Large Ro - VM3
This model is a fine-tuned version of
openai/whisper-large
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1271
Wer: 19.1998
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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Use 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
training_steps: 500
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0554
0.1206
100
0.1900
26.7148
0.0588
0.2413
200
0.1866
42.6189
0.156
0.3619
300
0.1515
22.3330
0.1327
0.4825
400
0.1349
18.0598
0.1226
0.6031
500
0.1271
19.1998
Framework versions
Transformers 4.50.1
Pytorch 2.6.0+cu124
Datasets 2.13.1
Tokenizers 0.21.1
