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whisper-base-speech – AI Model by jayavardhan31 | AlphaNeural AI
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whisper-base-speech
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
te
google/fleurs
openai/whisper-base
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper base te - jayavardhan
This model is a fine-tuned version of
openai/whisper-base
on the fleurs dataset. It achieves the following results on the evaluation set:
Loss: 0.1934
Wer: 70.4367
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: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 250
training_steps: 1500
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.084
6.12
500
0.1455
71.1065
0.0297
12.23
1000
0.1682
69.8570
0.0175
18.35
1500
0.1934
70.4367
Framework versions
Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2
