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whisper-medium-ms – AI Model by Scrya | AlphaNeural AI
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whisper-medium-ms
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
whisper-event
generated_from_trainer
ms
google/fleurs
apache-2.0
model-index
endpoints_compatible
us
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Whisper Medium MS - FLEURS
This model is a fine-tuned version of
openai/whisper-medium
on the FLEURS dataset. It achieves the following results on the evaluation set:
eval_loss: 0.2941
eval_wer: 10.2
eval_runtime: 954.9
eval_samples_per_second: 0.784
eval_steps_per_second: 0.049
epoch: 53.2
step: 5000
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: 32
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 1
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: 500
training_steps: 5000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2
