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whisper-tuned – AI Model by SamuelHarner | AlphaNeural AI
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SamuelHarner
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whisper-tuned
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
sv
mozilla-foundation/common_voice_11_0
openai/whisper-small
finetune
apache-2.0
endpoints_compatible
us
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Whisper Small sv-SE augmented data tuned model
This model is a fine-tuned version of
openai/whisper-small
on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
eval_loss: 0.4671
eval_wer: 31.0882
eval_runtime: 2727.495
eval_samples_per_second: 1.858
eval_steps_per_second: 0.232
epoch: 1.3
step: 1000
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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 1600
mixed_precision_training: Native AMP
Framework versions
Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0
