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whisper-medium-cs – AI Model by sgangireddy | AlphaNeural AI
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whisper-medium-cs
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
whisper-event
mozilla-foundation/common_voice_11_0
apache-2.0
model-index
endpoints_compatible
us
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openai/whisper-medium
This model is a fine-tuned version of
openai/whisper-medium
on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
Loss: 0.1805
Wer: 11.8358
Model description
The Model is fine-tuned for 1000 steps/updates on CV11 Czech train+valiation data.
Zero-shot - 18.80 (CV9 test data, even on CV11 the WER is closer OR a bit higher than this)
Fine-tuned - 11.83 (CV11 test data)
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: 64
eval_batch_size: 32
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: 1000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0076
4.06
1000
0.1805
11.8358
Framework versions
Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2
