whisper-medium-sr-v3 – AI Model by Sagicc | AlphaNeural AI

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Sagicc/
whisper-medium-sr-v3

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automatic-speech-recognition

generated_from_trainer

mozilla-foundation/common_voice_16_0

classla/ParlaSpeech-RS

Sagicc/audio-lmb-ds

openai/whisper-medium

endpoints_compatible

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Whisper Medium v3

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

Loss: 0.1501
Wer Ortho: 0.1759
Wer: 0.0791

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: 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: 50
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.2054	0.03	500	0.2392	0.2715	0.1484
0.1782	0.05	1000	0.2056	0.2411	0.1155
0.1736	0.08	1500	0.1768	0.1990	0.0994
0.1662	0.11	2000	0.1677	0.1925	0.0940
0.1409	0.13	2500	0.1589	0.1891	0.0860
0.1346	0.16	3000	0.1565	0.1897	0.0881
0.1263	0.19	3500	0.1523	0.1805	0.0819
0.137	0.22	4000	0.1501	0.1759	0.0791

Framework versions

Transformers 4.37.2
Pytorch 2.0.1+cu117
Datasets 2.16.1
Tokenizers 0.15.1