whisper-small-qve-es – AI Model by cportoca | AlphaNeural AI

You can deploy this model and start earning money today!

cportoca/
whisper-small-qve-es

like

0

automatic-speech-recognition

generated_from_trainer

cportoca/Quechua_dataset

openai/whisper-large

endpoints_compatible

Views

No views yet

Whisper Large Ja-Qve - cportoca

This model is a fine-tuned version of openai/whisper-large on the Quechua_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.2409
Wer: 17.7910

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: 16
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2791	1.3550	1000	0.3439	32.1619
0.137	2.7100	2000	0.2366	26.9532
0.0305	4.0650	3000	0.2266	21.3367
0.0142	5.4201	4000	0.2322	18.5441
0.0048	6.7751	5000	0.2285	18.4500
0.0014	8.1301	6000	0.2378	18.1362
0.0007	9.4851	7000	0.2394	17.6969
0.0004	10.8401	8000	0.2409	17.7910

Framework versions

Transformers 4.46.3
Pytorch 2.4.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3