re-skill

whisper-large-v3-turbo-tj

Deploy Dedicated

README

License: mit

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: 5e-05
train_batch_size: 128
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.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
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
0.1505	1.0	704	0.3244	23.0135
0.1011	2.0	1408	0.2744	18.8362
0.0635	3.0	2112	0.2646	17.0636
0.0328	4.0	2816	0.2667	16.4316

Framework versions

Transformers 4.52.4
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.4

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

re-skill

Model Tree

Base

openai/whisper-large-v3-turbo

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

whisper-large-v3-turbo-tj

README

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Explore FriendliAI today

README

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions