EYEDOL

whisper-tiny-hausa

Deploy Dedicated

README

License: apache-2.0

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: 32
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 0.1
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer Ortho, Wer
Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
1.4906	1.0	665	0.7049	0.5690	0.5170
1.3724	2.0	1330	0.6859	0.5655	0.5104
1.2209	3.0	1995

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.3
Tokenizers 0.22.2

Available on FriendliAI

Dedicated Endpoints

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

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

EYEDOL

Model Tree

Base

EYEDOL/whisper-tiny-hausa

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

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: 32
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 0.1
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer Ortho, Wer
Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
1.4906	1.0	665	0.7049	0.5690	0.5170
1.3724	2.0	1330	0.6859	0.5655	0.5104
1.2209	3.0	1995

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.3
Tokenizers 0.22.2