This model is a fine-tuned version of openai/whisper-small on the Hy Generated Audio Data with CV 20.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1685
Wer: 36.7015

Model description

This model is based on OpenAI's Whisper Small and fine-tuned for Armenian using exclusively real audio data. It is designed to transcribe Armenian speech into text and serves as a benchmark to evaluate how well the model learns using only real (non-synthetic) data.

Training and evaluation data

The dataset contains both real and high-quality synthetic Armenian speech clips.

Split	# Clips	Duration (hours)
`train`	9,300	13.53
`test`	5,818	9.16
`eval`	5,856	8.76

Total duration: ~31 hours
Train set duration(train+generated): ~13 hours
Test set duration(test+eval) ~18 hours

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
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1946	0.9983	581	0.2224	48.2229
0.1212	1.9966	1162	0.1735	39.2161
0.077	2.9948	1743	0.1685	36.7015

Framework versions

Transformers 4.51.3
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

ErikMkrtchyan/whisper-small-hy-cv20.0

Whisper Small Hy - Erik Mkrtchyan