fa-ir-stt-whisper-small-v1

whisper-small-fa

This model is a fine-tuned version of openai/whisper-small on an unknown dataset. This was an experiment; better results are likely with more data and longer training. It achieves the following results on the evaluation set:

• Loss: 0.1537
• Wer: 19.2460

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 OptimizerNames.ADAMW_TORCH_FUSED 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: 4000

Training results

Note: Early stopping at 4k steps due to rising gap (train vs val) indicating overfitting.

How to use

from transformers import pipeline

asr = pipeline(
    task="automatic-speech-recognition",
    model="kiarashQ/fa-ir-stt-whisper-small-v1",
    chunk_length_s=30,
    stride_length_s=(5, 5),
    return_timestamps=False
)
out = asr("example.wav")
print(out["text"])

Framework versions

• Transformers 4.56.2
• Pytorch 2.8.0+cu128
• Datasets 4.1.1
• Tokenizers 0.22.1