Qipchoq-STT

Qipchoq-STT is a fine-tuned automatic speech recognition (ASR) model based on xmullaboyeva/mullaboyeva-stt. The model converts speech audio into text and is intended for research and practical speech recognition applications.

Model description

This model was fine-tuned from xmullaboyeva/mullaboyeva-stt using a custom speech dataset. It is designed for speech-to-text transcription and can be integrated into Python applications using the Hugging Face Transformers library.

Usage

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="xmullaboyeva/Qipchoq-STT"
)

result = pipe("audio.wav")
print(result["text"])

Intended uses

The model can be used for:

• Automatic speech recognition (ASR)
• Speech-to-text transcription
• Speech technology research
• Voice-enabled applications
• Dataset transcription and annotation

Limitations

Recognition accuracy may decrease for:

• Noisy audio recordings
• Speech with accents or dialects not represented in the training data
• Overlapping speakers
• Extremely short audio clips

Training and evaluation data

The model was fine-tuned on a custom speech dataset.

Training procedure

Training hyperparameters

Hyperparameter	Value
Learning rate	1e-5
Train batch size	8
Evaluation batch size	2
Gradient accumulation steps	4
Total train batch size	32
Optimizer	Adam (β₁=0.9, β₂=0.999, ε=1e-8)
Learning rate scheduler	Cosine
Warmup ratio	0.05
Number of epochs	5
Random seed	42

Training results

Training Loss	Epoch	Step	Validation Loss
0.2231	1.97	200	0.1962
0.1199	3.93	400	0.2005

Final validation loss: 0.2005

Framework versions

• Transformers 4.38.0
• PyTorch 2.9.1+cu126
• Datasets 2.18.0
• Tokenizers 0.15.2