Parameter	Value
Base Model	swechatelangana/swecha-gonthuka-asr
Epochs	10
Learning Rate	1e-5
Batch Size	4
Gradient Accumulation	2
Optimizer	AdamW
Framework	Hugging Face Transformers
GPU	NVIDIA Tesla T4

Evaluation

The model was evaluated using Character Error Rate (CER).

Metric	Value
CER	0.14
Character Accuracy	86%

Character Accuracy = (1 − CER) × 100

Usage

Install dependencies

pip install transformers torch librosa

Load the model

python

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="Pravalika2711/gonthuka-asr-finetuned",
    feature_extractor="Pravalika2711/gonthuka-asr-finetuned",
    tokenizer="Pravalika2711/gonthuka-asr-finetuned"
)

Run inference

python

result = pipe("audio.wav")

print(result["text"])

Applications

Telugu Speech Recognition
Voice Assistants
Speech-to-Text Systems
Accessibility Applications
Telugu Language Research
Educational Applications

Limitations

Performance may decrease for noisy environments.
Works best with 16 kHz mono WAV audio.
Accuracy depends on speaker accent and recording quality.

Acknowledgements

This work is built upon the excellent Swecha Gonthuka ASR model.

Base Model:

https://huggingface.co/swechatelangana/swecha-gonthuka-asr

Citation

If you use this model, please cite both the original Swecha Gonthuka ASR model and this fine-tuned model.

bibtex

@misc{gonthuka_finetuned,
  title={Fine-Tuned Telugu ASR using Swecha Gonthuka ASR},
  author={Pravalika Moraboyina},
  year={2026},
  publisher={Hugging Face}
}