Swecha Gonthuka ASR (Telugu)
Telugu automatic speech recognition model (wav2vec2-based), trained on the Swecha Gonthuka dataset. It is evaluated on Telugu-only test sets with Character Error Rate (CER).
Model details
- Training data: Swecha Gonthuka dataset
- Language: Telugu (te)
- Metric: CER (Character Error Rate) — text normalized to Telugu script + spaces before scoring.
Evaluation results
| Dataset | Test samples | CER (%) |
|---|---|---|
| FLEURS (te_in) | 304 | 6.32 |
| OpenSLR66 | 420 | 9.00 |
| Common Voice 22 (te) | 58 | 11.92 |
Note: For evaluation we used only those samples that contain no English words-Telugu text only-for each dataset, to allow a fair evaluation of model capability.
Usage
Python (Transformers)
pip install transformers torch librosa
from transformers import pipeline
pipe = pipeline(
"automatic-speech-recognition",
model="viswamaicoe/swecha-gonthuka-asr",
feature_extractor="viswamaicoe/swecha-gonthuka-asr",
)
# From file (16 kHz mono WAV preferred)
text = pipe("audio.wav")
print(text) # {"text": "..."}
Responsible and ethical use
- Intended use: This model is intended for Telugu automatic speech recognition in applications such as transcription, accessibility, and language preservation. Use it in accordance with applicable laws and platform policies.
- Limitations: Performance may vary with accent, dialect, noise, and recording quality. Do not rely on it as the sole source for critical or legal transcriptions without human review.
- Misuse: Do not use this model to transcribe private conversations without consent, to create misleading or harmful content, or for any purpose that violates privacy, consent, or local regulations.
- Bias and fairness: As with any ASR system, outputs can reflect biases present in training data. Evaluate outputs in context and consider human review for high-stakes use cases.
Citation
If you use this model in your work, please cite the Swecha Gonthuka dataset and this model:
@misc{swecha-gonthuka-asr,
title = {Swecha Gonthuka ASR: Telugu Speech Recognition},
author = {Viswam AI COE},
year = {2025},
howpublished = {\url{https://huggingface.co/viswamaicoe/swecha-gonthuka-asr}},
note = {Trained on Swecha Gonthuka dataset; wav2vec2-based Telugu ASR}
}