Telugu ASR Fine-Tuned Model
Overview
This repository contains a fine-tuned Automatic Speech Recognition (ASR) model for the Telugu language.
The model is built upon Swecha Gonthuka ASR and further fine-tuned using a custom Telugu speech dataset to improve transcription quality on domain-specific Telugu speech.
The model converts spoken Telugu audio into Telugu Unicode text and can be used for:
Automatic Speech Recognition (ASR)
Speech-to-Text applications
Voice transcription
Voice assistants
Educational applications
Telugu speech processing research
Model Details
Property Value Model Type Wav2Vec2ForCTC Language Telugu Framework Hugging Face Transformers Base Model swechatelangana/swecha-gonthuka-asr Task Automatic Speech Recognition Fine-tuned By Dinesh
Base Model
This model has been fine-tuned from:
swechatelangana/swecha-gonthuka-asr
Original architecture:
Harveenchadha/wav2vec2-pretrained-clsril-23-10k
↓
swechatelangana/swecha-gonthuka-asr
↓
dinesh480/telugu-asr-finetunedFine-Tuning Dataset
The model was fine-tuned on a custom Telugu speech dataset.
Dataset Characteristics:
Language: Telugu
Audio Format: WAV
Sampling Rate: 16 kHz
Mono Audio
UTF-8 Telugu Transcriptions
Training Configuration
Hyperparameter Value Framework Transformers Architecture Wav2Vec2ForCTC Optimizer AdamW Learning Rate 1e-4 Epochs 10 Batch Size 8 Mixed Precision FP16 Loss CTC Loss
Usage
Install dependencies
pip install transformers torchaudio librosaLoad the model
1 from transformers import AutoProcessor
2 from transformers import AutoModelForCTC
3
4 processor = AutoProcessor . from_pretrained (
5 "dinesh480/telugu-asr-finetuned"
6 )
7
8 model = AutoModelForCTC . from_pretrained (
9 "dinesh480/telugu-asr-finetuned"
10 )
Or use the pipeline
1 from transformers import pipeline
2
3 pipe = pipeline (
4 "automatic-speech-recognition" ,
5 model = "dinesh480/telugu-asr-finetuned"
6 )
7
8 result = pipe ( "audio.wav" )
9
10 print ( result [ "text" ] )
Example
Input Audio
Predicted Output
ఈ రోజు వాతావరణం చాలా బాగుంది.Performance
The model has been fine-tuned to improve Telugu speech transcription quality over the base model on the custom training dataset.
Evaluation metrics may include:
Word Error Rate (WER)
Character Error Rate (CER)
Applications
This model can be used for:
Telugu Speech Recognition
Audio Transcription
Educational Projects
Research
Voice Assistants
Speech Analytics
Subtitle Generation
Limitations
Performance may decrease for:
Heavy background noise
Multiple speakers
Very low-quality recordings
Strong regional accents not present in the training data
Citation
If you use this model in your research, please cite:
1 @misc{telugu_asr_finetuned,
2 title={Telugu ASR Fine-Tuned Model},
3 author={K. Dinesh},
4 year={2026},
5 publisher={Hugging Face}
6 }
Acknowledgements
This project is based on:
Swecha Telangana
Hugging Face Transformers
PyTorch
Wav2Vec2
Hugging Face Hub
Special thanks to the developers of Swecha Gonthuka ASR for releasing the original Telugu ASR model.
License
This model follows the Apache-2.0 License inherited from the base model.
