The model is based on Swecha Gonthuka ASR and has been fine-tuned using a custom Telugu speech dataset consisting of paired audio recordings and transcriptions.

The model converts spoken Telugu audio into Telugu text and is intended for speech transcription, accessibility, and language technology research.

Base Model

Model: swechatelangana/swecha-gonthuka-asr
Architecture: Wav2Vec2ForCTC
Framework: Hugging Face Transformers
Language: Telugu (te)

Fine-Tuning Dataset

The model was fine-tuned on a Telugu speech corpus containing:

Telugu audio recordings
Corresponding Telugu text transcriptions
Audio sampled at 16 kHz

Intended Uses

This model can be used for:

Telugu Speech-to-Text
Automatic Speech Recognition (ASR)
Voice-enabled applications
Telugu transcription
Language technology research

Usage

python

import librosa
import torch
from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "Sujji77/swecha-gonthuka-finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "Sujji77/swecha-gonthuka-finetuned"
)

audio, sr = librosa.load("sample.wav", sr=16000)

inputs = processor(
    audio,
    sampling_rate=16000,
    return_tensors="pt"
)

with torch.no_grad():
    logits = model(**inputs).logits

pred_ids = torch.argmax(logits, dim=-1)

transcription = processor.batch_decode(pred_ids)[0]

print(transcription)

Training Details

Item	Value
Base Model	Swecha Gonthuka ASR
Architecture	Wav2Vec2ForCTC
Framework	Transformers
Language	Telugu
Task	Automatic Speech Recognition

Limitations

Performance depends on recording quality.
Background noise may reduce transcription accuracy.
Best suited for Telugu speech.

Author

Srujana Talari

Frontend Developer • AI Intern

Citation

If you use this model in your research or application, please cite this repository. v