swecha_gonthuka_finetuned_asr

Gonthuka Telugu ASR Fine-Tuned Model

Overview

This repository contains a fine-tuned Wav2Vec2 Automatic Speech Recognition (ASR) model for the Telugu language.

The model has been fine-tuned on a custom Telugu speech dataset collected as part of the Gonthuka Project. It converts spoken Telugu audio into Telugu text and can be used for transcription, subtitle generation, accessibility applications, and speech-enabled systems.

---

Base Model

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

---

Fine-Tuning Dataset

The model was fine-tuned using a custom Telugu speech dataset.

Dataset Details

---

Training Details

---

Evaluation

The model was evaluated using standard Automatic Speech Recognition metrics.

---

Usage

Install

pip install transformers torchaudio librosa

Load the Model

from transformers import Wav2Vec2Processor
from transformers import Wav2Vec2ForCTC

processor = Wav2Vec2Processor.from_pretrained(
    "suryateja2109/swecha_gonthuka_finetuned_asr"
)

model = Wav2Vec2ForCTC.from_pretrained(
    "suryateja2109/swecha_gonthuka_finetuned_asr"
)

Inference

import torch
import librosa

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

inputs = processor(
    speech,
    sampling_rate=16000,
    return_tensors="pt",
    padding=True
)

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

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

transcription = processor.batch_decode(predicted_ids)

print(transcription[0])

---

Applications

• Telugu Speech Recognition
• Voice Assistants
• Speech-to-Text Systems
• Subtitle Generation
• Audio Transcription
• Accessibility Applications
• Educational Tools

---

Limitations

• Best suited for Telugu speech.
• Performance may decrease with noisy recordings.
• Mixed-language speech may reduce transcription accuracy.
• Larger and more diverse datasets can further improve performance.

---

Future Work

• Increase dataset size
• Improve Word Error Rate (WER)
• Support noisy environments
• Add multilingual support
• Optimize for real-time inference

---

Citation

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

@misc{gonthuka_asr,
  author = {Surya Teja},
  title = {Gonthuka Telugu ASR Fine-Tuned Model},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/suryateja2109/swecha_gonthuka_finetuned_asr}
}

---

License

This project is released under the Apache-2.0 License.

---

Author

Surya Teja

B.Tech Data Science

Fine-tuned as part of the Gonthuka Telugu ASR Project.