The model is built on Swecha Gonthuka ASR and further fine-tuned using a custom Telugu speech dataset to improve transcription quality and recognition accuracy for Telugu speech.

This model converts spoken Telugu audio into Telugu Unicode text and can be used in a wide range of speech recognition applications.

Features

🎤 Telugu Automatic Speech Recognition (ASR)
📝 Speech-to-Text Conversion
⚡ Fine-tuned Wav2Vec2ForCTC Model
🤗 Compatible with Hugging Face Transformers
🎧 Supports 16 kHz WAV Audio
📚 Custom Telugu Speech Dataset Fine-tuning

Model Details

Property	Value
Model Name	swecha-gonthuka-finetuned
Hugging Face Repository	Vasantha23/swecha-gonthuka-finetuned
Model Type	Wav2Vec2ForCTC
Framework	Hugging Face Transformers
Base Model	swechatelangana/swecha-gonthuka-asr
Language	Telugu
Task	Automatic Speech Recognition
Fine-tuned By	Vasantha

Base Model

This model is fine-tuned from:

swechatelangana/swecha-gonthuka-asr

Model Lineage

text

Harveenchadha/wav2vec2-pretrained-clsril-23-10k
                ↓
swechatelangana/swecha-gonthuka-asr
                ↓
Vasantha23/swecha-gonthuka-finetuned

After Hugging Face processes the repository metadata, this lineage should automatically appear under the Model Tree section.

Fine-Tuning Dataset

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

Dataset Characteristics

Language: Telugu
Audio Format: WAV
Sampling Rate: 16 kHz
Mono Audio
UTF-8 Telugu Transcriptions
Task: Automatic Speech Recognition

Training Configuration

Hyperparameter	Value
Architecture	Wav2Vec2ForCTC
Framework	Hugging Face Transformers
Optimizer	AdamW
Learning Rate	1e-4
Batch Size	8
Epochs	10
Mixed Precision	FP16
Loss Function	CTC Loss

Installation

pip install transformers torchaudio librosa soundfile

Usage

Load Processor and Model

python

from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "Vasantha23/swecha-gonthuka-finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "Vasantha23/swecha-gonthuka-finetuned"
)

Speech Recognition Pipeline

python

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="Vasantha23/swecha-gonthuka-finetuned"
)

result = pipe("audio.wav")

print(result["text"])

Manual Inference

python

import librosa
import torch
from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "Vasantha23/swecha-gonthuka-finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "Vasantha23/swecha-gonthuka-finetuned"
)

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

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

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

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

transcription = processor.batch_decode(predicted_ids)[0]

print(transcription)

Example

Input

audio.wav

Output

ఈ రోజు వాతావరణం చాలా బాగుంది.

Applications

This model can be used for:

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

Performance

The model has been fine-tuned to improve Telugu speech recognition performance over the base model using a custom Telugu dataset.

Typical evaluation metrics include:

Word Error Rate (WER)
Character Error Rate (CER)

Limitations

Performance may decrease in the following situations:

Heavy background noise
Low-quality recordings
Multiple speakers
Strong regional accents
Very short audio clips

Acknowledgements

This project builds upon the excellent work of:

Swecha Telangana
Hugging Face
Hugging Face Transformers
PyTorch
Wav2Vec2
Mozilla Common Voice Community

Special thanks to the creators of Swecha Gonthuka ASR for making the base Telugu ASR model publicly available.

Citation

If you use this model in your work, please cite:

bibtex

@misc{swecha_gonthuka_finetuned,
  title={Swecha Gonthuka Fine-Tuned Telugu ASR},
  author={Vasantha},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/Vasantha23/swecha-gonthuka-finetuned}
}

License

This model is released under the AGPL-3.0 License, consistent with the licensing of the base model.

Contact

For questions, suggestions, or improvements, please open an issue in the Hugging Face repository or contact the repository maintainer.

Hugging Face Repository

https://huggingface.co/Vasantha23/swecha-gonthuka-finetuned