telugu-whisper-finetuned

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

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Model Details

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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-finetuned

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Fine-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

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Training Configuration

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Usage

Install dependencies

pip install transformers torchaudio librosa

Load the model

from transformers import AutoProcessor
from transformers import AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "dinesh480/telugu-asr-finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "dinesh480/telugu-asr-finetuned"
)

Or use the pipeline

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="dinesh480/telugu-asr-finetuned"
)

result = pipe("audio.wav")

print(result["text"])

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Example

Input Audio

audio.wav

Predicted Output

ఈ రోజు వాతావరణం చాలా బాగుంది.

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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)

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Applications

This model can be used for:

• Telugu Speech Recognition
• Audio Transcription
• Educational Projects
• Research
• Voice Assistants
• Speech Analytics
• Subtitle Generation

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Limitations

Performance may decrease for:

• Heavy background noise
• Multiple speakers
• Very low-quality recordings
• Strong regional accents not present in the training data

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Citation

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

@misc{telugu_asr_finetuned,
  title={Telugu ASR Fine-Tuned Model},
  author={K. Dinesh},
  year={2026},
  publisher={Hugging Face}
}

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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.

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License

This model follows the Apache-2.0 License inherited from the base model.