swecha-gonthuka-asr

Telugu Automatic Speech Recognition (ASR)

Model Overview

This model is a fine-tuned Automatic Speech Recognition (ASR) model for the Telugu language. It is based on the swechatelangana/swecha-gonthuka-asr model and fine-tuned on a custom Telugu speech dataset.

The model converts spoken Telugu audio into Telugu text.

---

Model Details

• Developed by: Rahul Karnati
• Language: Telugu (te)
• Task: Automatic Speech Recognition (ASR)
• Base Model: swechatelangana/swecha-gonthuka-asr
• Framework: Hugging Face Transformers
• Fine-tuned using: PyTorch + Hugging Face Trainer

---

Dataset

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

Dataset statistics:

• Total recordings: 1226
• Language: Telugu
• Audio Format: MP3
• Sampling Rate: 16 kHz
• Training Split: 1103
• Validation Split: 123

The dataset contains Telugu speech recordings with corresponding Telugu transcripts.

---

Training Configuration

---

Evaluation Results

Compared to the previous version of the model:

The additional training data improved the overall recognition performance.

---

Usage

from transformers import AutoProcessor, AutoModelForCTC
import librosa
import torch

processor = AutoProcessor.from_pretrained("YOUR_USERNAME/YOUR_MODEL_NAME")
model = AutoModelForCTC.from_pretrained("YOUR_USERNAME/YOUR_MODEL_NAME")

speech, sr = librosa.load("sample.mp3", sr=16000)

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

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

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

prediction = processor.batch_decode(predicted_ids)[0]

print(prediction)

---

Applications

• Telugu Speech-to-Text
• Voice Assistants
• Accessibility Applications
• Educational Tools
• Voice Search
• Telugu Voice Interfaces

---

Limitations

The model may produce recognition errors for:

• Noisy recordings
• Heavy regional accents
• Rare words
• Extremely fast speech

Additional diverse training data can further improve performance.

---

Future Work

• Increase dataset size
• Support multiple Telugu dialects
• Improve recognition in noisy environments
• Reduce Word Error Rate
• Deploy as a web application using FastAPI and Streamlit

---

Acknowledgements

• Swecha Foundation
• Hugging Face Transformers
• PyTorch
• Google Colab

---

Author

Rahul Karnati

Data Science Undergraduate

Specialization: Artificial Intelligence and Machine Learning