This model is a fine-tuned version of the Swecha Gonthuka ASR model for Telugu Automatic Speech Recognition (ASR). It has been fine-tuned on a custom Telugu speech dataset containing approximately 5,000 audio-transcription pairs.

Developed by: Venigalla Shamanth Chowdary
Base model: swechatelangana/swecha-gonthuka-asr
Model type: Wav2Vec2ForCTC
Language: Telugu (te)
License: Apache-2.0 (same as the base model, if applicable)
Framework: Hugging Face Transformers + PyTorch

Model Sources

Base Model: https://huggingface.co/swechatelangana/swecha-gonthuka-asr
Fine-tuned Model Repository: https://huggingface.co/ShamanthChowdary/Swecha_Gonthuka_ASR_Finetuned

Uses

Direct Use

This model is intended for:

Telugu Speech-to-Text
Voice assistants
Automatic transcription
Telugu speech datasets
Research in Indian language ASR
Educational and accessibility applications

Downstream Use

Possible downstream applications include:

Subtitle generation
Meeting transcription
Voice search
Speech analytics
Telugu conversational AI
Voice-enabled applications

Out-of-Scope Use

This model is not intended for:

Languages other than Telugu
Speaker identification
Emotion recognition
Medical or legal transcription where high accuracy is required
Safety-critical applications

Bias, Risks and Limitations

Performance depends on:

Audio quality
Background noise
Speaker accent
Recording device
Speech speed

The model may perform less accurately on unseen accents or noisy recordings.

How to Get Started

python

from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "ShamanthChowdary/Swecha_Gonthuka_ASR_Finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "ShamanthChowdary/Swecha_Gonthuka_ASR_Finetuned"
)

Training Details

Training Data

The model was fine-tuned using a custom Telugu speech dataset containing approximately:

5000 audio samples
WAV format
Sampling rate: 16 kHz
Human-annotated Telugu transcriptions

Training Procedure

Preprocessing

Audio resampled to 16 kHz
Audio-token alignment using Wav2Vec2 Processor
Text tokenization using the processor tokenizer

Training Hyperparameters

Parameter	Value
Epochs	10
Learning Rate	1e-5
Batch Size	4
Gradient Accumulation	2
Warmup Steps	500
Optimizer	AdamW
Mixed Precision	FP16

Evaluation

Metrics

Word Error Rate (WER)

Results

Metric	Value
Validation Loss	0.2603
Training Loss	0.5500
Word Error Rate (WER)	0.4595

Model Architecture

Architecture: Wav2Vec2ForCTC
Framework: Transformers
Backend: PyTorch

Hardware

Training performed using:

Google Colab
NVIDIA Tesla T4 GPU

Software

Python
PyTorch
Hugging Face Transformers
Datasets
Gradio

More Information

This model was developed as part of a Telugu Automatic Speech Recognition fine-tuning project using the Swecha Gonthuka ASR base model.

Author

Venigalla Shamanth Chowdary

GitHub: https://github.com/shamanth-25

LinkedIn: https://www.linkedin.com/in/shamanthchowdary/