Swecha_Gonthuka_ASR_Finetuned

Swecha Gonthuka ASR Finetuned

Model Details

Model Description

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

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Model Sources

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

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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

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Downstream Use

Possible downstream applications include:

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

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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

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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.

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How to Get Started

from transformers import AutoProcessor, AutoModelForCTC

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

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

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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

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Training Procedure

Preprocessing

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

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Training Hyperparameters

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Evaluation

Metrics

Word Error Rate (WER)

Results

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Model Architecture

• Architecture: Wav2Vec2ForCTC
• Framework: Transformers
• Backend: PyTorch

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Hardware

Training performed using:

• Google Colab
• NVIDIA Tesla T4 GPU

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Software

• Python
• PyTorch
• Hugging Face Transformers
• Datasets
• Gradio

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More Information

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

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Author

Venigalla Shamanth Chowdary

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

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