cvas-544/autotinglishsub-whisper-telugu

AutoTinglishSub – Whisper Telugu Tinglish Fine-Tune

Overview

cvas-544/autotinglishsub-whisper-telugu is a fine-tuned Automatic Speech Recognition (ASR) model optimized for Tinglish — Telugu and Indian English code-mixed speech.

This model is designed specifically for subtitle generation workflows in short-form content such as Reels, TikTok, and YouTube Shorts.

It improves transcription quality for:

• —Telugu conversational speech
• —Indian English accents
• —Telugu-English code-switching
• —Fast-paced creator-style delivery
• —Informal spoken content

Model Lineage

This model is fine-tuned from:

• —vasista22/whisper-telugu-large-v2 (Apache 2.0)

Which itself is fine-tuned from:

• —openai/whisper-large-v2 (MIT License)

This model continues that lineage with further optimization for Tinglish subtitle workflows.

Intended Use

Primary use cases:

• —Subtitle generation for short-form video
• —Creator-focused ASR pipelines
• —Local CLI subtitle tools
• —Tinglish speech recognition
• —Code-switched Telugu + English audio

The model is optimized for conversational and creator-style speech rather than formal Telugu literature.

Training Summary

• —Fine-tuning performed on Tinglish-focused dataset
• —Total training chunks used: 120
• —Objective: Improve transcription accuracy for Telugu + Indian English mixed speech
• —Focus on subtitle-ready clean outputs

Evaluation Metrics

Word Error Rate (WER)

Details:

• —Measured after first fine-tuning phase
• —Evaluated on held-out validation split
• —Based on 120 training chunks
• —Metric computed using standard WER calculation (substitutions + insertions + deletions / total words)

This represents a significant improvement in handling Tinglish code-mixed speech compared to the base Telugu fine-tuned model in informal creator-style audio.

Usage

Using Transformers Pipeline

import torch
from transformers import pipeline

audio = "/path/to/audio.wav"
device = "cuda:0" if torch.cuda.is_available() else "cpu"

transcribe = pipeline(
    task="automatic-speech-recognition",
    model="cvas-544/autotinglishsub-whisper-telugu",
    chunk_length_s=30,
    device=device
)

print("Transcription:", transcribe(audio)["text"])