# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ManiKumarAdapala/indic-transcribe-core-8bit", trust_remote_code=True, device_map="auto")Indic-Transcribe-Core 8-bit
An 8-bit quantized version of Bodhan AI's Indic-Transcribe-Core, created using BitsAndBytes quantization.
The model retains the capabilities of the original multilingual ASR model while significantly reducing GPU memory requirements, making it more suitable for resource-constrained inference and serving multiple users.
Key Features
- 🎙️ Multilingual Automatic Speech Recognition
- 🇮🇳 Supports Indian languages
- ⚡ 8-bit quantization using BitsAndBytes
- 💾 Reduced GPU VRAM usage
- 🔌 Designed for efficient inference and deployment
VRAM Usage
| Model | Approx. GPU VRAM |
|---|---|
Original indic-transcribe-core |
~5 GB |
| This 8-bit model | ~2.5 GB |
| Reduction | ~50% |
Actual memory usage may vary depending on GPU, batch size, audio duration, and inference configuration.
Base Model
This model is derived from:
bodhan-ai/indic-transcribe-core
Please refer to the original model card for the architecture, training details, supported languages, benchmarks, limitations, and licensing information.
Quantization
The model was quantized to 8-bit using BitsAndBytes.
The primary goal of this quantized release is to reduce GPU memory consumption while maintaining the capabilities of the original model.
Inference
For a complete inference example, see the Inference Notebook.
Intended Use
Suitable for:
- Indian-language speech-to-text
- Voice applications
- Multilingual transcription
- Low-VRAM GPU deployment
- Multi-user ASR serving
- On-premise and edge-oriented inference
Attribution
This is a quantized derivative of Bodhan AI's Indic-Transcribe-Core. Please retain the original model's attribution and comply with its license and usage requirements.
Disclaimer
This repository contains an 8-bit quantized version of the original model. Performance, accuracy, memory usage, and compatibility may vary depending on the inference environment.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ManiKumarAdapala/indic-transcribe-core-8bit", trust_remote_code=True)