indic-asr-onnx 0.1.1

Quantized IndicConformer ASR models for multiple Indian languages

Indic ASR Quantized

A helper package to use Quantized Indic ASR (Automatic Speech Recognition) for multiple Indic languages.

The original model was developed by AI4Bharat and can be found here

Installation

CPU-only Installation (Recommended for limited resources)

pip install uv
uv pip install indic-asr-onnx --extra-index-url https://download.pytorch.org/whl/cpu

GPU Installation

pip install uv
uv pip install indic-asr-onnx --extra-index-url https://download.pytorch.org/whl/cu113

Quick Start

from indic_asr_onnx import IndicTranscriber

# Initialize (downloads model automatically)
transcriber = IndicTranscriber()

# Transcribe audio using CTC head
text = transcriber.transcribe_ctc("audio.wav", "hi")  # Hindi
print(text)

# Transcribe audio using RNN-T head
text = transcriber.transcribe_rnnt("audio.wav", "hi")  # Hindi
print(text)

Supported Languages

• Assamese (as)
• Bengali (bn)
• Bodo (brx)
• Dogri (doi)
• Gujarati (gu)
• Hindi (hi)
• Kannada (kn)
• Kashmiri (ks)
• Konkani (kok)
• Maithili (mai)
• Malayalam (ml)
• Manipuri (mni)
• Marathi (mr)
• Nepali (ne)
• Odia (or)
• Punjabi (pa)
• Sanskrit (sa)
• Santali (sat)
• Sindhi (sd)
• Tamil (ta)
• Telugu (te)
• Urdu (ur)

Features

• Quantized Models: INT8 quantization for efficient CPU inference
• Multiple Languages: Support for 22 Indic languages
• Two Modes: CTC and RNN-T decoding
• Auto Download: Models download automatically on first use
• ONNX Runtime: Optimized inference with ONNX

Audio Requirements

• Format: WAV, MP3, FLAC, etc.
• Sample Rate: Auto-resampled to 16kHz
• Channels: Mono (auto-converted)