Quantized IndicConformer ASR models for multiple Indian languages
Project description
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)
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