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sunilmahendrakar/indic-conformer-600m-multilingual

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IndicConformer

AI4Bharat's IndicConformers is a suite of ASR models built to deliver accurate speech-to-text conversion in all 22 official Indian languages. By leveraging cutting-edge deep learning techniques, these models provide precise transcriptions. As the country's first open-source ASR system covering such a vast array of languages, AI4Bharat Indic Conformer is a transformative tool for making technology more inclusive and accessible to all. IndicConformer is released under the MIT license.

Model Details

  • —Model Name: IndicConformer-600M-Multi
  • —Repository: ai4bharat/indic-conformer-600m-multilingual
  • —Architecture: Multilingual Conformer-based Hybrid CTC + RNNT ASR model
  • —Parameter Size: 600M
  • —Languages Supported: IN-22

Model Usage

This model can be used to transcribe speech in various Indian languages. It supports two decoding strategies:

  • —CTC (Connectionist Temporal Classification)
  • —RNNT (Recurrent Neural Network Transducer)

Installation

Ensure that you have transformers and torchaudio installed:

bash
pip install transformers torchaudio onnx onnxruntime onnxruntime-gpu

Inference Example

python
from transformers import AutoModel
import torch, torchaudio

# Load the model
model = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)

# Load an audio file
wav, sr = torchaudio.load("audio.flac")
wav = torch.mean(wav, dim=0, keepdim=True)

target_sample_rate = 16000  # Expected sample rate
if sr != target_sample_rate:
    resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate)
    wav = resampler(wav)

# Perform ASR with CTC decoding
transcription_ctc = model(wav, "hi", "ctc")
print("CTC Transcription:", transcription_ctc)

# Perform ASR with RNNT decoding
transcription_rnnt = model(wav, "hi", "rnnt")
print("RNNT Transcription:", transcription_rnnt)

Supported Languages

IndicConformer-600M-Multi is trained for 22 officially recognized languages of India, including:

  • —Assamese(as)
  • —Bengali(bn)
  • —Bodo(brx)
  • —Dogri(doi)
  • —Gujarati(gu)
  • —Hindi(hi)
  • —Kannada(kn)
  • —Konkani(kok)
  • —Kashmiri(ks)
  • —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)

The tokenizers used for these languages can be found here