bharatgenai / Shrutam-2

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Shrutam-2: LLM-Powered Multilingual Indic Speech Recognition

Shrutam-2 is a LLM based automatic speech recognition system for 12 major Indian languages. It bridges a Conformer speech encoder with a pretrained LLM decoder through a Mixture-of-Experts (MoE) projection layer, enabling high-quality, prompt-controllable transcription across diverse Indic languages.

Architecture Overview

Unlike conventional CTC/Attention ASR systems that map audio directly to text tokens, Shrutam-2 reframes speech recognition as a conditional language generation task. A speech encoder produces frame-level audio representations, which are then projected into the LLM's embedding space and fed to a frozen LLM decoder alongside a text prompt.

The key architectural contribution is the MoE Projector that bridges the encoder and the LLM:

Each expert is a two-layer MLP ( encoder_dim → 2048 → llm_dim ). Rather than routing each frame to a single expert, SMEAR computes frame-wise router probabilities, averages them at the utterance level, and produces a single merged weight matrix per utterance. This yields a smooth, fully differentiable routing mechanism with a simple MSE-based load-balancing loss.

Why LLM-Based ASR?

Traditional ASR pipelines rely on acoustic models trained exclusively on speech-text pairs. By grounding transcription in a pretrained LLM, this approach gains several advantages:

• Rich linguistic priors — The LLM's language knowledge reduces hallucinations and improves fluency, especially for low-resource languages.
• Prompt controllability — Transcription behavior can be steered through natural-language prompts without retraining.
• Unified multilingual capacity — A single model serves all 12 languages, with the MoE layer learning language-adaptive projections.

Languages Supported

Extended Capabilities

Note: The capabilities below are not fully tested and are presented as potential directions. They can be unlocked or significantly enhanced with task-specific fine-tuning.

Prompt Customisation

Because the LLM decoder conditions on both audio embeddings and a text prompt, you can control transcription behavior at inference time by changing the prompt.

Basic transcription:

"Transcribe speech to text."

Language-specific prompting:

"Transcribe the following Hindi speech to text."
"Transcribe the following Tamil speech to Devanagari text."

Domain-specific prompting:

"Transcribe the following medical conversation in Hindi."
"Transcribe the following legal proceeding in Bengali."

Few-Shot Prompting

The LLM backbone enables few-shot prompting where you provide example transcriptions in the prompt to bias the model toward a specific vocabulary, style, or domain:

"The following are examples of transcriptions from a banking domain:
 - 'मुझे अपने खाते का बैलेंस जानना है'
 - 'कृपया मेरा पिन रीसेट कर दीजिए'
Now transcribe the following speech to text."

This is particularly useful for:

• Domain adaptation — Bias the decoder toward domain-specific terminology (medical, legal, financial) without retraining.
• Named entity handling — Provide example transcriptions containing proper nouns, brand names, or technical terms so the model calibrates its output vocabulary.
• Script/transliteration control — Guide the model toward a particular script or romanization convention.

Code-Switching Support

The multilingual nature of both the speech encoder and the LLM enables handling of code-switched speech (e.g., Hindi-English) when prompted appropriately:

"Transcribe the following Hindi-English code-mixed speech to text."

Usage

Requirements

pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu118
pip install transformers=4.56.3 huggingface_hub==0.36.0 pyyaml

Quick Start

Update inference_config.yaml with your model paths (see Configuration below), then run:

python inference_script.py

The script loads the full pipeline (encoder, MoE projector, LLM), transcribes the audio file, and prints the output text.

License

This model is released under the BharatGen non-commercial license. Please refer to the LICENSE file for detailed terms and conditions.

For more details about the model - https://arxiv.org/abs/2601.19451