Add Shrutam-2 contrib model: multilingual Indic ASR on Neuron

[jimburtoft]

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Description

Three-stage ASR pipeline for Shrutam-2 (bharatgenai/Shrutam-2), supporting 12 Indian languages on Trainium2. The pipeline consists of:

1. Conformer encoder (607.7M params, 24 layers) — traced via torch_neuronx.trace(), 9ms latency for 10s audio
2. SMEAR-MoE projector (50.4M params, 8 experts) — traced via torch_neuronx.trace(), 1.6ms latency
3. LLM decoder (1.2B LlamaForCausalLM) — compiled via NxD Inference ImageToTextModelWrapper with audio embedding scatter, 113 tok/s

End-to-end: 20.8 audio-seconds/s single-core, 61.1 audio-seconds/s with DP=4 on trn2.3xlarge. WER delta vs CPU: +1.3% (18/20 FLEURS samples).

Model Information

Model Name: Shrutam-2 (bharatgenai/Shrutam-2)

Model Architecture: Conformer encoder + SMEAR-MoE projector + LlamaForCausalLM decoder

Purpose: Multilingual automatic speech recognition for 12 Indian languages (Hindi, Tamil, Telugu, Bengali, Kannada, Malayalam, Marathi, Gujarati, Odia, Punjabi, Assamese, Urdu)

Checklist

Required Components

• Accuracy Test (ex. test/integration/test_model.py)

  • 9 integration tests across 4 test classes (encoder, SMEAR, LLM, E2E pipeline)
  • Encoder/SMEAR: cosine similarity > 0.99 vs CPU reference
  • LLM: text generation quality and throughput validation
  • E2E: synthetic audio, real FLEURS audio, and throughput tests
  • All 9 tests pass on trn2.3xlarge

• README.md with the following sections:

  • Usage Example: Three-step setup (trace encoder/SMEAR, compile LLM, run pipeline)
  • Compatibility Matrix: trn2.3xlarge SDK 2.29 validated
  • Example Checkpoints: bharatgenai/Shrutam-2
  • Testing Instructions: Full pytest commands with environment variables

• Source Code (src/)

  • modeling_shrutam2.py: Complete pipeline implementation including Conformer encoder, SMEAR-MoE projector, NxDI LLM wrapper, Shrutam2Pipeline class, and trace/compile utilities
  • Follows BioReason-Pro multimodal pipeline pattern

Optional Components

• Unit Tests (CPU or Neuron-based)
• vLLM Integration

Folder Structure

Confirm your contribution follows this structure:

/contrib/models/Shrutam-2/
  README.md
  /src
    __init__.py
    modeling_shrutam2.py
  /test
    __init__.py
    /unit
      __init__.py
    /integration
      __init__.py
      test_model.py

Testing

How did you test this change?

All 9 tests executed on trn2.3xlarge (LNC=2, Neuron SDK 2.29, NxDI 0.9.x):

pytest test/integration/test_model.py -v --timeout=600

Test Results:

test_encoder_accuracy_neuron_allclose  PASSED  (cosine sim: 0.9985)
test_encoder_latency                   PASSED  (9ms)
test_smear_accuracy_neuron_allclose    PASSED  (cosine sim: ~1.0)
test_smear_latency                     PASSED  (1.6ms)
test_llm_text_generation               PASSED
test_llm_decode_throughput             PASSED  (113 tok/s)
test_pipeline_synthetic_audio          PASSED
test_pipeline_real_audio               PASSED  (Hindi FLEURS)
test_pipeline_throughput               PASSED  (>30 tok/s)

Compatibility

Tested with:

• Neuron SDK Version(s): 2.29
• Instance Type(s): trn2.3xlarge (LNC=2)
• PyTorch Version: 2.9
• Python Version: 3.12.3

Additional Information

• The model uses the Pixtral/ImageToText NxDI pattern to scatter audio embeddings into the LLM input sequence, similar to how vision models inject image embeddings
• The Conformer encoder weights are FP32 and compiled with --auto-cast matmult for BF16 compute
• repetition_penalty=1.3 is recommended for greedy decoding to avoid hallucination on ~5% of samples that are beam-search-dependent
• The SMEAR-MoE projector uses utterance-level soft routing (no top-k gating), which causes einsum scaling issues at batch sizes > 1. For batched inference, run SMEAR at BS=1 in a loop.

Related Issues

N/A

vLLM Integration

• This model/feature is intended for use with vLLM
• Documentation includes vLLM registration instructions

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By submitting this PR, I confirm that:

• I have read and followed the contributing guidelines
• This is a community contribution and may have limited testing compared to officially-supported models
• The code follows best practices and is well-documented
• All required components listed above are included