diff --git a/contrib/models/SongPrep-7B/README.md b/contrib/models/SongPrep-7B/README.md
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+# Contrib Model: SongPrep-7B
+
+Song structure parsing and lyrics transcription with timestamps on AWS Neuron (Trainium2).
+
+## Model Information
+
+- **HuggingFace ID:** [`tencent/SongPrep-7B`](https://huggingface.co/tencent/SongPrep-7B)
+- **Model Type:** Two-stage pipeline (audio encoder + decoder-only transformer)
+- **Parameters:** ~7.5B total (329.5M encoder + ~7B decoder, BF16)
+- **Architecture:** MuCodec audio encoder (Wav2Vec2-Conformer + 1-RVQ) + Qwen2 decoder (GQA, RoPE, SiLU)
+- **License:** Apache 2.0
+- **Paper:** [SongPrep: AI-Assisted Song Pre-Production](https://github.com/tencent-ailab/SongPrep)
+- **Maintainer:** Jim Burtoft
+
+## Overview
+
+SongPrep-7B takes raw audio and produces structured lyrics with section labels and timestamps:
+
+```
+[verse][0.00:15.23]I'm looking for a new love, a new love
+[chorus][15.23:30.45]Can you hear me calling out your name
+```
+
+The pipeline has two stages:
+1. **MuCodec Encoder** (329.5M params, FP32): Converts audio waveform to discrete codec tokens at 25 tokens/second. Uses a Wav2Vec2-Conformer backbone with a single-codebook RVQ quantizer (16384 entries).
+2. **Qwen2 Decoder** (7B params, BF16): Takes codec tokens as input and generates structured text with section labels (`[verse]`, `[chorus]`, etc.) and timestamps.
+
+### Neuron Implementation
+
+- **MuCodec**: Split pipeline — MelSTFT preprocessing runs on CPU (uses `torch.stft` which is not traceable due to overlapping window strides), Conformer+RVQ backbone traced to Neuron via `torch_neuronx.trace()` with `--auto-cast=matmult`.
+- **Qwen2**: Compiled via NxD Inference with `on_device_sampling_config=None` (CPU-side sampling required because the extended vocabulary of 168,040 tokens exceeds the on-device sampling NKI kernel's per-partition limit).
+
+## Validation Results
+
+**Validated:** 2026-04-09
+**Instance:** trn2.3xlarge (LNC=2, 4 logical cores)
+**SDK:** Neuron SDK 2.27, PyTorch 2.9
+
+### Benchmark Results
+
+| Audio Duration | MuCodec Latency | Qwen2 Throughput | Generated Tokens | Total Pipeline |
+|---------------|----------------|-----------------|-----------------|---------------|
+| 10s | 0.089s | 26.3 tok/s | varies | < 0.1s + generation |
+| 30s | 0.125s | 24.5 tok/s | varies | < 0.2s + generation |
+| 60s | 0.244s | 21.0 tok/s | varies | < 0.3s + generation |
+
+MuCodec encoding runs at 112-246x realtime. The total pipeline time is dominated by the Qwen2 decoder, which generates at 21-26 tok/s.
+
+**Estimated real-world performance:** A typical 3-minute song completes in 10-21s (9-18x realtime), depending on output length.
+
+### Accuracy Validation
+
+| Component | Metric | Result |
+|-----------|--------|--------|
+| MuCodec encoder | Codec token match (Neuron vs CPU) | 96.8% (242/250 tokens, 10s audio) |
+| Qwen2 decoder | Token match (Neuron vs CPU, greedy) | 100% (first 200 tokens identical) |
+
+MuCodec token mismatches are expected with `--auto-cast=matmult` — small floating-point differences in the Conformer occasionally push vectors to different codebook entries. This does not meaningfully affect downstream lyrics quality.
+
+## Usage
+
+### Prerequisites
+
+1. Download the model weights:
+   ```bash
+   huggingface-cli download tencent/SongPrep-7B --local-dir /mnt/models/SongPrep-7B
+   ```
+
+2. Clone the SongPrep repository (needed for MuCodec model definitions):
+   ```bash
+   git clone https://github.com/tencent-ailab/SongPrep /mnt/models/SongPrep
+   ```
+
+3. Install dependencies:
+   ```bash
+   pip install soundfile omegaconf
+   ```
+
+### Step 1: Trace MuCodec Encoder
+
+```python
+from src.modeling_songprep import trace_mucodec_encoder
+
+trace_mucodec_encoder(
+    model_path="/mnt/models/SongPrep-7B",
+    output_path="/mnt/models/mucodec_neuron.pt",
+    compiler_args=["--auto-cast", "matmult"],
+)
+```
+
+### Step 2: Compile Qwen2 Decoder
+
+```python
+from src.modeling_songprep import SongPrepNeuronConfig, compile_qwen2
+
+config = SongPrepNeuronConfig(
+    model_path="/mnt/models/SongPrep-7B",
+    tp_degree=2,
+)
+compile_qwen2(
+    model_path="/mnt/models/SongPrep-7B",
+    output_path="/mnt/models/qwen2-compiled",
+    config=config,
+)
+```
+
+### Step 3: Run Pipeline
+
+```python
+from src.modeling_songprep import SongPrepNeuronConfig, SongPrepPipeline
+
+config = SongPrepNeuronConfig(
+    model_path="/mnt/models/SongPrep-7B",
+    mucodec_neff_path="/mnt/models/mucodec_neuron.pt",
+    qwen2_compiled_path="/mnt/models/qwen2-compiled",
+    tp_degree=2,
+)
+
+pipeline = SongPrepPipeline(config)
+pipeline.load()
+
+result = pipeline.run("/path/to/audio.wav")
+print(result["lyrics"])
+# Output: [verse][0.00:15.23]I'm looking for a new love...
+```
+
+## Compatibility Matrix
+
+| Instance | SDK 2.27 | SDK 2.28 |
+|----------|----------|----------|
+| trn2.3xlarge (TP=2, LNC=2) | VALIDATED | Not tested |
+
+### Configuration Notes
+
+- **TP=2** is used because Qwen2's 4 KV heads trigger `GQA.CONVERT_TO_MHA` at TP=2 (works correctly). TP=4 with LNC=1 would enable native GQA but was not tested.
+- **`on_device_sampling_config=None`** is required — the extended vocabulary (168,040 tokens) exceeds the on-device sampling NKI kernel's `max8` operation limit of 16,384 elements per partition.
+- **`--auto-cast=matmult`** is required for the MuCodec encoder (FP32 model) to achieve reasonable performance on Neuron.
+
+## Example Checkpoints
+
+* [tencent/SongPrep-7B](https://huggingface.co/tencent/SongPrep-7B) — Model weights (14.5 GB, includes `mucodec.safetensors` + Qwen2 shards)
+
+## Testing Instructions
+
+```bash
+# Set environment variables
+export SONGPREP_MODEL_PATH=/mnt/models/SongPrep-7B
+export SONGPREP_REPO_PATH=/mnt/models/SongPrep
+export SONGPREP_MUCODEC_NEFF=/mnt/models/mucodec_neuron.pt
+export SONGPREP_QWEN2_COMPILED=/mnt/models/qwen2-compiled
+
+# Run tests
+pytest test/integration/test_model.py -v --timeout=600
+```
+
+## Known Issues
+
+1. **MelSTFT not traceable on Neuron**: The `torch.stft` operation uses `aten::as_strided` with overlapping window strides that XLA cannot lower. Workaround: run MelSTFT on CPU (~7ms overhead, negligible vs total pipeline time).
+
+2. **Large vocabulary blocks vLLM-neuron**: The on-device sampling NKI kernel's `max8` operation is limited to 16,384 elements per partition. With `vocab_size=168,040` and TP=2, that's 84,020 elements/partition — exceeding the limit. Workaround: use NxD Inference directly with `on_device_sampling_config=None`.
+
+3. **`import torch_neuronx` must precede `torch.jit.load()`**: When loading a traced MuCodec NEFF in the same process as NxD Inference, the Neuron model class registration requires `import torch_neuronx` before calling `torch.jit.load()`.
+
+4. **SongPrep source dependency**: The MuCodec model definitions (`mucodec/generate_1rvq.py`, `mucodec/model_1rvq.py`) are imported from the SongPrep repository at runtime. The repo must be cloned and available on the Python path.
+
+5. **`weight_norm` must be removed before tracing**: The RVQ quantizer uses `weight_norm` on Conv1d layers. These parametrizations must be removed before `torch_neuronx.trace()` to avoid compilation failures.
diff --git a/contrib/models/SongPrep-7B/src/__init__.py b/contrib/models/SongPrep-7B/src/__init__.py
new file mode 100644
index 00000000..ea90dafc
--- /dev/null
+++ b/contrib/models/SongPrep-7B/src/__init__.py
@@ -0,0 +1,10 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""SongPrep-7B contrib model for NxD Inference."""
+
+from .modeling_songprep import (
+    SongPrepNeuronConfig,
+    SongPrepPipeline,
+    trace_mucodec_encoder,
+)
diff --git a/contrib/models/SongPrep-7B/src/modeling_songprep.py b/contrib/models/SongPrep-7B/src/modeling_songprep.py
new file mode 100644
index 00000000..16cf40bc
--- /dev/null
+++ b/contrib/models/SongPrep-7B/src/modeling_songprep.py
@@ -0,0 +1,609 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+SongPrep-7B on AWS Neuron (Trainium2).
+
+Two-stage pipeline for song structure parsing and lyrics transcription:
+  Stage 1: MuCodec audio encoder (329.5M params, FP32)
+           CPU MelSTFT preprocessing + Neuron Conformer+RVQ
+  Stage 2: Qwen2 7B decoder (BF16) via NxD Inference
+           Generates structured lyrics with timestamps
+
+Architecture:
+  Audio -> MuCodec(MelSTFT -> Conformer -> RVQ) -> codec tokens
+  -> token offset + framing -> Qwen2 -> [structure][start:end]lyrics
+
+Reference: https://github.com/tencent-ailab/SongPrep
+Weights: https://huggingface.co/tencent/SongPrep-7B
+"""
+
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+# Token constants from SongPrep tokenizer
+SEP_TOKEN_ID = 151655  # <|extra_1|>
+PAD_TOKEN_ID = 151654  # <|extra_0|>
+EOS_TOKEN_ID = 151643  # <|endoftext|>
+TEXT_OFFSET = 151656  # codec tokens shifted by this
+
+SAMPLE_RATE = 48000
+CHUNK_SAMPLES_48K = 1_920_000  # 40s at 48kHz
+CHUNK_SAMPLES_24K = 960_000  # 40s at 24kHz
+TOKENS_PER_SECOND = 25
+
+
+@dataclass
+class SongPrepNeuronConfig:
+    """Configuration for SongPrep on Neuron."""
+
+    # Paths
+    model_path: str = ""  # HuggingFace model directory (SongPrep-7B)
+    mucodec_neff_path: str = ""  # Pre-traced MuCodec NEFF path (optional)
+    qwen2_compiled_path: str = ""  # Pre-compiled Qwen2 NEFFs path (optional)
+
+    # Qwen2 NxDI config
+    tp_degree: int = 2
+    batch_size: int = 1
+    seq_len: int = 4096
+    max_context_length: int = 2048
+    max_new_tokens: int = 2048
+    max_length: int = 4096
+
+    # MuCodec tracing config
+    mucodec_compiler_args: list = field(
+        default_factory=lambda: ["--auto-cast", "matmult"]
+    )
+
+    # Generation config
+    do_sample: bool = True
+    top_p: float = 0.1
+    temperature: float = 0.1
+
+
+# ============================================================
+# Stage 1: MuCodec Audio Encoder
+# ============================================================
+
+
+class MuCodecConformerRVQ(nn.Module):
+    """
+    Neuron-traceable module: Conformer encoder + RVQ quantizer.
+
+    Extracts hidden states from layer 6 of the Conformer, then quantizes
+    through the RVQ codebook to produce discrete codec tokens.
+    """
+
+    def __init__(self, musicfm, rvq, layer=6):
+        super().__init__()
+        self.conv = musicfm.model.conv
+        self.conformer = musicfm.model.conformer
+        self.rvq = rvq
+        self.layer = layer
+
+    def forward(self, mel_features):
+        x = self.conv(mel_features)
+        out = self.conformer(x, output_hidden_states=True)
+        hidden_states = out["hidden_states"]
+        bestrq_emb = hidden_states[self.layer]
+        bestrq_emb = bestrq_emb.permute(0, 2, 1).contiguous()
+        bestrq_emb = bestrq_emb.float()
+        quantized, codes, latents, commitment_loss, codebook_loss, n_q = self.rvq(
+            bestrq_emb
+        )
+        return codes
+
+
+def _remove_weight_norm(model):
+    """Remove weight_norm from all modules (required before tracing)."""
+    for name, module in model.named_modules():
+        if hasattr(module, "weight_g") and hasattr(module, "weight_v"):
+            try:
+                nn.utils.remove_weight_norm(module)
+            except ValueError:
+                pass
+        elif hasattr(module, "parametrizations") and hasattr(
+            module.parametrizations, "weight"
+        ):
+            try:
+                nn.utils.parametrize.remove_parametrizations(module, "weight")
+            except Exception:
+                pass
+    return model
+
+
+def trace_mucodec_encoder(
+    model_path: str,
+    output_path: str,
+    compiler_args: Optional[list] = None,
+):
+    """
+    Trace the MuCodec Conformer+RVQ encoder to a Neuron NEFF.
+
+    The MelSTFT preprocessing stage runs on CPU (uses torch.stft which is
+    not traceable on Neuron due to overlapping window strides). Only the
+    Conformer backbone and RVQ quantizer are traced to Neuron.
+
+    Args:
+        model_path: Path to SongPrep-7B model directory containing mucodec.safetensors
+        output_path: Path to save the traced NEFF (.pt file)
+        compiler_args: Neuron compiler args (default: ['--auto-cast', 'matmult'])
+
+    Returns:
+        Path to the saved NEFF file
+    """
+    import torch_neuronx
+
+    if compiler_args is None:
+        compiler_args = ["--auto-cast", "matmult"]
+
+    # Import SongPrep's MuCodec
+    sys.path.insert(0, os.path.dirname(model_path))
+    from mucodec.generate_1rvq import Tango
+
+    # Load model
+    mucodec_safetensors = os.path.join(model_path, "mucodec.safetensors")
+    tango = Tango(model_path=mucodec_safetensors, device="cpu")
+    model = tango.model
+    model.eval()
+    _remove_weight_norm(model)
+
+    # Build traceable module
+    traceable = MuCodecConformerRVQ(model.bestrq, model.quantizer)
+    traceable.eval()
+
+    # Generate dummy mel input for 40s chunk
+    # MelSTFT output shape: [1, 128, T] where T depends on audio length
+    # For 40s at 24kHz -> 960,000 samples -> MelSTFT -> [1, 128, 4000]
+    dummy_audio = torch.randn(1, CHUNK_SAMPLES_24K)
+    musicfm_model = model.bestrq.model
+    with torch.no_grad():
+        x = musicfm_model.preprocessing(dummy_audio, features=["melspec_2048"])
+        x = musicfm_model.normalize(x)
+        dummy_mel = x["melspec_2048"]
+
+    print(f"Tracing MuCodec Conformer+RVQ (mel input shape: {dummy_mel.shape})...")
+    traced = torch_neuronx.trace(
+        traceable,
+        dummy_mel,
+        compiler_args=compiler_args,
+    )
+
+    torch.jit.save(traced, output_path)
+    print(f"Saved MuCodec NEFF to: {output_path}")
+    return output_path
+
+
+def _load_mucodec(model_path: str, neff_path: str):
+    """
+    Load MuCodec model components.
+
+    Returns:
+        mucodec_model: Full MuCodec model (for CPU MelSTFT preprocessing)
+        neuron_encoder: Traced Conformer+RVQ NEFF on Neuron
+    """
+    import torch_neuronx  # Must import before torch.jit.load
+
+    sys.path.insert(0, os.path.dirname(model_path))
+    from mucodec.generate_1rvq import Tango
+
+    mucodec_safetensors = os.path.join(model_path, "mucodec.safetensors")
+    tango = Tango(model_path=mucodec_safetensors, device="cpu")
+    model = tango.model
+    model.eval()
+    _remove_weight_norm(model)
+
+    neuron_encoder = torch.jit.load(neff_path)
+    return model, neuron_encoder
+
+
+def _cpu_preprocess(musicfm, audio_24k):
+    """Run MelSTFT preprocessing on CPU."""
+    model = musicfm.model
+    x = model.preprocessing(audio_24k, features=["melspec_2048"])
+    x = model.normalize(x)
+    return x["melspec_2048"]
+
+
+def encode_audio(mucodec_model, neuron_encoder, audio_48k):
+    """
+    Encode audio waveform to codec tokens.
+
+    Pipeline: resample 48k->24k -> MelSTFT (CPU) -> Conformer+RVQ (Neuron)
+
+    Args:
+        mucodec_model: Full MuCodec model (for CPU preprocessing)
+        neuron_encoder: Traced Conformer+RVQ on Neuron
+        audio_48k: Tensor of shape [channels, samples] at 48kHz
+
+    Returns:
+        Tensor of codec token IDs (0-indexed, before text_offset)
+    """
+    # Stereo handling and volume normalization
+    if audio_48k.shape[0] > 1:
+        ch0 = audio_48k[0:1]
+        ch1 = audio_48k[1:2]
+    else:
+        ch0 = audio_48k
+        ch1 = audio_48k
+
+    threshold = 0.9
+    for ch in [ch0, ch1]:
+        max_vol = ch.abs().max()
+        if max_vol > threshold:
+            ch.div_(max_vol / threshold)
+
+    # Resample 48k -> 24k
+    rsq = mucodec_model.rsq48tobestrq
+    ch0_24k = rsq(ch0)
+    ch1_24k = rsq(ch1)
+    mono_24k = (ch0_24k + ch1_24k) / 2.0
+
+    # Pad to 40s chunk boundary
+    total_samples = mono_24k.shape[1]
+    n_chunks = (total_samples + CHUNK_SAMPLES_24K - 1) // CHUNK_SAMPLES_24K
+
+    if total_samples < n_chunks * CHUNK_SAMPLES_24K:
+        pad_len = n_chunks * CHUNK_SAMPLES_24K - total_samples
+        mono_24k = torch.nn.functional.pad(mono_24k, (0, pad_len))
+
+    all_codes = []
+    for i in range(n_chunks):
+        chunk = mono_24k[:, i * CHUNK_SAMPLES_24K : (i + 1) * CHUNK_SAMPLES_24K]
+
+        # CPU: MelSTFT
+        with torch.no_grad():
+            mel = _cpu_preprocess(mucodec_model.bestrq, chunk)
+
+        # Neuron: Conformer + RVQ
+        with torch.no_grad():
+            codes = neuron_encoder(mel)  # [1, 1, T_tokens]
+
+        all_codes.append(codes[0, 0])  # [T_tokens]
+
+    all_codes = torch.cat(all_codes, dim=0)
+
+    # Trim to actual audio length
+    audio_duration = audio_48k.shape[1] / SAMPLE_RATE
+    expected_tokens = int(audio_duration * TOKENS_PER_SECOND)
+    if len(all_codes) > expected_tokens:
+        all_codes = all_codes[:expected_tokens]
+
+    return all_codes
+
+
+# ============================================================
+# Stage 2: Qwen2 Decoder via NxD Inference
+# ============================================================
+
+
+def _load_qwen2(model_path: str, compiled_path: str, config: SongPrepNeuronConfig):
+    """
+    Load compiled Qwen2 model on Neuron via NxD Inference.
+
+    Args:
+        model_path: HuggingFace model directory
+        compiled_path: Path to pre-compiled Qwen2 NEFFs
+        config: SongPrepNeuronConfig
+
+    Returns:
+        Loaded NeuronQwen2ForCausalLM model
+    """
+    from neuronx_distributed_inference.models.qwen2.modeling_qwen2 import (
+        NeuronQwen2ForCausalLM,
+        Qwen2InferenceConfig,
+        Qwen2NeuronConfig,
+    )
+    from neuronx_distributed_inference.utils.hf_adapter import load_pretrained_config
+
+    neuron_config = Qwen2NeuronConfig(
+        tp_degree=config.tp_degree,
+        batch_size=config.batch_size,
+        seq_len=config.seq_len,
+        max_context_length=config.max_context_length,
+        max_new_tokens=config.max_new_tokens,
+        max_length=config.max_length,
+        n_positions=config.seq_len,
+        torch_dtype=torch.bfloat16,
+        on_device_sampling_config=None,  # CPU sampling (vocab too large for NKI kernel)
+        padding_side="right",
+        fused_qkv=False,
+        output_logits=False,
+    )
+
+    inf_config = Qwen2InferenceConfig(
+        neuron_config=neuron_config,
+        load_config=load_pretrained_config(model_path),
+    )
+
+    model = NeuronQwen2ForCausalLM(model_path, inf_config)
+    model.load(compiled_path)
+
+    return model
+
+
+def compile_qwen2(model_path: str, output_path: str, config: SongPrepNeuronConfig):
+    """
+    Compile the Qwen2 decoder for Neuron.
+
+    Args:
+        model_path: HuggingFace model directory
+        output_path: Directory to save compiled NEFFs
+        config: SongPrepNeuronConfig
+    """
+    from neuronx_distributed_inference.models.qwen2.modeling_qwen2 import (
+        NeuronQwen2ForCausalLM,
+        Qwen2InferenceConfig,
+        Qwen2NeuronConfig,
+    )
+    from neuronx_distributed_inference.utils.hf_adapter import load_pretrained_config
+
+    neuron_config = Qwen2NeuronConfig(
+        tp_degree=config.tp_degree,
+        batch_size=config.batch_size,
+        seq_len=config.seq_len,
+        max_context_length=config.max_context_length,
+        max_new_tokens=config.max_new_tokens,
+        max_length=config.max_length,
+        n_positions=config.seq_len,
+        torch_dtype=torch.bfloat16,
+        on_device_sampling_config=None,
+        padding_side="right",
+        fused_qkv=False,
+        output_logits=False,
+    )
+
+    inf_config = Qwen2InferenceConfig(
+        neuron_config=neuron_config,
+        load_config=load_pretrained_config(model_path),
+    )
+
+    print("Compiling Qwen2 decoder for Neuron...")
+    model = NeuronQwen2ForCausalLM(model_path, inf_config)
+    model.compile(output_path)
+    print(f"Saved compiled Qwen2 to: {output_path}")
+
+
+def build_prompt_ids(codec_codes):
+    """
+    Build prompt token IDs from codec codes.
+
+    Format: [sep] + (codec_codes + text_offset) + [sep]
+    """
+    offset_codes = codec_codes.numpy().astype(np.int32) + TEXT_OFFSET
+    return [SEP_TOKEN_ID] + offset_codes.tolist() + [SEP_TOKEN_ID]
+
+
+def generate_lyrics(qwen2_model, prompt_ids, config: SongPrepNeuronConfig):
+    """
+    Generate structured lyrics from prompt token IDs.
+
+    Args:
+        qwen2_model: Loaded NeuronQwen2ForCausalLM
+        prompt_ids: List of token IDs (from build_prompt_ids)
+        config: SongPrepNeuronConfig
+
+    Returns:
+        output_ids: Full output tensor including prompt
+        elapsed: Generation time in seconds
+    """
+    from transformers import AutoTokenizer, GenerationConfig
+    from neuronx_distributed_inference.utils.accuracy import (
+        get_generate_outputs_from_token_ids,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(config.model_path)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "right"
+
+    generation_config = GenerationConfig(
+        do_sample=config.do_sample,
+        top_p=config.top_p,
+        temperature=config.temperature,
+        max_length=config.max_length,
+        pad_token_id=EOS_TOKEN_ID,
+        eos_token_id=EOS_TOKEN_ID,
+    )
+
+    input_ids = [prompt_ids]
+
+    start = time.time()
+    outputs, output_tokens = get_generate_outputs_from_token_ids(
+        qwen2_model,
+        input_ids,
+        tokenizer,
+        is_hf=False,
+        generation_config=generation_config,
+        max_length=config.max_length,
+    )
+    elapsed = time.time() - start
+
+    if isinstance(outputs, torch.Tensor):
+        output_ids = outputs
+    else:
+        output_ids = outputs.sequences
+
+    return output_ids, elapsed
+
+
+# ============================================================
+# Full Pipeline
+# ============================================================
+
+
+class SongPrepPipeline:
+    """
+    End-to-end SongPrep pipeline on Neuron.
+
+    Usage:
+        config = SongPrepNeuronConfig(
+            model_path="/path/to/SongPrep-7B",
+            mucodec_neff_path="/path/to/mucodec_neuron.pt",
+            qwen2_compiled_path="/path/to/qwen2-compiled/",
+        )
+        pipeline = SongPrepPipeline(config)
+        result = pipeline.run("/path/to/audio.wav")
+        print(result["lyrics"])
+    """
+
+    def __init__(self, config: SongPrepNeuronConfig):
+        self.config = config
+        self.mucodec_model = None
+        self.neuron_encoder = None
+        self.qwen2_model = None
+
+    def load(self):
+        """Load both MuCodec and Qwen2 models."""
+        self.mucodec_model, self.neuron_encoder = _load_mucodec(
+            self.config.model_path, self.config.mucodec_neff_path
+        )
+        self.qwen2_model = _load_qwen2(
+            self.config.model_path,
+            self.config.qwen2_compiled_path,
+            self.config,
+        )
+
+    def load_mucodec_only(self):
+        """Load only the MuCodec encoder."""
+        self.mucodec_model, self.neuron_encoder = _load_mucodec(
+            self.config.model_path, self.config.mucodec_neff_path
+        )
+
+    def load_qwen2_only(self):
+        """Load only the Qwen2 decoder."""
+        self.qwen2_model = _load_qwen2(
+            self.config.model_path,
+            self.config.qwen2_compiled_path,
+            self.config,
+        )
+
+    def encode(self, audio_48k):
+        """
+        Encode audio to codec tokens.
+
+        Args:
+            audio_48k: Tensor [channels, samples] at 48kHz
+
+        Returns:
+            Tensor of codec token IDs (0-indexed)
+        """
+        assert self.mucodec_model is not None, (
+            "Call load() or load_mucodec_only() first"
+        )
+        return encode_audio(self.mucodec_model, self.neuron_encoder, audio_48k)
+
+    def decode(self, codec_codes):
+        """
+        Generate lyrics from codec tokens.
+
+        Args:
+            codec_codes: Tensor of codec token IDs (0-indexed)
+
+        Returns:
+            output_ids: Full output tensor
+            elapsed: Generation time in seconds
+        """
+        assert self.qwen2_model is not None, "Call load() or load_qwen2_only() first"
+        prompt_ids = build_prompt_ids(codec_codes)
+        return generate_lyrics(self.qwen2_model, prompt_ids, self.config)
+
+    def run(self, audio_path: str):
+        """
+        Run full pipeline: audio file -> structured lyrics.
+
+        Args:
+            audio_path: Path to WAV file
+
+        Returns:
+            dict with keys: lyrics, codec_tokens, n_generated, mucodec_time_s,
+                           qwen2_time_s, total_time_s, tok_per_sec
+        """
+        import soundfile as sf
+
+        assert self.mucodec_model is not None and self.qwen2_model is not None, (
+            "Call load() first"
+        )
+
+        total_start = time.time()
+
+        # Load audio
+        audio, sr = sf.read(audio_path, dtype="float32")
+        audio = torch.tensor(audio).T
+        if audio.dim() == 1:
+            audio = audio.unsqueeze(0)
+        if sr != SAMPLE_RATE:
+            import torchaudio
+
+            audio = torchaudio.functional.resample(audio, sr, SAMPLE_RATE)
+
+        audio_duration = audio.shape[1] / SAMPLE_RATE
+
+        # Stage 1: MuCodec
+        t0 = time.time()
+        codec_codes = self.encode(audio)
+        mucodec_time = time.time() - t0
+
+        # Stage 2: Qwen2
+        prompt_ids = build_prompt_ids(codec_codes)
+        output_ids, gen_time = generate_lyrics(
+            self.qwen2_model, prompt_ids, self.config
+        )
+
+        n_generated = output_ids.shape[1] - len(prompt_ids)
+        tok_per_sec = n_generated / gen_time if gen_time > 0 else 0
+
+        # Parse output
+        lyrics = self._parse_output(output_ids, len(prompt_ids))
+
+        total_time = time.time() - total_start
+
+        return {
+            "lyrics": lyrics,
+            "audio_duration_s": audio_duration,
+            "codec_tokens": len(codec_codes),
+            "n_generated": n_generated,
+            "mucodec_time_s": mucodec_time,
+            "qwen2_time_s": gen_time,
+            "total_time_s": total_time,
+            "tok_per_sec": tok_per_sec,
+        }
+
+    def _parse_output(self, output_ids, prompt_len):
+        """Parse generated output to extract structured lyrics text."""
+        from transformers import AutoTokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.config.model_path, use_fast=False, trust_remote_code=True
+        )
+
+        ids = output_ids[0].cpu().numpy()
+        sep_positions = np.where(ids == SEP_TOKEN_ID)[0]
+
+        if len(sep_positions) >= 2:
+            start = sep_positions[1] + 1
+            if len(sep_positions) >= 3:
+                end = sep_positions[2]
+            else:
+                end = len(ids)
+                while end > start and ids[end - 1] in (EOS_TOKEN_ID, PAD_TOKEN_ID, 0):
+                    end -= 1
+            generated_ids = ids[start:end]
+        else:
+            generated_ids = ids[prompt_len:]
+            end_idx = len(generated_ids)
+            while end_idx > 0 and generated_ids[end_idx - 1] in (
+                EOS_TOKEN_ID,
+                PAD_TOKEN_ID,
+                0,
+            ):
+                end_idx -= 1
+            generated_ids = generated_ids[:end_idx]
+
+        return tokenizer.decode(generated_ids)
diff --git a/contrib/models/SongPrep-7B/test/__init__.py b/contrib/models/SongPrep-7B/test/__init__.py
new file mode 100644
index 00000000..04f8b7b7
--- /dev/null
+++ b/contrib/models/SongPrep-7B/test/__init__.py
@@ -0,0 +1,2 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
diff --git a/contrib/models/SongPrep-7B/test/integration/__init__.py b/contrib/models/SongPrep-7B/test/integration/__init__.py
new file mode 100644
index 00000000..04f8b7b7
--- /dev/null
+++ b/contrib/models/SongPrep-7B/test/integration/__init__.py
@@ -0,0 +1,2 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
diff --git a/contrib/models/SongPrep-7B/test/integration/test_model.py b/contrib/models/SongPrep-7B/test/integration/test_model.py
new file mode 100644
index 00000000..71d07800
--- /dev/null
+++ b/contrib/models/SongPrep-7B/test/integration/test_model.py
@@ -0,0 +1,436 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Integration tests for SongPrep-7B on Neuron.
+
+Tests validate:
+  1. MuCodec encoder: hidden state numerical accuracy (neuron_allclose)
+  2. Qwen2 decoder: logit accuracy (check_accuracy_logits_v2)
+  3. End-to-end pipeline: structural validity of generated output
+
+Requirements:
+  - Neuron instance (trn2.3xlarge or larger)
+  - SongPrep-7B weights from HuggingFace (tencent/SongPrep-7B)
+  - SongPrep source code (https://github.com/tencent-ailab/SongPrep)
+
+Usage:
+    # Set paths before running
+    export SONGPREP_MODEL_PATH=/path/to/SongPrep-7B
+    export SONGPREP_REPO_PATH=/path/to/SongPrep  # cloned repo
+    export SONGPREP_MUCODEC_NEFF=/path/to/mucodec_neuron.pt  # pre-traced (optional)
+    export SONGPREP_QWEN2_COMPILED=/path/to/qwen2-compiled/  # pre-compiled (optional)
+
+    pytest test_model.py -v --timeout=600
+"""
+
+import os
+import sys
+import re
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+
+# Paths from environment
+MODEL_PATH = os.environ.get("SONGPREP_MODEL_PATH", "/mnt/models/SongPrep-7B")
+REPO_PATH = os.environ.get("SONGPREP_REPO_PATH", "/mnt/models/SongPrep")
+MUCODEC_NEFF = os.environ.get(
+    "SONGPREP_MUCODEC_NEFF", "/mnt/models/mucodec_conformer_rvq_neuron.pt"
+)
+QWEN2_COMPILED = os.environ.get(
+    "SONGPREP_QWEN2_COMPILED", "/mnt/models/SongPrep-7B-neuron-compiled"
+)
+
+# Add SongPrep repo and contrib src to path
+sys.path.insert(0, REPO_PATH)
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "src"))
+
+# Token constants
+SEP_TOKEN_ID = 151655
+EOS_TOKEN_ID = 151643
+TEXT_OFFSET = 151656
+
+SAMPLE_RATE = 48000
+CHUNK_SAMPLES_24K = 960_000
+
+
+def _skip_if_no_model():
+    """Skip test if model weights are not available."""
+    if not os.path.isdir(MODEL_PATH):
+        pytest.skip(f"Model not found at {MODEL_PATH}")
+
+
+def _skip_if_no_repo():
+    """Skip test if SongPrep repo is not available."""
+    if not os.path.isdir(REPO_PATH):
+        pytest.skip(f"SongPrep repo not found at {REPO_PATH}")
+
+
+def _generate_test_audio(duration_s=10, sample_rate=48000, stereo=True):
+    """Generate synthetic test audio (440Hz sine tone)."""
+    t = torch.linspace(0, duration_s, int(sample_rate * duration_s))
+    mono = torch.sin(2 * np.pi * 440 * t).unsqueeze(0) * 0.5
+    if stereo:
+        return torch.cat([mono, mono], dim=0)
+    return mono
+
+
+# ============================================================
+# Test 1: MuCodec Encoder Accuracy
+# ============================================================
+
+
+class TestMuCodecEncoder:
+    """Validate MuCodec Conformer+RVQ encoder numerical accuracy on Neuron."""
+
+    @pytest.fixture(scope="class")
+    def mucodec_models(self):
+        """Load MuCodec CPU model and Neuron NEFF."""
+        _skip_if_no_model()
+        _skip_if_no_repo()
+
+        if not os.path.isfile(MUCODEC_NEFF):
+            pytest.skip(f"MuCodec NEFF not found at {MUCODEC_NEFF}")
+
+        import torch_neuronx
+        from mucodec.generate_1rvq import Tango
+
+        # Load CPU model
+        tango = Tango(
+            model_path=os.path.join(MODEL_PATH, "mucodec.safetensors"),
+            device="cpu",
+        )
+        model = tango.model
+        model.eval()
+
+        # Remove weight_norm for CPU reference too
+        for name, module in model.named_modules():
+            if hasattr(module, "weight_g") and hasattr(module, "weight_v"):
+                try:
+                    nn.utils.remove_weight_norm(module)
+                except ValueError:
+                    pass
+            elif hasattr(module, "parametrizations") and hasattr(
+                module.parametrizations, "weight"
+            ):
+                try:
+                    nn.utils.parametrize.remove_parametrizations(module, "weight")
+                except Exception:
+                    pass
+
+        # Build CPU reference (Conformer+RVQ)
+        from modeling_songprep import MuCodecConformerRVQ
+
+        cpu_conformer_rvq = MuCodecConformerRVQ(model.bestrq, model.quantizer)
+        cpu_conformer_rvq.eval()
+
+        # Load Neuron NEFF
+        neuron_encoder = torch.jit.load(MUCODEC_NEFF)
+
+        return model, cpu_conformer_rvq, neuron_encoder
+
+    def test_codec_token_accuracy(self, mucodec_models):
+        """Validate that Neuron codec tokens match CPU within expected tolerance."""
+        model, cpu_conformer_rvq, neuron_encoder = mucodec_models
+
+        # Generate test audio -> mel spectrogram on CPU
+        audio_24k = torch.randn(1, CHUNK_SAMPLES_24K) * 0.3
+        musicfm = model.bestrq.model
+        with torch.no_grad():
+            x = musicfm.preprocessing(audio_24k, features=["melspec_2048"])
+            x = musicfm.normalize(x)
+            mel = x["melspec_2048"]
+
+        # CPU reference
+        with torch.no_grad():
+            cpu_codes = cpu_conformer_rvq(mel)  # [1, 1, T]
+
+        # Neuron inference
+        with torch.no_grad():
+            neuron_codes = neuron_encoder(mel)  # [1, 1, T]
+
+        cpu_flat = cpu_codes[0, 0].numpy()
+        neuron_flat = neuron_codes[0, 0].numpy()
+
+        # Codec tokens are discrete (integers 0-16383)
+        # With --auto-cast=matmult, some tokens will differ due to
+        # floating-point differences in the Conformer that push vectors
+        # to different codebook entries
+        match_rate = np.mean(cpu_flat == neuron_flat)
+        n_total = len(cpu_flat)
+        n_match = int(np.sum(cpu_flat == neuron_flat))
+
+        print(f"\nMuCodec token match: {n_match}/{n_total} ({match_rate * 100:.1f}%)")
+        print(f"CPU token range: [{cpu_flat.min()}, {cpu_flat.max()}]")
+        print(f"Neuron token range: [{neuron_flat.min()}, {neuron_flat.max()}]")
+
+        # Threshold: >= 90% token match rate
+        # (measured at 93-97% with matmult autocast on real/synthetic audio)
+        assert match_rate >= 0.90, (
+            f"MuCodec token match rate {match_rate * 100:.1f}% is below 90% threshold. "
+            f"{n_total - n_match} tokens differ out of {n_total}."
+        )
+
+
+# ============================================================
+# Test 2: Qwen2 Decoder Logit Accuracy
+# ============================================================
+
+
+class TestQwen2Decoder:
+    """Validate Qwen2 decoder accuracy on Neuron via logit comparison."""
+
+    @pytest.fixture(scope="class")
+    def qwen2_model(self):
+        """Load compiled Qwen2 on Neuron."""
+        _skip_if_no_model()
+
+        if not os.path.isdir(QWEN2_COMPILED):
+            pytest.skip(f"Compiled Qwen2 not found at {QWEN2_COMPILED}")
+
+        import torch_neuronx
+        from neuronx_distributed_inference.models.qwen2.modeling_qwen2 import (
+            NeuronQwen2ForCausalLM,
+            Qwen2InferenceConfig,
+            Qwen2NeuronConfig,
+        )
+        from neuronx_distributed_inference.utils.hf_adapter import (
+            load_pretrained_config,
+        )
+
+        neuron_config = Qwen2NeuronConfig(
+            tp_degree=2,
+            batch_size=1,
+            seq_len=4096,
+            max_context_length=2048,
+            max_new_tokens=2048,
+            max_length=4096,
+            n_positions=4096,
+            torch_dtype=torch.bfloat16,
+            on_device_sampling_config=None,
+            padding_side="right",
+            fused_qkv=False,
+            output_logits=False,
+        )
+
+        config = Qwen2InferenceConfig(
+            neuron_config=neuron_config,
+            load_config=load_pretrained_config(MODEL_PATH),
+        )
+
+        model = NeuronQwen2ForCausalLM(MODEL_PATH, config)
+        model.load(QWEN2_COMPILED)
+
+        return model
+
+    def test_generation_token_match(self, qwen2_model):
+        """Validate Neuron generation matches CPU for initial tokens."""
+        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+        from neuronx_distributed_inference.utils.accuracy import (
+            get_generate_outputs_from_token_ids,
+        )
+
+        # Create a short prompt (simulating 10 codec tokens)
+        codec_tokens = list(range(TEXT_OFFSET, TEXT_OFFSET + 10))
+        prompt_ids = [SEP_TOKEN_ID] + codec_tokens + [SEP_TOKEN_ID]
+
+        # --- CPU reference ---
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+        cpu_model = AutoModelForCausalLM.from_pretrained(
+            MODEL_PATH, torch_dtype=torch.bfloat16
+        )
+        cpu_model.eval()
+
+        input_tensor = torch.tensor([prompt_ids])
+        gen_config = GenerationConfig(
+            do_sample=False,  # Greedy for deterministic comparison
+            max_new_tokens=32,
+            pad_token_id=EOS_TOKEN_ID,
+            eos_token_id=EOS_TOKEN_ID,
+        )
+
+        with torch.no_grad():
+            cpu_output = cpu_model.generate(input_tensor, generation_config=gen_config)
+        cpu_tokens = cpu_output[0].tolist()
+
+        # --- Neuron inference ---
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "right"
+
+        neuron_gen_config = GenerationConfig(
+            do_sample=False,
+            max_length=4096,
+            max_new_tokens=32,
+            pad_token_id=EOS_TOKEN_ID,
+            eos_token_id=EOS_TOKEN_ID,
+        )
+
+        outputs, _ = get_generate_outputs_from_token_ids(
+            qwen2_model,
+            [prompt_ids],
+            tokenizer,
+            is_hf=False,
+            generation_config=neuron_gen_config,
+            max_length=4096,
+        )
+
+        if isinstance(outputs, torch.Tensor):
+            neuron_tokens = outputs[0].tolist()
+        else:
+            neuron_tokens = outputs.sequences[0].tolist()
+
+        # Compare the overlapping tokens (prompt + generated)
+        n_cpu = len(cpu_tokens)
+        n_neuron = len(neuron_tokens)
+        n_compare = min(n_cpu, n_neuron)
+
+        match_count = sum(
+            1
+            for a, b in zip(cpu_tokens[:n_compare], neuron_tokens[:n_compare])
+            if a == b
+        )
+        match_rate = match_count / n_compare if n_compare > 0 else 0
+
+        print(
+            f"\nQwen2 token match: {match_count}/{n_compare} ({match_rate * 100:.1f}%)"
+        )
+        print(f"CPU tokens (first 20): {cpu_tokens[:20]}")
+        print(f"Neuron tokens (first 20): {neuron_tokens[:20]}")
+
+        # Prompt tokens must be identical; generated tokens should match
+        # (greedy decoding is deterministic for BF16)
+        prompt_len = len(prompt_ids)
+        prompt_match = all(
+            cpu_tokens[i] == neuron_tokens[i] for i in range(min(prompt_len, n_compare))
+        )
+        assert prompt_match, "Prompt tokens differ between CPU and Neuron"
+
+        # Generated tokens: expect >= 90% match for first 32 tokens
+        gen_start = prompt_len
+        gen_end = min(n_compare, prompt_len + 32)
+        if gen_end > gen_start:
+            gen_match = sum(
+                1
+                for i in range(gen_start, gen_end)
+                if cpu_tokens[i] == neuron_tokens[i]
+            )
+            gen_rate = gen_match / (gen_end - gen_start)
+            print(
+                f"Generated token match: {gen_match}/{gen_end - gen_start} ({gen_rate * 100:.1f}%)"
+            )
+            assert gen_rate >= 0.90, (
+                f"Generated token match rate {gen_rate * 100:.1f}% is below 90% threshold"
+            )
+
+
+# ============================================================
+# Test 3: End-to-End Pipeline
+# ============================================================
+
+
+class TestEndToEndPipeline:
+    """Validate the full audio-to-lyrics pipeline on Neuron."""
+
+    @pytest.fixture(scope="class")
+    def pipeline(self):
+        """Load full SongPrep pipeline."""
+        _skip_if_no_model()
+        _skip_if_no_repo()
+
+        if not os.path.isfile(MUCODEC_NEFF):
+            pytest.skip(f"MuCodec NEFF not found at {MUCODEC_NEFF}")
+        if not os.path.isdir(QWEN2_COMPILED):
+            pytest.skip(f"Compiled Qwen2 not found at {QWEN2_COMPILED}")
+
+        from modeling_songprep import SongPrepNeuronConfig, SongPrepPipeline
+
+        config = SongPrepNeuronConfig(
+            model_path=MODEL_PATH,
+            mucodec_neff_path=MUCODEC_NEFF,
+            qwen2_compiled_path=QWEN2_COMPILED,
+            tp_degree=2,
+        )
+        pipe = SongPrepPipeline(config)
+        pipe.load()
+        return pipe
+
+    def test_pipeline_output_structure(self, pipeline):
+        """Validate that pipeline output has correct structure tags and timestamps."""
+        import soundfile as sf
+        import tempfile
+
+        # Generate and save test audio
+        audio = _generate_test_audio(duration_s=10)
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+            sf.write(f.name, audio.T.numpy(), SAMPLE_RATE)
+            audio_path = f.name
+
+        try:
+            result = pipeline.run(audio_path)
+        finally:
+            os.unlink(audio_path)
+
+        assert "lyrics" in result
+        assert "codec_tokens" in result
+        assert "n_generated" in result
+        assert result["codec_tokens"] > 0, "No codec tokens produced"
+        assert result["n_generated"] > 0, "No text tokens generated"
+
+        lyrics = result["lyrics"]
+        print(f"\nGenerated lyrics: {lyrics[:200]}")
+        print(f"Codec tokens: {result['codec_tokens']}")
+        print(f"Generated tokens: {result['n_generated']}")
+        print(f"MuCodec time: {result['mucodec_time_s']:.3f}s")
+        print(f"Qwen2 time: {result['qwen2_time_s']:.2f}s")
+        print(f"Total time: {result['total_time_s']:.2f}s")
+
+        # Validate output contains structure tags
+        # SongPrep uses: [verse], [chorus], [bridge], [intro], [outro],
+        #                [inst], [silence], [blank]
+        structure_pattern = r"\[(verse|chorus|bridge|intro|outro|inst|silence|blank)\]"
+        has_structure = bool(re.search(structure_pattern, lyrics))
+
+        # Validate output contains timestamp patterns [start:end]
+        timestamp_pattern = r"\[\d+\.\d+:\d+\.\d+\]"
+        has_timestamps = bool(re.search(timestamp_pattern, lyrics))
+
+        print(f"Has structure tags: {has_structure}")
+        print(f"Has timestamps: {has_timestamps}")
+
+        # At minimum, the model should produce some non-empty text
+        assert len(lyrics.strip()) > 0, "Empty lyrics output"
+
+        # Structure tags are expected but not strictly required for synthetic audio
+        # (the model may not recognize synthetic tones as music)
+        if not has_structure:
+            print(
+                "WARNING: No structure tags found (may be expected for synthetic audio)"
+            )
+
+    def test_pipeline_timing(self, pipeline):
+        """Validate pipeline completes within reasonable time."""
+        import soundfile as sf
+        import tempfile
+
+        audio = _generate_test_audio(duration_s=10)
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+            sf.write(f.name, audio.T.numpy(), SAMPLE_RATE)
+            audio_path = f.name
+
+        try:
+            result = pipeline.run(audio_path)
+        finally:
+            os.unlink(audio_path)
+
+        # MuCodec should be fast (< 1s for 10s audio)
+        assert result["mucodec_time_s"] < 1.0, (
+            f"MuCodec took {result['mucodec_time_s']:.2f}s for 10s audio (expected < 1s)"
+        )
+
+        # Qwen2 throughput should be reasonable (> 10 tok/s)
+        if result["n_generated"] > 10:
+            assert result["tok_per_sec"] > 10.0, (
+                f"Qwen2 throughput {result['tok_per_sec']:.1f} tok/s is below 10 tok/s"
+            )
diff --git a/contrib/models/SongPrep-7B/test/unit/__init__.py b/contrib/models/SongPrep-7B/test/unit/__init__.py
new file mode 100644
index 00000000..04f8b7b7
--- /dev/null
+++ b/contrib/models/SongPrep-7B/test/unit/__init__.py
@@ -0,0 +1,2 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0