From c04cbcbe350f6c2bdbab2edca20e2a063b62c30a Mon Sep 17 00:00:00 2001
From: Alex-Wengg <hanweng9@gmail.com>
Date: Fri, 13 Mar 2026 15:44:42 -0400
Subject: [PATCH] feat: add Magpie TTS CoreML conversion pipeline

NVIDIA Magpie TTS Multilingual (357M) conversion to CoreML.

Pipeline (4 models):
- text_encoder: text tokenization and encoding
- decoder_prefill: batch speaker context into KV cache
- decoder_step: single AR step with KV cache
- nanocodec_decoder: codec tokens to 22kHz audio

9 languages (en, es, de, fr, it, vi, zh, hi, ja), 5 speakers.

Includes conversion scripts, traceable wrappers, export scripts
for embeddings/tokenizers/weights, and CoreML inference script.

Source: nvidia/magpie_tts_multilingual_357m
---
 models/tts/magpie/README.md                   |  253 +
 models/tts/magpie/coreml/build_openjtalk.sh   |  205 +
 models/tts/magpie/coreml/compile_mlmodelc.py  |   91 +
 .../magpie/coreml/convert_decoder_prefill.py  |  139 +
 .../tts/magpie/coreml/convert_decoder_step.py |  135 +
 models/tts/magpie/coreml/convert_nanocodec.py |  326 ++
 .../tts/magpie/coreml/convert_text_encoder.py |  102 +
 models/tts/magpie/coreml/export_constants.py  |  168 +
 .../magpie/coreml/export_local_transformer.py |  191 +
 models/tts/magpie/coreml/export_pypinyin.py   |   79 +
 .../magpie/coreml/export_tokenizer_aliases.py |   46 +
 models/tts/magpie/coreml/export_tokenizers.py |  228 +
 models/tts/magpie/coreml/generate_coreml.py   |  533 ++
 models/tts/magpie/coreml/generate_pytorch.py  |  166 +
 .../tts/magpie/coreml/openjtalk_wrapper.cpp   |  202 +
 models/tts/magpie/coreml/openjtalk_wrapper.h  |   53 +
 .../traceable/traceable_decoder_prefill.py    |  268 +
 .../traceable/traceable_decoder_step.py       |  359 ++
 .../traceable/traceable_text_encoder.py       |  246 +
 models/tts/magpie/pyproject.toml              |   41 +
 models/tts/magpie/uv.lock                     | 4393 +++++++++++++++++
 21 files changed, 8224 insertions(+)
 create mode 100644 models/tts/magpie/README.md
 create mode 100755 models/tts/magpie/coreml/build_openjtalk.sh
 create mode 100644 models/tts/magpie/coreml/compile_mlmodelc.py
 create mode 100644 models/tts/magpie/coreml/convert_decoder_prefill.py
 create mode 100644 models/tts/magpie/coreml/convert_decoder_step.py
 create mode 100644 models/tts/magpie/coreml/convert_nanocodec.py
 create mode 100644 models/tts/magpie/coreml/convert_text_encoder.py
 create mode 100644 models/tts/magpie/coreml/export_constants.py
 create mode 100644 models/tts/magpie/coreml/export_local_transformer.py
 create mode 100644 models/tts/magpie/coreml/export_pypinyin.py
 create mode 100644 models/tts/magpie/coreml/export_tokenizer_aliases.py
 create mode 100644 models/tts/magpie/coreml/export_tokenizers.py
 create mode 100644 models/tts/magpie/coreml/generate_coreml.py
 create mode 100644 models/tts/magpie/coreml/generate_pytorch.py
 create mode 100644 models/tts/magpie/coreml/openjtalk_wrapper.cpp
 create mode 100644 models/tts/magpie/coreml/openjtalk_wrapper.h
 create mode 100644 models/tts/magpie/coreml/traceable/traceable_decoder_prefill.py
 create mode 100644 models/tts/magpie/coreml/traceable/traceable_decoder_step.py
 create mode 100644 models/tts/magpie/coreml/traceable/traceable_text_encoder.py
 create mode 100644 models/tts/magpie/pyproject.toml
 create mode 100644 models/tts/magpie/uv.lock

diff --git a/models/tts/magpie/README.md b/models/tts/magpie/README.md
new file mode 100644
index 0000000..f4a6500
--- /dev/null
+++ b/models/tts/magpie/README.md
@@ -0,0 +1,253 @@
+# Magpie TTS 357M — CoreML Conversion
+
+Convert [NVIDIA Magpie TTS Multilingual 357M](https://huggingface.co/nvidia/magpie_tts_multilingual_357m) to CoreML for on-device iOS/macOS inference.
+
+## Architecture
+
+Magpie TTS is an encoder-decoder transformer that generates discrete audio codec tokens autoregressively, followed by NanoCodec vocoder for waveform synthesis.
+
+```
+Text → Text Encoder → Cross-Attention Conditioning
+                           ↓
+Speaker ID → Context Emb → Decoder (AR) → Codec Tokens → NanoCodec → Audio
+```
+
+**Key specs:**
+- 357M parameters total
+- 9 languages (en, es, de, fr, it, vi, zh, hi, ja)
+- 5 built-in speakers (John, Sofia, Aria, Jason, Leo)
+- 22kHz output audio
+- NanoCodec: 8 codebooks, 2016 codes each, 21.5fps
+
+## CoreML Model Split
+
+| Model | Purpose | Called |
+|-------|---------|-------|
+| `text_encoder.mlpackage` | Encode text → conditioning | Once per utterance |
+| `decoder_prefill.mlpackage` | Batch-prefill speaker context into KV cache | Once per utterance (optional) |
+| `decoder_step.mlpackage` | Single AR step with KV cache | ~50-200× per utterance |
+| `nanocodec_decoder.mlpackage` | Codec tokens → audio | Once per utterance |
+
+## Setup
+
+```bash
+uv sync
+
+# For NeMo (required for model extraction & tokenization):
+uv sync --extra nemo
+
+# For Mandarin pypinyin/jieba export:
+uv pip install pypinyin jieba
+```
+
+## Generation Steps
+
+All constants and models must be generated before running the iOS app or the Python generation pipeline. Run these in order:
+
+```bash
+# 1. Export constants (embeddings, speaker data, model config)
+#    Requires: NeMo
+#    Produces: constants/*.npy, constants/*.json
+python export_constants.py
+
+# 1b. Export local transformer weights as individual .npy files
+#     Requires: NeMo (or use --from-pt with previously extracted checkpoint)
+#     Produces: constants/local_transformer/*.npy
+python export_local_transformer.py
+
+# 2. Export tokenizer dictionaries (phoneme dicts, token2id, heteronyms)
+#    Requires: NeMo
+#    Produces: constants/*_token2id.json, *_phoneme_dict.json, *_heteronyms.json,
+#              mandarin_phoneme_*.json, japanese_phoneme_*.json,
+#              tokenizer_metadata.json, tokenizer_references.json
+python export_tokenizers.py
+
+# 3. Create English alias files expected by Swift EnglishTokenizer
+#    Requires: Step 2 complete
+#    Produces: english_phoneme_dict.json, english_token2id.json
+python export_tokenizer_aliases.py
+
+# 4. Export Mandarin pypinyin and jieba dictionaries
+#    Requires: pypinyin, jieba
+#    Produces: mandarin_pypinyin_char_dict.json, mandarin_pypinyin_phrase_dict.json,
+#              mandarin_jieba_dict.json
+python export_pypinyin.py
+
+# 5. Convert models to CoreML (.mlpackage)
+#    Requires: NeMo, coremltools
+#    Produces: build/*.mlpackage
+python convert/convert_text_encoder.py
+python convert/convert_decoder_prefill.py
+python convert/convert_decoder_step.py
+python convert/convert_nanocodec.py
+
+# 6. Compile for iOS/macOS (.mlpackage → .mlmodelc)
+#    Requires: Xcode command line tools
+#    Produces: compiled/*.mlmodelc
+python compile_mlmodelc.py
+
+# 7. Test generation on macOS
+python generate_coreml.py "Hello, this is Magpie TTS running on CoreML." --speaker 1
+```
+
+### CoreML Models
+
+| File | Required | Notes |
+|------|----------|-------|
+| `text_encoder.mlmodelc` | Yes | Falls back to `.mlpackage` if `.mlmodelc` absent |
+| `decoder_step.mlmodelc` | Yes | Falls back to `.mlpackage` |
+| `nanocodec_decoder.mlmodelc` | Yes | Falls back to `.mlpackage` |
+| `decoder_prefill.mlmodelc` | No | If absent, prefill runs step-by-step (slower) |
+
+### Core Constants (always loaded)
+
+| File | Generated by |
+|------|-------------|
+| `constants.json` | `export_constants.py` |
+| `speaker_info.json` | `export_constants.py` |
+| `tokenizer_metadata.json` | `export_tokenizers.py` |
+| `speaker_0.npy` .. `speaker_4.npy` | `export_constants.py` |
+| `audio_embedding_0.npy` .. `audio_embedding_7.npy` | `export_constants.py` |
+
+### Local Transformer Weights (always loaded)
+
+All files in `constants/local_transformer/`, generated by `export_local_transformer.py`:
+
+| File | Shape |
+|------|-------|
+| `in_proj_weight.npy` | (256, 768) |
+| `in_proj_bias.npy` | (256,) |
+| `pos_emb.npy` | (10, 256) |
+| `norm1_weight.npy`, `norm2_weight.npy` | (256,) |
+| `sa_qkv_weight.npy` | (768, 256) |
+| `sa_o_weight.npy` | (256, 256) |
+| `ffn_conv1_weight.npy` | (1024, 256, 1) |
+| `ffn_conv2_weight.npy` | (256, 1024, 1) |
+| `out_proj_{0-7}_weight.npy` | 8 x (2024, 256) |
+| `out_proj_{0-7}_bias.npy` | 8 x (2024,) |
+
+### Language Tokenizer Data (lazy-loaded per language)
+
+#### English
+| File | Generated by |
+|------|-------------|
+| `english_phoneme_dict.json` | `export_tokenizer_aliases.py` (alias of `english_phoneme_phoneme_dict.json`) |
+| `english_token2id.json` | `export_tokenizer_aliases.py` (alias of `english_phoneme_token2id.json`) |
+
+#### Spanish
+| File | Generated by |
+|------|-------------|
+| `spanish_phoneme_phoneme_dict.json` | `export_tokenizers.py` |
+| `spanish_phoneme_token2id.json` | `export_tokenizers.py` |
+
+#### German
+| File | Generated by |
+|------|-------------|
+| `german_phoneme_phoneme_dict.json` | `export_tokenizers.py` |
+| `german_phoneme_token2id.json` | `export_tokenizers.py` |
+| `german_phoneme_heteronyms.json` | `export_tokenizers.py` (optional, graceful fallback) |
+
+#### Hindi
+| File | Generated by |
+|------|-------------|
+| `hindi_chartokenizer_token2id.json` | `export_tokenizers.py` |
+
+#### Mandarin
+| File | Generated by |
+|------|-------------|
+| `mandarin_jieba_dict.json` | `extras/export_pypinyin.py` |
+| `mandarin_pypinyin_char_dict.json` | `extras/export_pypinyin.py` |
+| `mandarin_pypinyin_phrase_dict.json` | `extras/export_pypinyin.py` |
+| `mandarin_phoneme_pinyin_dict.json` | `export_tokenizers.py` |
+| `mandarin_phoneme_tone_dict.json` | `export_tokenizers.py` |
+| `mandarin_phoneme_ascii_letter_dict.json` | `export_tokenizers.py` |
+| `mandarin_phoneme_token2id.json` | `export_tokenizers.py` |
+
+#### French / Italian / Vietnamese
+No data files needed — these use ByT5 byte-level encoding (purely algorithmic).
+
+#### Japanese
+| File | Generated by |
+|------|-------------|
+| `japanese_phoneme_token2id.json` | `export_tokenizers.py` |
+| `japanese_phoneme_punctuation.json` | `export_tokenizers.py` |
+| `japanese_phoneme_ascii_letter_dict.json` | `export_tokenizers.py` |
+| `open_jtalk_dic/` | [OpenJTalk dictionary](https://sourceforge.net/projects/open-jtalk/files/Dictionary/open_jtalk_dic-1.11/) (UTF-8) |
+
+Japanese G2P uses [OpenJTalk](https://github.com/r9y9/open_jtalk) compiled as a static library via XCFramework. See [Building OpenJTalk](#building-openjtalk) below.
+
+## Python File Layout
+
+### Pipeline Scripts (root)
+
+| Script | Purpose |
+|--------|---------|
+| `export_constants.py` | Export embeddings, speaker data, model config |
+| `export_local_transformer.py` | Export local transformer weights as individual .npy files |
+| `export_tokenizers.py` | Export per-language tokenizer dictionaries from NeMo |
+| `export_tokenizer_aliases.py` | Create short-name English files expected by Swift |
+| `extract_models.py` | Inspect architecture or extract components from .nemo checkpoint |
+| `generate_coreml.py` | End-to-end TTS generation using converted CoreML models |
+| `compile_mlmodelc.py` | Compile `.mlpackage` → `.mlmodelc` via `xcrun coremlcompiler` |
+
+### Conversion Scripts (`convert/`)
+
+| Script | Purpose |
+|--------|---------|
+| `convert_text_encoder.py` | Convert text encoder to CoreML |
+| `convert_decoder_prefill.py` | Convert batch context prefill to CoreML |
+| `convert_decoder_step.py` | Convert single-step decoder (with KV cache) to CoreML |
+| `convert_nanocodec.py` | Convert NanoCodec vocoder to CoreML |
+
+### Traceable Wrappers (`traceable/`)
+
+PyTorch `nn.Module` wrappers that make NeMo model components traceable for CoreML conversion. They replace implicit state (e.g. in-place KV cache updates) with explicit tensor I/O.
+
+## Building OpenJTalk
+
+Japanese text input requires the OpenJTalk G2P library, compiled as a static XCFramework. The build script handles cloning, cross-compiling, and packaging.
+
+### Prerequisites
+
+- Xcode with iOS SDK installed
+- CMake (`brew install cmake`)
+
+### Build
+
+```bash
+./build_openjtalk.sh
+```
+
+This produces:
+- `ios/OpenJTalk.xcframework/` — static libraries for iOS device, iOS simulator, and macOS (arm64)
+- `ios/COpenJTalk/` — Swift module map (`module.modulemap` + header)
+
+The XCFramework is linked manually via `LIBRARY_SEARCH_PATHS` and `SWIFT_INCLUDE_PATHS` in `project.yml` (not as an Xcode framework dependency) to avoid module map collisions with the NemoTextProcessing XCFramework.
+
+### MeCab Dictionary
+
+Japanese tokenization also requires the OpenJTalk MeCab dictionary. Download and extract to `ios/constants/open_jtalk_dic/`:
+
+```bash
+curl -L "https://sourceforge.net/projects/open-jtalk/files/Dictionary/open_jtalk_dic-1.11/open_jtalk_dic_utf_8-1.11.tar.gz/download" | tar xz
+mv open_jtalk_dic_utf_8-1.11/* ios/constants/open_jtalk_dic/
+```
+
+The dictionary (~102MB) contains `sys.dic`, `matrix.bin`, `char.bin`, `unk.dic`, and related files needed by MeCab's morphological analyzer.
+
+## Speakers
+
+| Index | Name | Description |
+|-------|------|-------------|
+| 0 | John | Male (LibriVox, public domain) |
+| 1 | Sofia | Female (proprietary) |
+| 2 | Aria | Female (proprietary) |
+| 3 | Jason | Male (proprietary) |
+| 4 | Leo | Male (proprietary) |
+
+## References
+
+- [Magpie TTS Model Card](https://huggingface.co/nvidia/magpie_tts_multilingual_357m)
+- [NanoCodec](https://huggingface.co/nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps)
+- [NeMo Framework](https://github.com/NVIDIA/NeMo)
+- [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
diff --git a/models/tts/magpie/coreml/build_openjtalk.sh b/models/tts/magpie/coreml/build_openjtalk.sh
new file mode 100755
index 0000000..7935f62
--- /dev/null
+++ b/models/tts/magpie/coreml/build_openjtalk.sh
@@ -0,0 +1,205 @@
+#!/bin/bash
+#
+# Build OpenJTalk as an XCFramework for iOS and macOS.
+#
+# Usage: ./build_openjtalk.sh
+#
+# Prerequisites:
+#   - Xcode with iOS SDK installed
+#   - CMake (brew install cmake)
+#   - Git
+#
+# Output: ios/OpenJTalk.xcframework/
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+BUILD_ROOT="${SCRIPT_DIR}/build_openjtalk"
+OPENJTALK_REPO="${BUILD_ROOT}/open_jtalk"
+OPENJTALK_DIR="${OPENJTALK_REPO}/src"
+WRAPPER_H="${SCRIPT_DIR}/openjtalk_wrapper.h"
+WRAPPER_CPP="${SCRIPT_DIR}/openjtalk_wrapper.cpp"
+OUTPUT_DIR="${SCRIPT_DIR}/ios/OpenJTalk.xcframework"
+
+# Minimum deployment targets
+IOS_DEPLOYMENT_TARGET="17.0"
+MACOS_DEPLOYMENT_TARGET="14.0"
+
+# Targets to build
+TARGETS=(
+    "ios-arm64"
+    "ios-arm64-simulator"
+    "macos-arm64"
+)
+
+echo "=== Building OpenJTalk XCFramework ==="
+
+# Step 1: Clone open_jtalk if needed
+if [ ! -d "$OPENJTALK_REPO" ]; then
+    echo "Cloning r9y9/open_jtalk..."
+    mkdir -p "$BUILD_ROOT"
+    git clone --depth 1 https://github.com/r9y9/open_jtalk.git "$OPENJTALK_REPO"
+else
+    echo "Using existing open_jtalk at $OPENJTALK_REPO"
+fi
+
+# Step 2: Build for each target
+build_target() {
+    local target=$1
+    local build_dir="${BUILD_ROOT}/build-${target}"
+    local install_dir="${BUILD_ROOT}/install-${target}"
+
+    echo ""
+    echo "--- Building for ${target} ---"
+
+    rm -rf "$build_dir"
+    mkdir -p "$build_dir"
+
+    local cmake_args=(
+        -DCMAKE_BUILD_TYPE=Release
+        -DBUILD_SHARED_LIBS=OFF
+        -DCMAKE_INSTALL_PREFIX="$install_dir"
+    )
+
+    case "$target" in
+        ios-arm64)
+            local sdk_path
+            sdk_path=$(xcrun --sdk iphoneos --show-sdk-path)
+            cmake_args+=(
+                -DCMAKE_SYSTEM_NAME=iOS
+                -DCMAKE_OSX_ARCHITECTURES=arm64
+                -DCMAKE_OSX_DEPLOYMENT_TARGET="$IOS_DEPLOYMENT_TARGET"
+                -DCMAKE_OSX_SYSROOT="$sdk_path"
+                -DCMAKE_C_FLAGS="-fembed-bitcode"
+                -DCMAKE_CXX_FLAGS="-fembed-bitcode"
+            )
+            ;;
+        ios-arm64-simulator)
+            local sdk_path
+            sdk_path=$(xcrun --sdk iphonesimulator --show-sdk-path)
+            cmake_args+=(
+                -DCMAKE_SYSTEM_NAME=iOS
+                -DCMAKE_OSX_ARCHITECTURES=arm64
+                -DCMAKE_OSX_DEPLOYMENT_TARGET="$IOS_DEPLOYMENT_TARGET"
+                -DCMAKE_OSX_SYSROOT="$sdk_path"
+                -DCMAKE_C_FLAGS="-target arm64-apple-ios${IOS_DEPLOYMENT_TARGET}-simulator"
+                -DCMAKE_CXX_FLAGS="-target arm64-apple-ios${IOS_DEPLOYMENT_TARGET}-simulator"
+            )
+            ;;
+        macos-arm64)
+            cmake_args+=(
+                -DCMAKE_SYSTEM_NAME=Darwin
+                -DCMAKE_OSX_ARCHITECTURES=arm64
+                -DCMAKE_OSX_DEPLOYMENT_TARGET="$MACOS_DEPLOYMENT_TARGET"
+            )
+            ;;
+    esac
+
+    # Configure
+    cmake -S "$OPENJTALK_DIR" -B "$build_dir" "${cmake_args[@]}"
+
+    # Build
+    cmake --build "$build_dir" --config Release -j "$(sysctl -n hw.ncpu)"
+
+    # Find the built static library
+    local lib_path
+    lib_path=$(find "$build_dir" -name "libopenjtalk.a" | head -1)
+    if [ -z "$lib_path" ]; then
+        echo "ERROR: libopenjtalk.a not found in $build_dir"
+        find "$build_dir" -name "*.a" -print
+        exit 1
+    fi
+
+    # Compile the wrapper
+    local wrapper_obj="${build_dir}/openjtalk_wrapper.o"
+    local include_dirs=(
+        -I"${OPENJTALK_DIR}/njd"
+        -I"${OPENJTALK_DIR}/mecab/src"
+        -I"${OPENJTALK_DIR}/jpcommon"
+        -I"${OPENJTALK_DIR}/text2mecab"
+        -I"${OPENJTALK_DIR}/mecab2njd"
+        -I"${OPENJTALK_DIR}/njd2jpcommon"
+        -I"${OPENJTALK_DIR}/njd_set_pronunciation"
+        -I"${OPENJTALK_DIR}/njd_set_digit"
+        -I"${OPENJTALK_DIR}/njd_set_accent_phrase"
+        -I"${OPENJTALK_DIR}/njd_set_accent_type"
+        -I"${OPENJTALK_DIR}/njd_set_unvoiced_vowel"
+        -I"${OPENJTALK_DIR}/njd_set_long_vowel"
+    )
+
+    local cc_flags=()
+    case "$target" in
+        ios-arm64)
+            cc_flags+=(-isysroot "$(xcrun --sdk iphoneos --show-sdk-path)")
+            cc_flags+=(-target arm64-apple-ios${IOS_DEPLOYMENT_TARGET})
+            ;;
+        ios-arm64-simulator)
+            cc_flags+=(-isysroot "$(xcrun --sdk iphonesimulator --show-sdk-path)")
+            cc_flags+=(-target arm64-apple-ios${IOS_DEPLOYMENT_TARGET}-simulator)
+            ;;
+        macos-arm64)
+            cc_flags+=(-target arm64-apple-macos${MACOS_DEPLOYMENT_TARGET})
+            ;;
+    esac
+
+    xcrun clang++ -c "$WRAPPER_CPP" -o "$wrapper_obj" \
+        "${include_dirs[@]}" "${cc_flags[@]}" \
+        -std=c++17 -O2
+
+    # Merge wrapper into static library
+    local merged_lib="${build_dir}/libopenjtalk_merged.a"
+    libtool -static -o "$merged_lib" "$lib_path" "$wrapper_obj"
+
+    # Store for xcframework creation
+    mkdir -p "$install_dir"
+    cp "$merged_lib" "$install_dir/libopenjtalk.a"
+
+    echo "Built: $install_dir/libopenjtalk.a"
+}
+
+for target in "${TARGETS[@]}"; do
+    build_target "$target"
+done
+
+# Step 3: Create xcframework (static libs only, no headers — headers are in COpenJTalk/)
+echo ""
+echo "--- Creating XCFramework ---"
+
+# Remove old xcframework
+rm -rf "$OUTPUT_DIR"
+
+# Create xcframework without headers (avoids module.modulemap collision with NemoTextProcessing)
+# Headers are provided separately via ios/COpenJTalk/ and SWIFT_INCLUDE_PATHS
+xcodebuild -create-xcframework \
+    -library "${BUILD_ROOT}/install-ios-arm64/libopenjtalk.a" \
+    -library "${BUILD_ROOT}/install-ios-arm64-simulator/libopenjtalk.a" \
+    -library "${BUILD_ROOT}/install-macos-arm64/libopenjtalk.a" \
+    -output "$OUTPUT_DIR"
+
+# Step 4: Create COpenJTalk module directory for Swift import
+COPENJTALK_DIR="${SCRIPT_DIR}/ios/COpenJTalk"
+mkdir -p "$COPENJTALK_DIR"
+cp "$WRAPPER_H" "$COPENJTALK_DIR/openjtalk_wrapper.h"
+cat > "$COPENJTALK_DIR/module.modulemap" << 'EOF'
+module COpenJTalk {
+    header "openjtalk_wrapper.h"
+    export *
+}
+EOF
+
+echo ""
+echo "=== XCFramework created at: $OUTPUT_DIR ==="
+echo "=== COpenJTalk module at: $COPENJTALK_DIR ==="
+echo ""
+
+# Verify
+echo "Contents:"
+find "$OUTPUT_DIR" -type f | sort
+echo ""
+echo "Architecture check:"
+for target in "${TARGETS[@]}"; do
+    lib=$(find "$OUTPUT_DIR" -path "*${target}*" -name "libopenjtalk.a" | head -1)
+    if [ -n "$lib" ]; then
+        echo "  ${target}: $(lipo -info "$lib" 2>/dev/null)"
+    fi
+done
diff --git a/models/tts/magpie/coreml/compile_mlmodelc.py b/models/tts/magpie/coreml/compile_mlmodelc.py
new file mode 100644
index 0000000..ce19cf0
--- /dev/null
+++ b/models/tts/magpie/coreml/compile_mlmodelc.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""Compile Core ML packages into ``.mlmodelc`` bundles via ``xcrun``.
+
+This script walks through the default Parakeet CoreML directories, finds
+all ``*.mlpackage`` bundles, and compiles each of them with
+``xcrun coremlcompiler`` into ``./compiled`` while preserving the
+relative directory structure.
+"""
+from __future__ import annotations
+
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parent
+OUTPUT_ROOT = BASE_DIR / "compiled"
+SOURCE_DIRS = [BASE_DIR ]
+
+
+def ensure_coremlcompiler() -> None:
+    """Ensure ``xcrun coremlcompiler`` is available for the active Xcode."""
+    xcrun_path = shutil.which("xcrun")
+    if xcrun_path is None:
+        print("Error: 'xcrun' not found on PATH. Install Xcode command line tools.", file=sys.stderr)
+        sys.exit(1)
+
+    try:
+        subprocess.run([
+            xcrun_path,
+            "--find",
+            "coremlcompiler",
+        ], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    except subprocess.CalledProcessError:
+        print("Error: 'coremlcompiler' not found via xcrun. Check your Xcode installation.", file=sys.stderr)
+        sys.exit(1)
+
+
+def gather_packages() -> list[Path]:
+    """Return a list of all ``*.mlpackage`` bundles under the source dirs."""
+    packages: list[Path] = []
+    for source in SOURCE_DIRS:
+        if not source.exists():
+            print(f"Warning: {source.relative_to(BASE_DIR)} does not exist; skipping", file=sys.stderr)
+            continue
+        packages.extend(source.rglob("*.mlpackage"))
+    return packages
+
+
+def compile_package(package: Path) -> None:
+    """Compile a single ``.mlpackage`` bundle using ``xcrun coremlcompiler``."""
+    relative_pkg = package.relative_to(BASE_DIR)
+    output_dir = OUTPUT_ROOT / relative_pkg.parent
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = output_dir / f"{package.stem}.mlmodelc"
+
+    if output_path.exists():
+        shutil.rmtree(output_path)
+
+    cmd = [
+        "xcrun",
+        "coremlcompiler",
+        "compile",
+        str(package),
+        str(output_dir),
+    ]
+
+    print(f"Compiling {relative_pkg} -> {output_path.relative_to(BASE_DIR)}")
+    subprocess.run(cmd, check=True)
+
+
+def main() -> None:
+    ensure_coremlcompiler()
+    packages = gather_packages()
+
+    if not packages:
+        print("No .mlpackage bundles found to compile.")
+        return
+
+    for package in packages:
+        try:
+            compile_package(package)
+        except subprocess.CalledProcessError as exc:
+            print(f"Failed to compile {package}: {exc}", file=sys.stderr)
+            sys.exit(exc.returncode)
+
+    print(f"Finished compiling {len(packages)} package(s) into {OUTPUT_ROOT.relative_to(BASE_DIR)}.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/tts/magpie/coreml/convert_decoder_prefill.py b/models/tts/magpie/coreml/convert_decoder_prefill.py
new file mode 100644
index 0000000..b17397b
--- /dev/null
+++ b/models/tts/magpie/coreml/convert_decoder_prefill.py
@@ -0,0 +1,139 @@
+"""Convert decoder prefill model to CoreML.
+
+Processes all speaker context tokens (T_ctx=110) in a single forward pass,
+replacing 110 sequential decoder_step calls during prefill.
+
+Usage:
+    python convert/convert_decoder_prefill.py [--nemo-path /path/to/model.nemo]
+"""
+import sys
+import os
+import argparse
+import json
+
+import torch
+import numpy as np
+import coremltools as ct
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from traceable.traceable_decoder_prefill import TraceableDecoderPrefill
+
+
+def convert_decoder_prefill(nemo_path=None, max_seq_len=512, max_text_len=256,
+                            t_ctx=110, output_path="build/decoder_prefill.mlpackage"):
+    # Load model
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    cfg = model.cfg
+    dec_cfg = dict(cfg.decoder)
+    d_model = dec_cfg["d_model"]
+    n_layers = dec_cfg["n_layers"]
+    sa_n_heads = dec_cfg["sa_n_heads"]
+    d_head = d_model // sa_n_heads
+
+    # Read T_ctx from speaker_info if not specified
+    constants_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "constants")
+    si_path = os.path.join(constants_dir, "speaker_info.json")
+    if os.path.exists(si_path):
+        with open(si_path) as f:
+            si = json.load(f)
+        t_ctx_from_file = si.get("T", t_ctx)
+        if t_ctx_from_file != t_ctx:
+            print(f"Using T_ctx={t_ctx_from_file} from speaker_info.json (was {t_ctx})")
+            t_ctx = t_ctx_from_file
+
+    # Create traceable prefill model
+    print(f"Creating traceable prefill model (T_ctx={t_ctx})...")
+    prefill = TraceableDecoderPrefill.from_magpie(model, t_ctx=t_ctx)
+    prefill.eval()
+
+    # Example inputs
+    B = 1
+    audio_embed = torch.randn(B, t_ctx, d_model)
+    encoder_output = torch.randn(B, max_text_len, d_model)
+    encoder_mask = torch.ones(B, max_text_len, dtype=torch.bool)
+
+    # Trace
+    print("Tracing model...")
+    with torch.no_grad():
+        traced = torch.jit.trace(prefill, (audio_embed, encoder_output, encoder_mask))
+
+    # Verify trace output
+    with torch.no_grad():
+        outputs = traced(audio_embed, encoder_output, encoder_mask)
+    print(f"Trace outputs: {len(outputs)} caches")
+    for i, cache in enumerate(outputs):
+        print(f"  cache{i}: {cache.shape}")
+
+    # Convert to CoreML
+    print("Converting to CoreML...")
+    inputs = [
+        ct.TensorType(name="audio_embed", shape=(1, t_ctx, d_model)),
+        ct.TensorType(name="encoder_output", shape=(1, max_text_len, d_model)),
+        ct.TensorType(name="encoder_mask", shape=(1, max_text_len), dtype=np.bool_),
+    ]
+
+    mlmodel = ct.convert(
+        traced,
+        inputs=inputs,
+        convert_to="mlprogram",
+        compute_precision=ct.precision.FLOAT16,
+        minimum_deployment_target=ct.target.iOS17,
+    )
+
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    mlmodel.save(output_path)
+    print(f"Saved to {output_path}")
+
+    # Print output spec
+    spec = mlmodel.get_spec()
+    print(f"\n=== OUTPUTS ({len(spec.description.output)}) ===")
+    for out in spec.description.output:
+        if out.type.HasField("multiArrayType"):
+            shape = list(out.type.multiArrayType.shape)
+            print(f"  {out.name}: {shape}")
+
+    # Quick test
+    print("\nTesting CoreML model...")
+    coreml_model = ct.models.MLModel(output_path, compute_units=ct.ComputeUnit.CPU_ONLY)
+
+    test_inputs = {
+        "audio_embed": np.random.randn(1, t_ctx, d_model).astype(np.float32),
+        "encoder_output": np.random.randn(1, max_text_len, d_model).astype(np.float32),
+        "encoder_mask": np.ones((1, max_text_len), dtype=np.float32),
+    }
+
+    out = coreml_model.predict(test_inputs)
+    print(f"Output keys: {len(out)}")
+    for k, v in sorted(out.items()):
+        if isinstance(v, np.ndarray):
+            print(f"  {k}: shape={v.shape}")
+
+    # Verify cache shapes match decoder_step format
+    expected_cache_shape = (2, 1, max_seq_len, sa_n_heads, d_head)
+    for k, v in out.items():
+        if isinstance(v, np.ndarray) and len(v.shape) == 5:
+            assert v.shape == expected_cache_shape, \
+                f"Cache {k} shape {v.shape} != expected {expected_cache_shape}"
+    print(f"All cache shapes match decoder_step format: {expected_cache_shape}")
+    print("Done!")
+
+    return output_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nemo-path", type=str, default=None)
+    parser.add_argument("--max-seq-len", type=int, default=512)
+    parser.add_argument("--max-text-len", type=int, default=256)
+    parser.add_argument("--t-ctx", type=int, default=110)
+    parser.add_argument("--output", type=str, default="build/decoder_prefill.mlpackage")
+    args = parser.parse_args()
+    convert_decoder_prefill(args.nemo_path, args.max_seq_len, args.max_text_len,
+                            args.t_ctx, args.output)
diff --git a/models/tts/magpie/coreml/convert_decoder_step.py b/models/tts/magpie/coreml/convert_decoder_step.py
new file mode 100644
index 0000000..52037f4
--- /dev/null
+++ b/models/tts/magpie/coreml/convert_decoder_step.py
@@ -0,0 +1,135 @@
+"""Convert decoder step model to CoreML.
+
+Usage:
+    python convert/convert_decoder_step.py [--nemo-path /path/to/model.nemo]
+"""
+import sys
+import os
+import argparse
+
+import torch
+import numpy as np
+import coremltools as ct
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from traceable.traceable_decoder_step import TraceableDecoderStep
+
+
+def convert_decoder_step(nemo_path=None, max_seq_len=512, max_text_len=256,
+                         output_path="build/decoder_step.mlpackage"):
+    # Load model
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    cfg = model.cfg
+    dec_cfg = dict(cfg.decoder)
+    d_model = dec_cfg["d_model"]
+    n_layers = dec_cfg["n_layers"]
+    sa_n_heads = dec_cfg["sa_n_heads"]
+    d_head = d_model // sa_n_heads
+
+    # Create traceable decoder step
+    print("Creating traceable decoder step...")
+    decoder = TraceableDecoderStep.from_magpie(model)
+    decoder.eval()
+
+    # Example inputs
+    B = 1
+    T_enc = max_text_len
+    H = sa_n_heads
+    D = d_head
+
+    audio_embed = torch.randn(B, 1, d_model)
+    encoder_output = torch.randn(B, T_enc, d_model)
+    encoder_mask = torch.ones(B, T_enc, dtype=torch.bool)
+
+    # Flat cache and position args
+    caches = []
+    positions = []
+    for i in range(n_layers):
+        cache = torch.zeros(2, B, max_seq_len, H, D)
+        # Simulate some prefilled context
+        cache[:, :, :10, :, :] = torch.randn(2, B, 10, H, D) * 0.1
+        caches.append(cache)
+        positions.append(torch.tensor([10.0]))
+
+    # Build flat argument tuple
+    example_inputs = (audio_embed, encoder_output, encoder_mask)
+    for i in range(n_layers):
+        example_inputs = example_inputs + (caches[i], positions[i])
+
+    # Trace
+    print("Tracing model...")
+    with torch.no_grad():
+        traced = torch.jit.trace(decoder, example_inputs)
+
+    # Convert to CoreML
+    print("Converting to CoreML...")
+    inputs = [
+        ct.TensorType(name="audio_embed", shape=(1, 1, d_model)),
+        ct.TensorType(name="encoder_output", shape=(1, T_enc, d_model)),
+        ct.TensorType(name="encoder_mask", shape=(1, T_enc), dtype=np.bool_),
+    ]
+    for i in range(n_layers):
+        inputs.append(ct.TensorType(name=f"cache{i}", shape=(2, 1, max_seq_len, H, D)))
+        inputs.append(ct.TensorType(name=f"position{i}", shape=(1,)))
+
+    mlmodel = ct.convert(
+        traced,
+        inputs=inputs,
+        convert_to="mlprogram",
+        compute_precision=ct.precision.FLOAT16,
+        minimum_deployment_target=ct.target.iOS17,
+    )
+
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    mlmodel.save(output_path)
+
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    mlmodel.save(output_path)
+    print(f"Saved to {output_path}")
+
+    # Print output spec
+    spec = mlmodel.get_spec()
+    print("\n=== OUTPUTS ===")
+    for out in spec.description.output:
+        if out.type.HasField("multiArrayType"):
+            shape = list(out.type.multiArrayType.shape)
+            print(f"  {out.name}: {shape}")
+
+    # Quick test
+    print("\nTesting CoreML model...")
+    coreml_model = ct.models.MLModel(output_path, compute_units=ct.ComputeUnit.CPU_ONLY)
+
+    test_inputs = {
+        "audio_embed": np.random.randn(1, 1, d_model).astype(np.float32),
+        "encoder_output": np.random.randn(1, T_enc, d_model).astype(np.float32),
+        "encoder_mask": np.ones((1, T_enc), dtype=np.float32),
+    }
+    for i in range(n_layers):
+        test_inputs[f"cache{i}"] = np.zeros((2, 1, max_seq_len, H, D), dtype=np.float32)
+        test_inputs[f"position{i}"] = np.array([0.0], dtype=np.float32)
+
+    out = coreml_model.predict(test_inputs)
+    print(f"Output keys: {len(out)}")
+    for k, v in sorted(out.items()):
+        if isinstance(v, np.ndarray):
+            print(f"  {k}: shape={v.shape}")
+    print("Done!")
+
+    return output_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nemo-path", type=str, default=None)
+    parser.add_argument("--max-seq-len", type=int, default=512)
+    parser.add_argument("--max-text-len", type=int, default=256)
+    parser.add_argument("--output", type=str, default="build/decoder_step.mlpackage")
+    args = parser.parse_args()
+    convert_decoder_step(args.nemo_path, args.max_seq_len, args.max_text_len, args.output)
diff --git a/models/tts/magpie/coreml/convert_nanocodec.py b/models/tts/magpie/coreml/convert_nanocodec.py
new file mode 100644
index 0000000..f1aa799
--- /dev/null
+++ b/models/tts/magpie/coreml/convert_nanocodec.py
@@ -0,0 +1,326 @@
+"""Convert NanoCodec decoder to CoreML.
+
+The NanoCodec decoder converts discrete codec tokens back to audio waveform.
+It's a causal HiFi-GAN-based vocoder.
+
+The main conversion challenge is Snake activation, which uses a TorchScript
+function internally. We replace it with a plain PyTorch equivalent before tracing.
+
+Usage:
+    python convert/convert_nanocodec.py [--nemo-path /path/to/model.nemo]
+"""
+import sys
+import os
+import argparse
+
+import torch
+import torch.nn as nn
+import numpy as np
+import coremltools as ct
+
+
+def _snake_plain(x, alpha):
+    """Plain PyTorch snake activation: x + (1/alpha) * sin^2(alpha * x)."""
+    return x + (1.0 / (alpha + 1e-9)) * torch.sin(alpha * x).pow(2)
+
+
+class TraceableSnake(nn.Module):
+    """Snake activation without TorchScript — traceable by coremltools."""
+
+    def __init__(self, original_snake):
+        super().__init__()
+        self.alpha = original_snake.alpha
+
+    def forward(self, x):
+        return _snake_plain(x, self.alpha)
+
+
+class TraceableHalfSnake(nn.Module):
+    """HalfSnake activation without TorchScript."""
+
+    def __init__(self, original):
+        super().__init__()
+        self.snake_channels = original.snake_channels
+        self.snake_act = TraceableSnake(original.snake_act)
+        self.lrelu = nn.LeakyReLU()
+
+    def forward(self, x):
+        snake_out = self.snake_act(x[:, :self.snake_channels, :])
+        lrelu_out = self.lrelu(x[:, self.snake_channels:, :])
+        return torch.cat([snake_out, lrelu_out], dim=1)
+
+
+def replace_snake_activations(module):
+    """Recursively replace Snake/HalfSnake with traceable versions."""
+    replaced = 0
+    for name, child in module.named_children():
+        cls_name = type(child).__name__
+        if cls_name == 'HalfSnake':
+            setattr(module, name, TraceableHalfSnake(child))
+            replaced += 1
+        elif cls_name == 'Snake':
+            setattr(module, name, TraceableSnake(child))
+            replaced += 1
+        else:
+            replaced += replace_snake_activations(child)
+    return replaced
+
+
+def _patch_causal_convs(module):
+    """Make all CausalConv1d modules CoreML-traceable.
+
+    NeMo's CausalConv1dNorm stores kernel_size, stride, padding_total as 0-dim
+    tensor buffers and uses torch.ceil().to(int64) / .long() in
+    _get_extra_padding_for_conv1d. These produce `aten::Int(tensor)` ops that
+    coremltools cannot convert.
+
+    Fix:
+    1. Replace tensor buffers with plain Python ints
+    2. Replace _get_extra_padding_for_conv1d with pure-Python-arithmetic version
+    """
+    import math
+    import types
+
+    def _safe_extra_padding(self, hidden_states):
+        # For fixed-size inputs (max_frames=256), extra padding is always 0.
+        # Returning a constant avoids symbolic shape arithmetic that produces
+        # untraceable aten::Int ops in the traced graph.
+        return 0
+
+    for name, child in module.named_modules():
+        # Convert tensor buffers to plain ints
+        for attr in ('padding_total', 'kernel_size', 'stride'):
+            val = getattr(child, attr, None)
+            if isinstance(val, torch.Tensor) and val.ndim == 0:
+                int_val = int(val.item())
+                if attr in dict(child.named_buffers()):
+                    delattr(child, attr)
+                child.__dict__[attr] = int_val
+
+        # Replace the problematic method
+        if hasattr(child, '_get_extra_padding_for_conv1d'):
+            child._get_extra_padding_for_conv1d = types.MethodType(_safe_extra_padding, child)
+
+
+def _patch_mask_noop():
+    """Replace mask_sequence_tensor with a no-op in NeMo's audio codec module.
+
+    Since we use fixed-size inputs (max_frames), all positions are valid and
+    masking is unnecessary. This eliminates the einops rearrange and comparison
+    ops inside mask_sequence_tensor that produce untraceable symbolic int ops.
+    """
+    import nemo.collections.tts.modules.audio_codec_modules as acm
+    acm.mask_sequence_tensor = lambda tensor, lengths: tensor
+
+
+def _disable_typecheck():
+    """Disable NeMo's @typecheck decorator so we can pass None for input_len."""
+    from nemo.core.classes.common import typecheck
+    typecheck.set_typecheck_enabled(False)
+
+
+def _patch_conv_transpose_unpad(module):
+    """Patch CausalConvTranspose1dNorm to use negative indexing for unpadding.
+
+    The original code does:
+        end = hidden_states.shape[-1] - self.padding_right
+        hidden_states = hidden_states[..., self.padding_left : end]
+
+    This produces aten::size → prim::NumToTensor → aten::Int in the traced graph,
+    which coremltools cannot convert. Using negative indexing avoids querying shape:
+        hidden_states = hidden_states[..., self.padding_left : -self.padding_right]
+    """
+    import types
+
+    def _forward_neg_index(self, inputs, input_len):
+        hidden_states = self.conv(inputs)
+        # Use negative indexing to avoid aten::size → aten::Int
+        if self.padding_right > 0:
+            hidden_states = hidden_states[..., self.padding_left : -self.padding_right]
+        elif self.padding_left > 0:
+            hidden_states = hidden_states[..., self.padding_left :]
+        hidden_states = self.activation(hidden_states)
+        return hidden_states  # mask_sequence_tensor already patched to no-op
+
+    import nemo.collections.tts.modules.audio_codec_modules as acm
+    for name, child in module.named_modules():
+        if isinstance(child, acm.CausalConvTranspose1dNorm):
+            child.forward = types.MethodType(_forward_neg_index, child)
+
+
+class TraceableNanoCodecDecoder(nn.Module):
+    """Wrapper around NanoCodec's decode path for tracing.
+
+    The NeMo codec uses symbolic int operations (.long(), .to(int64)) in:
+    1. CausalConv1dNorm._get_extra_padding_for_conv1d — shape-derived padding
+    2. CausalHiFiGANDecoder.forward — (audio_len * up_sample_rate).long()
+    3. mask_sequence_tensor — length-based masking
+
+    CoreML's converter cannot handle these symbolic int casts. Since we always
+    use fixed-size inputs (max_frames), we:
+    - Monkey-patch _get_extra_padding to use Python int arithmetic
+    - Patch mask_sequence_tensor to be a no-op (no padding to mask)
+    - Bypass the audio_decoder.forward to avoid symbolic length tracking
+    - Pass None for input_len everywhere (typecheck disabled)
+    """
+
+    def __init__(self, codec_model, max_frames):
+        super().__init__()
+        self.codec_model = codec_model
+        self.max_frames = max_frames
+
+        # Disable NeMo typecheck so modules accept None for input_len
+        _disable_typecheck()
+
+        # Patch mask_sequence_tensor to no-op (fixed-size inputs need no masking)
+        _patch_mask_noop()
+
+        # Patch CausalConv1d modules for CoreML traceability
+        _patch_causal_convs(codec_model.audio_decoder)
+
+        # Patch ConvTranspose1d unpadding to use negative indexing
+        _patch_conv_transpose_unpad(codec_model.audio_decoder)
+
+    def forward(self, tokens):
+        """
+        Args:
+            tokens: (B, num_codebooks, max_frames) int32 - codec token IDs
+        Returns:
+            audio: (B, T_samples) float32 - decoded audio waveform
+        """
+        # --- dequantize (bypass tokens_len masking, not needed for fixed input) ---
+        codec = self.codec_model
+        tokens_cbt = tokens.permute(1, 0, 2)  # (C, B, T)
+        indices_grouped = tokens_cbt.chunk(codec.vector_quantizer.num_groups, dim=0)
+        dequantized_parts = []
+        for indices_group, fsq_group in zip(indices_grouped, codec.vector_quantizer.fsqs):
+            # FSQ decode: rearrange 'D B T -> B D T' without einops (avoids aten::Int)
+            idx = indices_group.permute(1, 0, 2)
+            codes_nonneg = (idx // fsq_group.dim_base_index) % fsq_group.num_levels
+            dq = fsq_group.nonnegative_to_codes(codes_nonneg)
+            dequantized_parts.append(dq)
+        dequantized = torch.cat(dequantized_parts, dim=1)
+        dequantized = dequantized.to(codec.dtype)
+
+        # --- audio decoder (no input_len needed, masking is patched to no-op) ---
+        ad = codec.audio_decoder
+        out = ad.pre_conv(inputs=dequantized, input_len=None)
+        for act, res_layer, up_conv, _rate in zip(
+            ad.activations, ad.res_layers, ad.up_sample_conv_layers, ad.up_sample_rates
+        ):
+            out = act(out)
+            out = up_conv(inputs=out, input_len=None)
+            out = res_layer(inputs=out, input_len=None)
+
+        out = ad.post_activation(out)
+        out = ad.post_conv(inputs=out, input_len=None)
+        audio = ad.out_activation(out)
+        # Use squeeze instead of einops rearrange to avoid symbolic aten::Int ops
+        audio = audio.squeeze(1)  # (B, 1, T) -> (B, T)
+        return audio
+
+
+def convert_nanocodec(nemo_path=None, max_frames=256, output_path="build/nanocodec_decoder.mlpackage"):
+    # Load model
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    codec = model._codec_model
+    codec.eval()
+
+    num_codebooks = codec.num_codebooks
+    codebook_size = codec.codebook_size
+    print(f"NanoCodec: {num_codebooks} codebooks, {codebook_size} codes, "
+          f"{codec.sample_rate}Hz, {codec.samples_per_frame} samples/frame")
+
+    # Replace Snake activations with traceable versions
+    print("Replacing Snake activations...")
+    replaced = replace_snake_activations(codec)
+    print(f"  Replaced {replaced} Snake/HalfSnake modules")
+
+    # Verify replacement
+    with torch.no_grad():
+        test_tokens = torch.randint(0, codebook_size, (1, num_codebooks, 10), dtype=torch.long)
+        test_len = torch.tensor([10], dtype=torch.long)
+        audio_test = codec.decode(tokens=test_tokens, tokens_len=test_len)
+        print(f"  Post-replacement decode test: audio shape = {audio_test[0].shape}")
+
+    # Create traceable wrapper
+    print("Creating traceable NanoCodec decoder...")
+    traceable_codec = TraceableNanoCodecDecoder(codec, max_frames)
+    traceable_codec.eval()
+
+    # Example inputs
+    B = 1
+    T = max_frames
+    tokens = torch.randint(0, codebook_size, (B, num_codebooks, T), dtype=torch.long)
+
+    # Test forward pass
+    print("Testing forward pass...")
+    with torch.no_grad():
+        audio = traceable_codec(tokens)
+        print(f"Audio output shape: {audio.shape}")
+        expected_samples = T * codec.samples_per_frame
+        print(f"Expected ~{expected_samples} samples, got {audio.shape[-1]}")
+
+    # Trace
+    print("Tracing model...")
+    with torch.no_grad():
+        traced = torch.jit.trace(traceable_codec, (tokens,))
+
+    # Verify trace
+    with torch.no_grad():
+        ref_out = traceable_codec(tokens)
+        traced_out = traced(tokens)
+        diff = (ref_out - traced_out).abs().max().item()
+        print(f"Trace verification - max diff: {diff:.6e}")
+
+    # Convert to CoreML
+    print("Converting to CoreML...")
+    mlmodel = ct.convert(
+        traced,
+        inputs=[
+            ct.TensorType(name="tokens", shape=(1, num_codebooks, T), dtype=np.int32),
+        ],
+        outputs=[
+            ct.TensorType(name="audio", dtype=np.float32),
+        ],
+        convert_to="mlprogram",
+        compute_precision=ct.precision.FLOAT16,
+        minimum_deployment_target=ct.target.iOS17,
+    )
+
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    mlmodel.save(output_path)
+    print(f"Saved to {output_path}")
+
+    # Test CoreML
+    print("\nTesting CoreML model...")
+    coreml_model = ct.models.MLModel(output_path, compute_units=ct.ComputeUnit.CPU_ONLY)
+
+    test_tokens = np.random.randint(0, codebook_size, (1, num_codebooks, T)).astype(np.int32)
+
+    out = coreml_model.predict({
+        "tokens": test_tokens,
+    })
+
+    audio_out = out["audio"]
+    print(f"Audio output shape: {audio_out.shape}")
+    print(f"Audio range: [{audio_out.min():.4f}, {audio_out.max():.4f}]")
+    print("Done!")
+
+    return output_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nemo-path", type=str, default=None)
+    parser.add_argument("--max-frames", type=int, default=256)
+    parser.add_argument("--output", type=str, default="build/nanocodec_decoder.mlpackage")
+    args = parser.parse_args()
+    convert_nanocodec(args.nemo_path, args.max_frames, args.output)
diff --git a/models/tts/magpie/coreml/convert_text_encoder.py b/models/tts/magpie/coreml/convert_text_encoder.py
new file mode 100644
index 0000000..c60a490
--- /dev/null
+++ b/models/tts/magpie/coreml/convert_text_encoder.py
@@ -0,0 +1,102 @@
+"""Convert text encoder to CoreML.
+
+Usage:
+    python convert/convert_text_encoder.py [--nemo-path /path/to/model.nemo]
+"""
+import sys
+import os
+import argparse
+
+import torch
+import numpy as np
+import coremltools as ct
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from traceable.traceable_text_encoder import TraceableTextEncoder
+
+
+def convert_text_encoder(nemo_path=None, max_text_len=256, output_path="build/text_encoder.mlpackage"):
+    # Load model
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    # Create traceable encoder
+    print("Creating traceable text encoder...")
+    encoder = TraceableTextEncoder.from_magpie(model, include_text_embedding=True)
+    encoder.eval()
+
+    # Example inputs
+    B = 1
+    T = max_text_len
+    text_tokens = torch.randint(0, 100, (B, T), dtype=torch.long)
+    text_mask = torch.ones(B, T, dtype=torch.bool)
+    text_mask[:, T // 2:] = False  # Simulate padding
+
+    # Trace
+    print("Tracing model...")
+    with torch.no_grad():
+        traced = torch.jit.trace(encoder, (text_tokens, text_mask))
+
+    # Verify trace output
+    with torch.no_grad():
+        ref_out = encoder(text_tokens, text_mask)
+        traced_out = traced(text_tokens, text_mask)
+        diff = (ref_out - traced_out).abs().max().item()
+        print(f"Trace verification - max diff: {diff:.6e}")
+
+    # Convert to CoreML
+    print("Converting to CoreML...")
+    d_model = encoder.d_model
+
+    mlmodel = ct.convert(
+        traced,
+        inputs=[
+            ct.TensorType(name="text_tokens", shape=(1, T), dtype=np.int32),
+            ct.TensorType(name="text_mask", shape=(1, T), dtype=np.bool_),
+        ],
+        outputs=[
+            ct.TensorType(name="encoder_output", dtype=np.float32),
+        ],
+        convert_to="mlprogram",
+        compute_precision=ct.precision.FLOAT16,
+        minimum_deployment_target=ct.target.iOS17,
+    )
+
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    mlmodel.save(output_path)
+    print(f"Saved to {output_path}")
+
+    # Test CoreML model
+    print("\nTesting CoreML model...")
+    coreml_model = ct.models.MLModel(output_path, compute_units=ct.ComputeUnit.CPU_ONLY)
+
+    test_tokens = np.random.randint(0, 100, (1, T)).astype(np.int32)
+    # CoreML converts bool inputs to float32 at I/O boundary
+    test_mask = np.ones((1, T), dtype=np.float32)
+    test_mask[0, T // 2:] = 0.0
+
+    out = coreml_model.predict({
+        "text_tokens": test_tokens,
+        "text_mask": test_mask,
+    })
+
+    enc_out = out["encoder_output"]
+    print(f"Output shape: {enc_out.shape}")
+    print(f"Output range: [{enc_out.min():.4f}, {enc_out.max():.4f}]")
+    print("Done!")
+
+    return output_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nemo-path", type=str, default=None)
+    parser.add_argument("--max-text-len", type=int, default=256)
+    parser.add_argument("--output", type=str, default="build/text_encoder.mlpackage")
+    args = parser.parse_args()
+    convert_text_encoder(args.nemo_path, args.max_text_len, args.output)
diff --git a/models/tts/magpie/coreml/export_constants.py b/models/tts/magpie/coreml/export_constants.py
new file mode 100644
index 0000000..ecd2471
--- /dev/null
+++ b/models/tts/magpie/coreml/export_constants.py
@@ -0,0 +1,168 @@
+"""Export constants from MagpieTTS model for CoreML inference.
+
+Extracts and saves:
+- Audio embedding tables (numpy arrays)
+- Speaker context embeddings (numpy arrays)
+- Text embedding table (numpy array)
+- Model configuration (JSON)
+- Tokenizer files
+
+Usage:
+    python export_constants.py [--nemo-path /path/to/model.nemo]
+"""
+import argparse
+import json
+import os
+import shutil
+
+import numpy as np
+import torch
+
+
+def export_constants(nemo_path=None, output_dir="constants"):
+    # Load model
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    os.makedirs(output_dir, exist_ok=True)
+    cfg = model.cfg
+
+    # 1. Audio embedding tables
+    print("Exporting audio embedding tables...")
+    for i, emb in enumerate(model.audio_embeddings):
+        path = os.path.join(output_dir, f"audio_embedding_{i}.npy")
+        np.save(path, emb.weight.detach().cpu().numpy())
+        print(f"  audio_embedding_{i}: {emb.weight.shape}")
+
+    # 2. Text embedding table
+    print("Exporting text embedding table...")
+    if hasattr(model, "text_embedding"):
+        path = os.path.join(output_dir, "text_embedding.npy")
+        np.save(path, model.text_embedding.weight.detach().cpu().numpy())
+        print(f"  text_embedding: {model.text_embedding.weight.shape}")
+
+    # 3. Speaker context embeddings
+    if model.has_baked_context_embedding and model.baked_context_embedding is not None:
+        print("Exporting speaker embeddings...")
+        emb_weight = model.baked_context_embedding.weight.detach().cpu().numpy()
+        T = int(model._baked_embedding_T.item()) if model._baked_embedding_T is not None else None
+        D = int(model._baked_embedding_D.item()) if model._baked_embedding_D is not None else None
+        lens = model.baked_context_embedding_len.detach().cpu().numpy() if model.baked_context_embedding_len is not None else None
+
+        np.save(os.path.join(output_dir, "speaker_embeddings_raw.npy"), emb_weight)
+        print(f"  speaker_embeddings_raw: {emb_weight.shape}")
+
+        # Reshape each speaker's embedding to (T, D) and save individually
+        num_speakers = model.num_baked_speakers
+        for spk_idx in range(num_speakers):
+            spk_emb = emb_weight[spk_idx]  # Flat (T*D,)
+            if T and D:
+                spk_emb = spk_emb.reshape(T, D)
+            spk_len = int(lens[spk_idx]) if lens is not None else T
+            spk_emb = spk_emb[:spk_len]
+            np.save(os.path.join(output_dir, f"speaker_{spk_idx}.npy"), spk_emb)
+            print(f"  speaker_{spk_idx}: {spk_emb.shape}")
+
+        speaker_info = {
+            "num_speakers": num_speakers,
+            "T": T,
+            "D": D,
+            "lens": lens.tolist() if lens is not None else None,
+            "names": {
+                "0": "John",
+                "1": "Sofia",
+                "2": "Aria",
+                "3": "Jason",
+                "4": "Leo",
+            },
+        }
+        with open(os.path.join(output_dir, "speaker_info.json"), "w") as f:
+            json.dump(speaker_info, f, indent=2)
+
+    # 4. Model constants
+    print("Exporting model constants...")
+    constants = {
+        "model_type": model.model_type,
+        "embedding_dim": int(cfg.embedding_dim),
+        "frame_stacking_factor": model.frame_stacking_factor,
+        "num_audio_codebooks": model.num_audio_codebooks,
+        "codebook_size": model.codebook_size,
+        "num_all_tokens_per_codebook": model.num_all_tokens_per_codebook,
+        "sample_rate": model.sample_rate,
+        "output_sample_rate": model.output_sample_rate,
+        "codec_samples_per_frame": model.codec_model_samples_per_frame,
+        "special_tokens": {
+            "audio_bos_id": model.audio_bos_id,
+            "audio_eos_id": model.audio_eos_id,
+            "context_audio_bos_id": model.context_audio_bos_id,
+            "context_audio_eos_id": model.context_audio_eos_id,
+            "mask_token_id": model.mask_token_id,
+            "text_bos_id": model.bos_id,
+            "text_eos_id": model.eos_id,
+        },
+        "encoder": {
+            "n_layers": int(dict(cfg.encoder)["n_layers"]),
+            "d_model": int(dict(cfg.encoder)["d_model"]),
+            "d_ffn": int(dict(cfg.encoder)["d_ffn"]),
+            "sa_n_heads": int(dict(cfg.encoder)["sa_n_heads"]),
+        },
+        "decoder": {
+            "n_layers": int(dict(cfg.decoder)["n_layers"]),
+            "d_model": int(dict(cfg.decoder)["d_model"]),
+            "d_ffn": int(dict(cfg.decoder)["d_ffn"]),
+            "sa_n_heads": int(dict(cfg.decoder)["sa_n_heads"]),
+        },
+    }
+
+    # Add inference parameters
+    if hasattr(model, "inference_parameters") and model.inference_parameters is not None:
+        ip = model.inference_parameters
+        constants["inference"] = {
+            "temperature": float(ip.temperature),
+            "topk": int(ip.topk),
+            "cfg_scale": float(ip.cfg_scale),
+            "max_decoder_steps": int(ip.max_decoder_steps),
+            "min_generated_frames": int(ip.min_generated_frames),
+        }
+
+    with open(os.path.join(output_dir, "constants.json"), "w") as f:
+        json.dump(constants, f, indent=2, default=str)
+
+    # 5. Export tokenizer info
+    print("Exporting tokenizer info...")
+    tok = model.tokenizer
+    tokenizer_info = {
+        "type": type(tok).__name__,
+    }
+    if hasattr(tok, "num_tokens"):
+        tokenizer_info["num_tokens"] = tok.num_tokens
+    if hasattr(tok, "tokenizer_names"):
+        tokenizer_info["tokenizer_names"] = tok.tokenizer_names
+    if hasattr(tok, "num_tokens_per_tokenizer"):
+        tokenizer_info["num_tokens_per_tokenizer"] = {
+            k: v for k, v in tok.num_tokens_per_tokenizer.items()
+        }
+    if hasattr(tok, "tokenizers"):
+        tokenizer_info["available_tokenizers"] = list(tok.tokenizers.keys())
+    with open(os.path.join(output_dir, "tokenizer_info.json"), "w") as f:
+        json.dump(tokenizer_info, f, indent=2, default=str)
+
+    print(f"\nAll constants saved to {output_dir}/")
+    for f_name in sorted(os.listdir(output_dir)):
+        size = os.path.getsize(os.path.join(output_dir, f_name))
+        unit = "KB" if size < 1024 * 1024 else "MB"
+        size_val = size / 1024 if unit == "KB" else size / 1024 / 1024
+        print(f"  {f_name}: {size_val:.1f} {unit}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nemo-path", type=str, default=None)
+    parser.add_argument("--output-dir", type=str, default="constants")
+    args = parser.parse_args()
+    export_constants(args.nemo_path, args.output_dir)
diff --git a/models/tts/magpie/coreml/export_local_transformer.py b/models/tts/magpie/coreml/export_local_transformer.py
new file mode 100644
index 0000000..be93772
--- /dev/null
+++ b/models/tts/magpie/coreml/export_local_transformer.py
@@ -0,0 +1,191 @@
+"""Export local transformer weights as individual .npy files.
+
+Extracts weights from the NeMo model's local transformer and saves them
+as individual numpy arrays in constants/local_transformer/.
+
+Can also re-export from a previously extracted local_transformer.pt checkpoint.
+
+Usage:
+    # From NeMo model (requires NeMo)
+    python export_local_transformer.py
+
+    # From extracted .pt checkpoint
+    python export_local_transformer.py --from-pt build/extracted/local_transformer.pt
+
+    # Custom output directory
+    python export_local_transformer.py --output-dir constants/local_transformer
+"""
+import argparse
+import os
+
+import numpy as np
+import torch
+
+
+def export_from_nemo(nemo_path=None):
+    """Load NeMo model and return the three local transformer state dicts."""
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    transformer_sd = model.local_transformer.state_dict()
+    in_proj_sd = model.local_transformer_in_projection.state_dict()
+    out_proj_sds = [proj.state_dict() for proj in model.local_transformer_out_projections]
+
+    return transformer_sd, in_proj_sd, out_proj_sds
+
+
+def export_from_pt(pt_path):
+    """Load from a previously extracted local_transformer.pt checkpoint."""
+    print(f"Loading from {pt_path}...")
+    data = torch.load(pt_path, map_location="cpu", weights_only=True)
+
+    return (
+        data["transformer_state_dict"],
+        data["in_projection_state_dict"],
+        data["out_projections_state_dict"],
+    )
+
+
+def save_weights(transformer_sd, in_proj_sd, out_proj_sds, output_dir):
+    """Save individual weight tensors as .npy files."""
+    os.makedirs(output_dir, exist_ok=True)
+
+    def save(name, tensor):
+        arr = tensor.detach().cpu().numpy()
+        path = os.path.join(output_dir, f"{name}.npy")
+        np.save(path, arr)
+        print(f"  {name}: {arr.shape} ({arr.dtype})")
+
+    # In-projection: decoder d_model (768) → local transformer d (256)
+    save("in_proj_weight", in_proj_sd["weight"])
+    save("in_proj_bias", in_proj_sd["bias"])
+
+    # Transformer layer weights
+    # The NeMo local transformer is a small 1-layer transformer with:
+    #   - Learnable positional embedding
+    #   - Pre-norm self-attention (single head)
+    #   - Pre-norm FFN (Conv1d with kernel_size=1)
+    #
+    # State dict keys vary by NeMo version. We detect and map them.
+    print("\nTransformer state dict keys:")
+    for k, v in transformer_sd.items():
+        print(f"  {k}: {v.shape}")
+
+    # Build a mapping from state dict keys to output names.
+    # Try common NeMo key patterns.
+    key_map = find_key_mapping(transformer_sd)
+
+    print("\nExporting transformer weights:")
+    for out_name, sd_key in key_map.items():
+        save(out_name, transformer_sd[sd_key])
+
+    # Per-codebook output projections (8 codebooks)
+    print(f"\nExporting {len(out_proj_sds)} output projections:")
+    for i, sd in enumerate(out_proj_sds):
+        save(f"out_proj_{i}_weight", sd["weight"])
+        save(f"out_proj_{i}_bias", sd["bias"])
+
+
+def find_key_mapping(sd):
+    """Map NeMo state dict keys to our output file names.
+
+    Uses explicit key mapping for known NeMo naming conventions.
+    The NeMo MagpieTTS local transformer state dict has these keys:
+
+        layers.0.norm_self.weight          → norm1_weight   (pre-SA LayerNorm)
+        layers.0.self_attention.qkv_net.weight → sa_qkv_weight
+        layers.0.self_attention.causal_mask    → (skipped, not a learned weight)
+        layers.0.self_attention.o_net.weight   → sa_o_weight
+        layers.0.norm_pos_ff.weight        → norm2_weight   (pre-FFN LayerNorm)
+        layers.0.pos_ff.proj.conv.weight   → ffn_conv1_weight (up-projection)
+        layers.0.pos_ff.o_net.conv.weight  → ffn_conv2_weight (down-projection)
+        position_embeddings.weight         → pos_emb
+    """
+    # Explicit mapping table: (substring to match in key) → output name
+    # Order matters: more specific substrings first to avoid ambiguity.
+    explicit_rules = [
+        ("position_embeddings",    "pos_emb"),
+        ("norm_self",              "norm1_weight"),
+        ("norm_pos_ff",            "norm2_weight"),
+        ("qkv_net",               "sa_qkv_weight"),
+        ("self_attention.o_net",   "sa_o_weight"),
+        ("pos_ff.proj",           "ffn_conv1_weight"),
+        ("pos_ff.o_net",          "ffn_conv2_weight"),
+    ]
+
+    mapping = {}
+    skip_fragments = ["causal_mask"]  # Non-learned buffers
+
+    for sd_key in sd.keys():
+        # Skip non-weight buffers
+        if any(frag in sd_key for frag in skip_fragments):
+            continue
+
+        matched = False
+        for substring, out_name in explicit_rules:
+            if substring in sd_key:
+                if out_name in mapping:
+                    raise RuntimeError(
+                        f"Duplicate match: both '{mapping[out_name]}' and "
+                        f"'{sd_key}' match rule '{substring}' → '{out_name}'"
+                    )
+                mapping[out_name] = sd_key
+                matched = True
+                break
+
+        if not matched:
+            print(f"  WARNING: No rule for key '{sd_key}' ({sd[sd_key].shape})")
+
+    expected = {
+        "pos_emb", "norm1_weight", "norm2_weight",
+        "sa_qkv_weight", "sa_o_weight",
+        "ffn_conv1_weight", "ffn_conv2_weight",
+    }
+    missing = expected - set(mapping.keys())
+    if missing:
+        print(f"\nState dict keys found:")
+        for k, v in sd.items():
+            print(f"  {k}: {v.shape}")
+        raise RuntimeError(
+            f"Could not map all weights. Missing: {missing}. "
+            f"The NeMo key naming may have changed — update explicit_rules "
+            f"in find_key_mapping()."
+        )
+
+    return mapping
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Export local transformer weights as individual .npy files"
+    )
+    parser.add_argument("--nemo-path", type=str, default=None,
+                        help="Path to .nemo checkpoint (default: download from HF)")
+    parser.add_argument("--from-pt", type=str, default=None,
+                        help="Load from extracted local_transformer.pt instead of NeMo")
+    parser.add_argument("--output-dir", type=str, default="constants/local_transformer",
+                        help="Output directory (default: constants/local_transformer)")
+    args = parser.parse_args()
+
+    if args.from_pt:
+        transformer_sd, in_proj_sd, out_proj_sds = export_from_pt(args.from_pt)
+    else:
+        transformer_sd, in_proj_sd, out_proj_sds = export_from_nemo(args.nemo_path)
+
+    save_weights(transformer_sd, in_proj_sd, out_proj_sds, args.output_dir)
+
+    print(f"\nDone. Files saved to {args.output_dir}/")
+    total = sum(
+        os.path.getsize(os.path.join(args.output_dir, f))
+        for f in os.listdir(args.output_dir) if f.endswith(".npy")
+    )
+    print(f"Total size: {total / 1024 / 1024:.1f} MB")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/tts/magpie/coreml/export_pypinyin.py b/models/tts/magpie/coreml/export_pypinyin.py
new file mode 100644
index 0000000..f43fa49
--- /dev/null
+++ b/models/tts/magpie/coreml/export_pypinyin.py
@@ -0,0 +1,79 @@
+"""Export pypinyin character→pinyin dictionary and phrase dict for Swift.
+
+Extracts from pypinyin and jieba packages:
+- mandarin_pypinyin_char_dict.json — character → pinyin list
+- mandarin_pypinyin_phrase_dict.json — phrase → pinyin list (polyphone disambiguation)
+- mandarin_jieba_dict.json — word → frequency (for jieba word segmentation)
+
+These are used by MandarinTokenizer.swift for on-device Mandarin G2P.
+
+Usage:
+    python extras/export_pypinyin.py [--output-dir constants]
+
+Requires: pypinyin, jieba (install via: uv pip install pypinyin jieba)
+"""
+import argparse
+import json
+import os
+
+
+def export_pypinyin(output_dir="constants"):
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 1. Character → pinyin dict (from pypinyin.PINYIN_DICT)
+    from pypinyin.core import PINYIN_DICT
+
+    char_pinyin = {}
+    for codepoint, pinyins_str in PINYIN_DICT.items():
+        char = chr(codepoint)
+        pinyins = pinyins_str.split(',')
+        char_pinyin[char] = pinyins
+
+    path = os.path.join(output_dir, "mandarin_pypinyin_char_dict.json")
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(char_pinyin, f, ensure_ascii=False, separators=(',', ':'))
+    size_kb = os.path.getsize(path) / 1024
+    print(f"char_pinyin: {len(char_pinyin)} entries, {size_kb:.1f} KB")
+
+    # 2. Phrase → pinyin dict (for multi-char polyphone disambiguation)
+    from pypinyin.phrases_dict import phrases_dict
+
+    # Convert from list-of-lists to list-of-strings for simpler Swift parsing
+    phrase_pinyin = {}
+    for phrase, pinyins_list in phrases_dict.items():
+        # pinyins_list is like [['yī'], ['dīng'], ['bù'], ['shí']]
+        # Flatten to ['yī', 'dīng', 'bù', 'shí']
+        flat = [p[0] if isinstance(p, list) else p for p in pinyins_list]
+        phrase_pinyin[phrase] = flat
+
+    path = os.path.join(output_dir, "mandarin_pypinyin_phrase_dict.json")
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(phrase_pinyin, f, ensure_ascii=False, separators=(',', ':'))
+    size_kb = os.path.getsize(path) / 1024
+    print(f"phrase_pinyin: {len(phrase_pinyin)} entries, {size_kb:.1f} KB")
+
+    # 3. Export jieba dictionary (word, frequency, POS)
+    import jieba
+    jieba_dict_path = os.path.join(os.path.dirname(jieba.__file__), 'dict.txt')
+    # Convert to JSON: word → freq (POS not needed for cut)
+    jieba_data = {}
+    with open(jieba_dict_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            parts = line.strip().split()
+            if len(parts) >= 2:
+                word = parts[0]
+                freq = int(parts[1])
+                jieba_data[word] = freq
+
+    path = os.path.join(output_dir, "mandarin_jieba_dict.json")
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(jieba_data, f, ensure_ascii=False, separators=(',', ':'))
+    size_kb = os.path.getsize(path) / 1024
+    print(f"jieba_dict: {len(jieba_data)} entries, {size_kb:.1f} KB")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output-dir", type=str, default="constants")
+    args = parser.parse_args()
+    export_pypinyin(args.output_dir)
diff --git a/models/tts/magpie/coreml/export_tokenizer_aliases.py b/models/tts/magpie/coreml/export_tokenizer_aliases.py
new file mode 100644
index 0000000..3ab0c4a
--- /dev/null
+++ b/models/tts/magpie/coreml/export_tokenizer_aliases.py
@@ -0,0 +1,46 @@
+"""Create alias files expected by the Swift EnglishTokenizer.
+
+The Swift EnglishTokenizer loads constants with short names:
+  - english_phoneme_dict.json   (phoneme dictionary)
+  - english_token2id.json       (token→ID mapping)
+  - english_heteronyms.json     (heteronyms list)
+
+But export_tokenizers.py generates them with the full tokenizer name prefix:
+  - english_phoneme_phoneme_dict.json
+  - english_phoneme_token2id.json
+  - english_phoneme_heteronyms.json
+
+This script copies the canonical files to the short-name aliases that Swift expects.
+Run this after export_tokenizers.py.
+
+Usage:
+    python export_tokenizer_aliases.py [--output-dir constants]
+"""
+import argparse
+import os
+import shutil
+
+ALIASES = [
+    ("english_phoneme_phoneme_dict.json", "english_phoneme_dict.json"),
+    ("english_phoneme_token2id.json", "english_token2id.json"),
+    ("english_phoneme_heteronyms.json", "english_heteronyms.json"),
+]
+
+
+def export_aliases(output_dir="constants"):
+    for src_name, dst_name in ALIASES:
+        src = os.path.join(output_dir, src_name)
+        dst = os.path.join(output_dir, dst_name)
+        if not os.path.exists(src):
+            print(f"  WARNING: {src_name} not found, skipping")
+            continue
+        shutil.copy2(src, dst)
+        size_kb = os.path.getsize(dst) / 1024
+        print(f"  {src_name} -> {dst_name} ({size_kb:.1f} KB)")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output-dir", type=str, default="constants")
+    args = parser.parse_args()
+    export_aliases(args.output_dir)
diff --git a/models/tts/magpie/coreml/export_tokenizers.py b/models/tts/magpie/coreml/export_tokenizers.py
new file mode 100644
index 0000000..1d34871
--- /dev/null
+++ b/models/tts/magpie/coreml/export_tokenizers.py
@@ -0,0 +1,228 @@
+"""Export all language tokenizer data from MagpieTTS for Swift inference.
+
+Extracts from each language tokenizer in the AggregatedTTSTokenizer:
+- token2id mapping (token string -> local ID)
+- phoneme_dict (word -> phoneme list) for IPA G2P tokenizers
+- heteronyms list for English
+- tokenizer metadata (type, offset, locale, etc.)
+
+Usage:
+    python export_tokenizers.py [--nemo-path /path/to/model.nemo] [--output-dir constants]
+"""
+import argparse
+import json
+import os
+import sys
+
+
+def export_tokenizers(nemo_path=None, output_dir="constants"):
+    print("Loading MagpieTTS model...")
+    from nemo.collections.tts.models import MagpieTTSModel
+    if nemo_path:
+        model = MagpieTTSModel.restore_from(nemo_path, map_location="cpu")
+    else:
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    tok = model.tokenizer
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Build master metadata
+    metadata = {
+        "tokenizer_names": tok.tokenizer_names,
+        "offsets": {},
+        "num_tokens": {},
+        "types": {},
+        "eos_token_id": model.eos_id,
+    }
+
+    for name in tok.tokenizer_names:
+        sub_tok = tok.tokenizers[name]
+        offset = tok.tokenizer_offsets[name]
+        num_tokens = tok.num_tokens_per_tokenizer[name]
+        tok_type = type(sub_tok).__name__
+
+        metadata["offsets"][name] = offset
+        metadata["num_tokens"][name] = num_tokens
+        metadata["types"][name] = tok_type
+
+        print(f"\n{'='*60}")
+        print(f"Tokenizer: {name}")
+        print(f"  Type: {tok_type}")
+        print(f"  Offset: {offset}")
+        print(f"  Num tokens: {num_tokens}")
+
+        # Export token2id mapping
+        if hasattr(sub_tok, '_token2id'):
+            t2i = {k: v for k, v in sub_tok._token2id.items()}
+            t2i_path = os.path.join(output_dir, f"{name}_token2id.json")
+            with open(t2i_path, "w", encoding="utf-8") as f:
+                json.dump(t2i, f, ensure_ascii=False, indent=None, separators=(',', ':'))
+            size_kb = os.path.getsize(t2i_path) / 1024
+            print(f"  token2id: {len(t2i)} entries ({size_kb:.1f} KB)")
+
+        # Export phoneme dict for IPA G2P tokenizers
+        if hasattr(sub_tok, 'g2p') and hasattr(sub_tok.g2p, 'phoneme_dict') and sub_tok.g2p.phoneme_dict is not None:
+            g2p = sub_tok.g2p
+            pd = {}
+            for word, prons in g2p.phoneme_dict.items():
+                # prons is List[List[str]] — take first pronunciation
+                if isinstance(prons, list) and len(prons) > 0:
+                    if isinstance(prons[0], list):
+                        pd[word] = prons[0]
+                    else:
+                        pd[word] = prons
+            pd_path = os.path.join(output_dir, f"{name}_phoneme_dict.json")
+            with open(pd_path, "w", encoding="utf-8") as f:
+                json.dump(pd, f, ensure_ascii=False, indent=None, separators=(',', ':'))
+            size_mb = os.path.getsize(pd_path) / 1024 / 1024
+            print(f"  phoneme_dict: {len(pd)} entries ({size_mb:.1f} MB)")
+
+            # Export G2P metadata
+            g2p_meta = {}
+            if hasattr(g2p, 'locale'):
+                g2p_meta['locale'] = g2p.locale
+            if hasattr(g2p, 'grapheme_case'):
+                g2p_meta['grapheme_case'] = g2p.grapheme_case
+            if hasattr(g2p, 'grapheme_prefix'):
+                g2p_meta['grapheme_prefix'] = g2p.grapheme_prefix
+            if hasattr(g2p, 'use_stresses'):
+                g2p_meta['use_stresses'] = g2p.use_stresses
+            if hasattr(g2p, 'ignore_ambiguous_words'):
+                g2p_meta['ignore_ambiguous_words'] = g2p.ignore_ambiguous_words
+            if hasattr(g2p, 'phoneme_probability'):
+                g2p_meta['phoneme_probability'] = g2p.phoneme_probability
+            if g2p_meta:
+                metadata.setdefault("g2p_config", {})[name] = g2p_meta
+                print(f"  G2P config: {g2p_meta}")
+
+            # Export heteronyms
+            if hasattr(g2p, 'heteronyms') and g2p.heteronyms:
+                het_path = os.path.join(output_dir, f"{name}_heteronyms.json")
+                with open(het_path, "w", encoding="utf-8") as f:
+                    json.dump(sorted(list(g2p.heteronyms)), f, ensure_ascii=False)
+                print(f"  heteronyms: {len(g2p.heteronyms)} entries")
+
+        # For Chinese G2P: export pinyin phoneme dict and tone/letter mappings
+        if hasattr(sub_tok, 'g2p') and hasattr(sub_tok.g2p, 'tone_dict'):
+            g2p = sub_tok.g2p
+            # Pinyin phoneme dict
+            if hasattr(g2p, 'phoneme_dict'):
+                pd = {k: v if isinstance(v, list) else [v] for k, v in g2p.phoneme_dict.items()}
+                pd_path = os.path.join(output_dir, f"{name}_pinyin_dict.json")
+                with open(pd_path, "w", encoding="utf-8") as f:
+                    json.dump(pd, f, ensure_ascii=False, indent=None, separators=(',', ':'))
+                print(f"  pinyin_dict: {len(pd)} entries")
+
+            # Tone mapping
+            tone_path = os.path.join(output_dir, f"{name}_tone_dict.json")
+            with open(tone_path, "w", encoding="utf-8") as f:
+                json.dump(g2p.tone_dict, f, ensure_ascii=False)
+            print(f"  tone_dict: {len(g2p.tone_dict)} entries")
+
+            # ASCII letter mapping
+            letter_path = os.path.join(output_dir, f"{name}_ascii_letter_dict.json")
+            with open(letter_path, "w", encoding="utf-8") as f:
+                json.dump(g2p.ascii_letter_dict, f, ensure_ascii=False)
+            print(f"  ascii_letter_dict: {len(g2p.ascii_letter_dict)} entries")
+
+        # For Japanese G2P: export phoneme dict
+        if hasattr(sub_tok, 'g2p') and hasattr(sub_tok.g2p, 'ascii_letter_dict') and 'japanese' in name:
+            g2p = sub_tok.g2p
+            if hasattr(g2p, 'phoneme_dict') and g2p.phoneme_dict is not None:
+                pd = {k: v if isinstance(v, list) else [v] for k, v in g2p.phoneme_dict.items()}
+                pd_path = os.path.join(output_dir, f"{name}_word_dict.json")
+                with open(pd_path, "w", encoding="utf-8") as f:
+                    json.dump(pd, f, ensure_ascii=False, indent=None, separators=(',', ':'))
+                print(f"  word_dict: {len(pd)} entries")
+
+            letter_path = os.path.join(output_dir, f"{name}_ascii_letter_dict.json")
+            with open(letter_path, "w", encoding="utf-8") as f:
+                json.dump(g2p.ascii_letter_dict, f, ensure_ascii=False)
+            print(f"  ascii_letter_dict: {len(g2p.ascii_letter_dict)} entries")
+
+            if hasattr(g2p, 'punctuation'):
+                punct_path = os.path.join(output_dir, f"{name}_punctuation.json")
+                with open(punct_path, "w", encoding="utf-8") as f:
+                    json.dump(list(g2p.punctuation), f, ensure_ascii=False)
+
+        # For char-based tokenizers: export the char set info
+        if hasattr(sub_tok, 'text_preprocessing_func'):
+            # Store info about preprocessing
+            func_name = getattr(sub_tok.text_preprocessing_func, '__name__', str(sub_tok.text_preprocessing_func))
+            metadata.setdefault("preprocessing", {})[name] = func_name
+
+        # Export pad_with_space
+        if hasattr(sub_tok, 'pad_with_space'):
+            metadata.setdefault("pad_with_space", {})[name] = sub_tok.pad_with_space
+
+        # Export punct list
+        if hasattr(sub_tok, 'PUNCT_LIST'):
+            metadata.setdefault("punct_lists", {})[name] = list(sub_tok.PUNCT_LIST)
+        elif hasattr(sub_tok, 'punct_list'):
+            metadata.setdefault("punct_lists", {})[name] = list(sub_tok.punct_list)
+
+    # Run reference tokenizations for verification
+    print(f"\n{'='*60}")
+    print("Reference tokenizations (for Swift verification):")
+    test_texts = {
+        "english_phoneme": "Hello, this is a test.",
+        "spanish_phoneme": "Hola, esto es una prueba.",
+        "german_phoneme": "Hallo, das ist ein Test.",
+        "mandarin_phoneme": "你好，这是一个测试。",
+        "japanese_phoneme": "こんにちは、これはテストです。",
+        "french_chartokenizer": "Bonjour, ceci est un test.",
+        "hindi_chartokenizer": "नमस्ते, यह एक परीक्षा है।",
+        "italian_phoneme": "Ciao, questo è un test.",
+        "vietnamese_phoneme": "Xin chào, đây là một bài kiểm tra.",
+    }
+
+    references = {}
+    for name in tok.tokenizer_names:
+        if name in test_texts:
+            try:
+                # Force phoneme_probability=1.0 for deterministic output
+                sub = tok.tokenizers[name]
+                if hasattr(sub, 'g2p') and hasattr(sub.g2p, 'phoneme_probability'):
+                    old_prob = sub.g2p.phoneme_probability
+                    sub.g2p.phoneme_probability = 1.0
+                ids = tok.encode(test_texts[name], tokenizer_name=name)
+                if hasattr(sub, 'g2p') and hasattr(sub.g2p, 'phoneme_probability'):
+                    sub.g2p.phoneme_probability = old_prob
+                references[name] = {
+                    "text": test_texts[name],
+                    "token_ids": ids,
+                    "num_tokens": len(ids),
+                }
+                print(f"  {name}: \"{test_texts[name]}\" -> {len(ids)} tokens")
+                print(f"    IDs (first 20): {ids[:20]}")
+            except Exception as e:
+                print(f"  {name}: ERROR - {e}")
+                references[name] = {"text": test_texts[name], "error": str(e)}
+
+    # Save metadata
+    metadata_path = os.path.join(output_dir, "tokenizer_metadata.json")
+    with open(metadata_path, "w", encoding="utf-8") as f:
+        json.dump(metadata, f, ensure_ascii=False, indent=2)
+
+    # Save references
+    ref_path = os.path.join(output_dir, "tokenizer_references.json")
+    with open(ref_path, "w", encoding="utf-8") as f:
+        json.dump(references, f, ensure_ascii=False, indent=2)
+
+    print(f"\n{'='*60}")
+    print(f"All tokenizer data saved to {output_dir}/")
+    for f_name in sorted(os.listdir(output_dir)):
+        if 'tokenizer' in f_name or 'phoneme' in f_name or 'heteronym' in f_name or 'pinyin' in f_name or 'tone' in f_name or 'ascii' in f_name or 'punct' in f_name or 'word_dict' in f_name:
+            size = os.path.getsize(os.path.join(output_dir, f_name))
+            unit = "KB" if size < 1024 * 1024 else "MB"
+            size_val = size / 1024 if unit == "KB" else size / 1024 / 1024
+            print(f"  {f_name}: {size_val:.1f} {unit}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nemo-path", type=str, default=None)
+    parser.add_argument("--output-dir", type=str, default="constants")
+    args = parser.parse_args()
+    export_tokenizers(args.nemo_path, args.output_dir)
diff --git a/models/tts/magpie/coreml/generate_coreml.py b/models/tts/magpie/coreml/generate_coreml.py
new file mode 100644
index 0000000..e786ca8
--- /dev/null
+++ b/models/tts/magpie/coreml/generate_coreml.py
@@ -0,0 +1,533 @@
+"""Pure CoreML TTS generation for Magpie TTS 357M.
+
+Pipeline:
+1. Tokenize text
+2. Encode text (text_encoder.mlpackage)
+3. Prepare speaker context embedding
+4. Autoregressive generation loop (decoder_step.mlpackage)
+5. Local transformer codebook-by-codebook sampling (numpy)
+6. Decode codec tokens to audio (nanocodec_decoder.mlpackage)
+7. Save WAV
+
+Dependencies: numpy, coremltools, soundfile
+NeMo is required only for tokenization (could be replaced with standalone tokenizer).
+"""
+import json
+import math
+import os
+import time
+
+import numpy as np
+import coremltools as ct
+import soundfile as sf
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+CONST_DIR = os.path.join(SCRIPT_DIR, "constants")
+BUILD_DIR = os.path.join(SCRIPT_DIR, "build")
+
+# Decoder step output key names (from CoreML model spec)
+DECODER_LOGITS_KEY = "var_2201"
+DECODER_HIDDEN_KEY = "input"
+# Output cache keys (input keys are cache0..cache11)
+DECODER_CACHE_OUT_KEYS = [
+    "new_cache_1", "new_cache_3", "new_cache_5", "new_cache_7",
+    "new_cache_9", "new_cache_11", "new_cache_13", "new_cache_15",
+    "new_cache_17", "new_cache_19", "new_cache_21", "new_cache",
+]
+DECODER_POSITION_KEYS = [
+    "var_169", "var_346", "var_523", "var_700", "var_877", "var_1054",
+    "var_1231", "var_1408", "var_1585", "var_1762", "var_1939", "var_2116",
+]
+
+# Forbidden token IDs (special tokens that should never be sampled)
+# Allow EOS: forbid BOS(2016), CTX_BOS(2018), CTX_EOS(2019), MASK(2020), 2021-2023
+FORBIDDEN_TOKENS_ALLOW_EOS = [2016, 2018, 2019, 2020, 2021, 2022, 2023]
+# Forbid EOS too: also forbid EOS(2017)
+FORBIDDEN_TOKENS_FORBID_EOS = [2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023]
+
+
+def load_constants():
+    with open(os.path.join(CONST_DIR, "constants.json")) as f:
+        return json.load(f)
+
+
+def load_speaker_embedding(speaker_idx):
+    return np.load(os.path.join(CONST_DIR, f"speaker_{speaker_idx}.npy")).astype(np.float32)
+
+
+def load_audio_embeddings(constants):
+    tables = []
+    for i in range(constants["num_audio_codebooks"]):
+        tables.append(np.load(os.path.join(CONST_DIR, f"audio_embedding_{i}.npy")).astype(np.float32))
+    return tables
+
+
+def load_local_transformer():
+    """Load local transformer weights from exported numpy files."""
+    lt_dir = os.path.join(CONST_DIR, "local_transformer")
+    lt = {
+        "in_proj_weight": np.load(os.path.join(lt_dir, "in_proj_weight.npy")),
+        "in_proj_bias": np.load(os.path.join(lt_dir, "in_proj_bias.npy")),
+        "pos_emb": np.load(os.path.join(lt_dir, "pos_emb.npy")),
+        "norm1_weight": np.load(os.path.join(lt_dir, "norm1_weight.npy")),
+        "sa_qkv_weight": np.load(os.path.join(lt_dir, "sa_qkv_weight.npy")),
+        "sa_o_weight": np.load(os.path.join(lt_dir, "sa_o_weight.npy")),
+        "norm2_weight": np.load(os.path.join(lt_dir, "norm2_weight.npy")),
+        "ffn_conv1_weight": np.load(os.path.join(lt_dir, "ffn_conv1_weight.npy")),
+        "ffn_conv2_weight": np.load(os.path.join(lt_dir, "ffn_conv2_weight.npy")),
+    }
+    lt["out_proj_weights"] = []
+    lt["out_proj_biases"] = []
+    for i in range(8):
+        lt["out_proj_weights"].append(np.load(os.path.join(lt_dir, f"out_proj_{i}_weight.npy")))
+        lt["out_proj_biases"].append(np.load(os.path.join(lt_dir, f"out_proj_{i}_bias.npy")))
+    return lt
+
+
+def embed_audio_codes(codes, audio_embedding_tables, num_codebooks):
+    """Embed audio codes for one frame. codes: (num_codebooks,) int."""
+    embedding = None
+    for c in range(num_codebooks):
+        emb = audio_embedding_tables[c][codes[c]]
+        embedding = emb if embedding is None else embedding + emb
+    return (embedding / num_codebooks)[np.newaxis, np.newaxis, :]  # (1, 1, d_model)
+
+
+def numpy_layer_norm(x, weight, eps=1e-5):
+    """LayerNorm with no bias."""
+    mean = x.mean(axis=-1, keepdims=True)
+    var = x.var(axis=-1, keepdims=True)
+    return (x - mean) / np.sqrt(var + eps) * weight
+
+
+def numpy_gelu(x):
+    """GELU (tanh approximation)."""
+    return 0.5 * x * (1 + np.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * x ** 3)))
+
+
+def local_transformer_forward(sequence, lt_weights):
+    """Run 1-layer causal transformer on sequence.
+
+    Args:
+        sequence: (T, 256) float32 — input sequence
+        lt_weights: dict of weight arrays
+
+    Returns:
+        output: (T, 256) float32
+    """
+    T, D = sequence.shape
+
+    # Add positional embeddings
+    x = sequence + lt_weights["pos_emb"][:T]
+
+    # Layer: pre-norm causal self-attention
+    residual = x
+    x_norm = numpy_layer_norm(x, lt_weights["norm1_weight"])
+
+    # QKV projection (single head, d_model=256)
+    qkv = x_norm @ lt_weights["sa_qkv_weight"].T  # (T, 768)
+    q, k, v = np.split(qkv, 3, axis=-1)  # each (T, 256)
+
+    scale = 1.0 / math.sqrt(D)
+    attn = (q @ k.T) * scale  # (T, T)
+
+    # Causal mask
+    causal = np.tril(np.ones((T, T), dtype=np.float32))
+    attn = attn * causal + (1 - causal) * (-1e9)
+    attn = np.exp(attn - attn.max(axis=-1, keepdims=True))
+    attn = attn / attn.sum(axis=-1, keepdims=True)
+
+    sa_out = attn @ v  # (T, 256)
+    sa_out = sa_out @ lt_weights["sa_o_weight"].T  # (T, 256)
+    x = residual + sa_out
+
+    # Layer: pre-norm FFN (kernel_size=1, so just matmuls)
+    residual = x
+    x_norm = numpy_layer_norm(x, lt_weights["norm2_weight"])
+
+    # Conv1d with ks=1 is equivalent to linear: (T, 256) @ (256, 1024) → (T, 1024)
+    ffn_w1 = lt_weights["ffn_conv1_weight"].squeeze(-1)  # (1024, 256)
+    ffn_w2 = lt_weights["ffn_conv2_weight"].squeeze(-1)  # (256, 1024)
+    h = numpy_gelu(x_norm @ ffn_w1.T)
+    h = h @ ffn_w2.T
+    x = residual + h
+
+    return x
+
+
+def sample_topk(logits, temperature=0.6, topk=80):
+    """Top-k sampling with temperature."""
+    if topk > 0:
+        topk_vals = np.partition(logits, -topk)[-topk:]
+        threshold = topk_vals.min()
+        logits = np.where(logits >= threshold, logits, -np.inf)
+
+    logits = logits / max(temperature, 1e-8)
+    logits = logits - logits.max()
+    probs = np.exp(logits)
+    probs = probs / probs.sum()
+    return np.random.choice(len(probs), p=probs)
+
+
+def local_transformer_sample(decoder_hidden, lt_weights, audio_emb_tables,
+                             num_codebooks, temperature, topk, forbid_eos,
+                             uncond_decoder_hidden=None, cfg_scale=1.0):
+    """Sample codebook tokens autoregressively using local transformer.
+
+    When CFG is enabled, runs both conditional and unconditional sequences through
+    the local transformer and applies CFG at the codebook logit level (matching NeMo).
+
+    Args:
+        decoder_hidden: (768,) float32 — conditional decoder hidden state
+        lt_weights: dict of LT weight arrays
+        audio_emb_tables: list of (2024, 768) embedding tables
+        num_codebooks: 8
+        temperature: sampling temperature
+        topk: top-k value
+        forbid_eos: whether to forbid EOS token
+        uncond_decoder_hidden: (768,) float32 — unconditional decoder hidden (for CFG)
+        cfg_scale: CFG scale factor
+
+    Returns:
+        codes: (num_codebooks,) int32 — sampled codebook tokens
+    """
+    forbidden = FORBIDDEN_TOKENS_FORBID_EOS if forbid_eos else FORBIDDEN_TOKENS_ALLOW_EOS
+    use_cfg = uncond_decoder_hidden is not None and cfg_scale != 1.0
+
+    in_proj_w = lt_weights["in_proj_weight"]  # (256, 768)
+    in_proj_b = lt_weights["in_proj_bias"]  # (256,)
+
+    # Project decoder hidden → LT dim
+    cond_input = decoder_hidden @ in_proj_w.T + in_proj_b  # (256,)
+    cond_seq = cond_input[np.newaxis, :]  # (1, 256)
+
+    if use_cfg:
+        uncond_input = uncond_decoder_hidden @ in_proj_w.T + in_proj_b
+        uncond_seq = uncond_input[np.newaxis, :]
+
+    codes = np.zeros(num_codebooks, dtype=np.int32)
+
+    for cb_idx in range(num_codebooks):
+        # Run conditional LT
+        cond_out = local_transformer_forward(cond_seq, lt_weights)
+        out_w = lt_weights["out_proj_weights"][cb_idx]
+        out_b = lt_weights["out_proj_biases"][cb_idx]
+        cond_logits = cond_out[-1] @ out_w.T + out_b  # (2024,)
+
+        if use_cfg:
+            # Run unconditional LT
+            uncond_out = local_transformer_forward(uncond_seq, lt_weights)
+            uncond_logits = uncond_out[-1] @ out_w.T + out_b
+            # Apply CFG at logit level
+            cb_logits = cfg_scale * cond_logits + (1.0 - cfg_scale) * uncond_logits
+        else:
+            cb_logits = cond_logits
+
+        # Clear forbidden tokens
+        for tok_id in forbidden:
+            if tok_id < len(cb_logits):
+                cb_logits[tok_id] = -np.inf
+
+        # Sample
+        codes[cb_idx] = sample_topk(cb_logits, temperature, topk)
+
+        # Embed sampled token and project to LT dim for next step
+        token_emb = audio_emb_tables[cb_idx][codes[cb_idx]]  # (768,)
+        next_input = token_emb @ in_proj_w.T + in_proj_b  # (256,)
+        cond_seq = np.concatenate([cond_seq, next_input[np.newaxis, :]], axis=0)
+        if use_cfg:
+            # Unconditional path uses same sampled token (matching NeMo line 94)
+            uncond_seq = np.concatenate([uncond_seq, next_input[np.newaxis, :]], axis=0)
+
+    return codes
+
+
+def generate(
+    text: str,
+    speaker: int = 0,
+    language: str = "en",
+    output_path: str = "magpie_output.wav",
+    temperature: float = 0.6,
+    topk: int = 80,
+    max_steps: int = 500,
+    seed: int = 42,
+    use_cfg: bool = True,
+    cfg_scale: float = 2.5,
+):
+    np.random.seed(seed)
+    constants = load_constants()
+
+    num_codebooks = constants["num_audio_codebooks"]
+    audio_bos_id = constants["special_tokens"]["audio_bos_id"]
+    audio_eos_id = constants["special_tokens"]["audio_eos_id"]
+    sample_rate = constants["output_sample_rate"]
+    d_model = constants["decoder"]["d_model"]
+    n_layers = constants["decoder"]["n_layers"]
+    sa_n_heads = constants["decoder"]["sa_n_heads"]
+    d_head = d_model // sa_n_heads
+    max_text_len = 256
+    max_seq_len = 512
+    min_frames = constants["inference"].get("min_generated_frames", 4)
+
+    print(f"Text: '{text}'")
+    print(f"Speaker: {speaker}, Language: {language}")
+    if use_cfg:
+        print(f"CFG scale: {cfg_scale}")
+
+    # 1. Tokenize text
+    print("Tokenizing text...")
+    text_tokens = _tokenize_text(text, language, constants)
+    T_text = len(text_tokens)
+    print(f"  Tokens: {T_text}")
+
+    text_tokens_padded = np.zeros(max_text_len, dtype=np.int32)
+    text_tokens_padded[:T_text] = text_tokens
+    text_mask = np.zeros(max_text_len, dtype=np.float32)
+    text_mask[:T_text] = 1.0
+
+    # 2. Load CoreML models
+    print("Loading CoreML models...")
+    text_encoder = ct.models.MLModel(
+        os.path.join(BUILD_DIR, "text_encoder.mlpackage"),
+        compute_units=ct.ComputeUnit.CPU_AND_GPU,
+    )
+    decoder_step = ct.models.MLModel(
+        os.path.join(BUILD_DIR, "decoder_step.mlpackage"),
+        compute_units=ct.ComputeUnit.CPU_AND_GPU,
+    )
+    nanocodec = ct.models.MLModel(
+        os.path.join(BUILD_DIR, "nanocodec_decoder.mlpackage"),
+        compute_units=ct.ComputeUnit.CPU_AND_GPU,
+    )
+
+    # 3. Encode text
+    print("Encoding text...")
+    enc_out = text_encoder.predict({
+        "text_tokens": text_tokens_padded[np.newaxis, :],
+        "text_mask": text_mask[np.newaxis, :],
+    })
+    encoder_output = enc_out["encoder_output"]  # (1, max_text_len, d_model)
+
+    # For CFG: create unconditional encoder output and mask (matching NeMo's prepare_dummy_cond_for_cfg)
+    if use_cfg:
+        uncond_encoder_output = np.zeros_like(encoder_output)
+        # NeMo keeps first position unmasked (value is zero) for numerical stability
+        uncond_text_mask = np.zeros_like(text_mask)
+        uncond_text_mask[0] = 1.0
+
+    # 4. Load speaker context + audio embeddings + local transformer
+    print(f"Loading speaker {speaker} embedding...")
+    speaker_emb = load_speaker_embedding(speaker)
+    T_ctx = speaker_emb.shape[0]
+    audio_emb_tables = load_audio_embeddings(constants)
+    lt_weights = load_local_transformer()
+
+    # 5. Initialize KV caches (conditional)
+    def make_caches():
+        c, p = {}, {}
+        for i in range(n_layers):
+            c[f"cache{i}"] = np.zeros((2, 1, max_seq_len, sa_n_heads, d_head), dtype=np.float32)
+            p[f"position{i}"] = np.array([0.0], dtype=np.float32)
+        return c, p
+
+    caches, positions = make_caches()
+    if use_cfg:
+        uncond_caches, uncond_positions = make_caches()
+
+    def run_decoder_step(audio_embed_np, enc_out_np, mask_np, cache_dict, pos_dict):
+        step_inputs = {
+            "audio_embed": audio_embed_np.astype(np.float32),
+            "encoder_output": enc_out_np.astype(np.float32),
+            "encoder_mask": mask_np[np.newaxis, :].astype(np.float32),
+        }
+        step_inputs.update(cache_dict)
+        step_inputs.update(pos_dict)
+        step_out = decoder_step.predict(step_inputs)
+        for i in range(n_layers):
+            # Output cache keys differ from input keys after scatter-based cache rewrite
+            cache_dict[f"cache{i}"] = step_out[DECODER_CACHE_OUT_KEYS[i]]
+            pos_dict[f"position{i}"] = step_out[DECODER_POSITION_KEYS[i]]
+        return step_out[DECODER_HIDDEN_KEY]  # (1, 1, d_model) — decoder hidden
+
+    # 6. Prefill context
+    # Conditional path: real speaker context + real encoder output
+    # Unconditional path (CFG): ZERO context + zero encoder output (matching NeMo's dummy_additional_decoder_input)
+    print(f"Prefilling {T_ctx} context tokens into decoder...")
+    uncond_ctx_token = np.zeros((1, 1, d_model), dtype=np.float32)
+    for t in range(T_ctx):
+        ctx_token = speaker_emb[np.newaxis, np.newaxis, t, :]
+        run_decoder_step(ctx_token, encoder_output, text_mask, caches, positions)
+        if use_cfg:
+            run_decoder_step(uncond_ctx_token, uncond_encoder_output, uncond_text_mask, uncond_caches, uncond_positions)
+        if (t + 1) % 50 == 0:
+            print(f"  Prefilled {t + 1}/{T_ctx}")
+    print(f"  Prefill done. Position: {positions['position0'][0]:.0f}")
+
+    # 7. Autoregressive generation with local transformer
+    print(f"\nGenerating (max {max_steps} steps)...")
+    start_time = time.time()
+
+    current_codes = np.full(num_codebooks, audio_bos_id, dtype=np.int32)
+    all_predictions = []
+
+    for step in range(max_steps):
+        # Embed current audio codes
+        audio_embed = embed_audio_codes(current_codes, audio_emb_tables, num_codebooks)
+
+        # Run conditional decoder step → get hidden state
+        cond_hidden = run_decoder_step(audio_embed, encoder_output, text_mask, caches, positions)
+
+        if use_cfg:
+            # Run unconditional decoder step
+            uncond_hidden = run_decoder_step(
+                audio_embed, uncond_encoder_output, uncond_text_mask, uncond_caches, uncond_positions
+            )
+            uncond_decoder_hidden = uncond_hidden[0, 0]  # (d_model,)
+
+        decoder_hidden = cond_hidden[0, 0]  # (d_model,)
+
+        # Local transformer: sample codebooks autoregressively
+        # CFG is applied at the codebook logit level inside the LT (matching NeMo)
+        forbid_eos = (step < min_frames)
+        next_codes = local_transformer_sample(
+            decoder_hidden, lt_weights, audio_emb_tables,
+            num_codebooks, temperature, topk, forbid_eos,
+            uncond_decoder_hidden=uncond_decoder_hidden if use_cfg else None,
+            cfg_scale=cfg_scale if use_cfg else 1.0,
+        )
+
+        # Check EOS
+        is_eos = np.any(next_codes == audio_eos_id)
+        if is_eos and step >= min_frames:
+            print(f"  EOS at step {step}")
+            break
+
+        all_predictions.append(next_codes.copy())
+        current_codes = next_codes
+
+        if step % 20 == 0:
+            print(f"  Step {step}...")
+
+    gen_time = time.time() - start_time
+    num_frames = len(all_predictions)
+    print(f"Generated {num_frames} frames in {gen_time:.2f}s")
+
+    if num_frames == 0:
+        print("No audio generated!")
+        return
+
+    # 8. Decode with NanoCodec
+    print("Decoding audio with NanoCodec...")
+    predicted_codes = np.stack(all_predictions, axis=1)  # (num_cb, T_total)
+    T_total = predicted_codes.shape[1]
+
+    max_frames = 256
+    if T_total > max_frames:
+        print(f"  Warning: {T_total} frames > max {max_frames}, truncating")
+        predicted_codes = predicted_codes[:, :max_frames]
+        T_total = max_frames
+
+    padded = np.zeros((num_codebooks, max_frames), dtype=np.int32)
+    padded[:, :T_total] = predicted_codes
+
+    codec_out = nanocodec.predict({
+        "tokens": padded[np.newaxis, :, :].astype(np.int32),
+    })
+
+    audio = codec_out["audio"]
+    if audio.ndim > 1:
+        audio = audio.flatten()
+
+    expected_samples = T_total * constants["codec_samples_per_frame"]
+    audio = audio[:expected_samples]
+
+    peak = np.abs(audio).max()
+    if peak > 0:
+        audio = audio / peak * 0.9
+
+    sf.write(output_path, audio, sample_rate)
+    duration = len(audio) / sample_rate
+    rtf = gen_time / duration if duration > 0 else float("inf")
+    print(f"\nSaved to {output_path}")
+    print(f"Duration: {duration:.2f}s")
+    print(f"RTF: {rtf:.2f}x (generation time / audio duration)")
+
+
+def _tokenize_text(text, language, constants):
+    """Tokenize text using NeMo tokenizer."""
+    try:
+        from nemo.collections.tts.models import MagpieTTSModel
+        from nemo.collections.tts.parts.utils.tts_dataset_utils import (
+            chunk_and_tokenize_text_by_sentence,
+        )
+
+        model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+
+        language_tokenizer_map = {
+            "en": ["english_phoneme", "english"],
+            "es": ["spanish_phoneme", "spanish"],
+            "de": ["german_phoneme", "german"],
+            "fr": ["french_chartokenizer", "french"],
+            "zh": ["mandarin_phoneme", "mandarin"],
+            "ja": ["japanese_phoneme", "japanese"],
+            "hi": ["hindi_chartokenizer", "hindi"],
+            "it": ["italian_phoneme", "italian"],
+            "vi": ["vietnamese_phoneme", "vietnamese"],
+        }
+        available_tokenizers = list(model.tokenizer.tokenizers.keys())
+        tokenizer_name = available_tokenizers[0]
+        if language in language_tokenizer_map:
+            for candidate in language_tokenizer_map[language]:
+                if candidate in available_tokenizers:
+                    tokenizer_name = candidate
+                    break
+
+        # Force phoneme_probability=1.0 for deterministic phoneme lookup.
+        # Default 0.8 randomly falls back to graphemes for ~20% of words,
+        # which degrades synthesis quality since the model was trained on phonemes.
+        tok = model.tokenizer.tokenizers.get(tokenizer_name)
+        if tok is not None and hasattr(tok, "g2p") and hasattr(tok.g2p, "phoneme_probability"):
+            tok.g2p.phoneme_probability = 1.0
+
+        tokens, tokens_len, _ = chunk_and_tokenize_text_by_sentence(
+            text=text,
+            tokenizer_name=tokenizer_name,
+            text_tokenizer=model.tokenizer,
+            eos_token_id=model.eos_id,
+        )
+        return tokens[0].numpy().astype(np.int32)
+    except ImportError:
+        raise RuntimeError(
+            "NeMo toolkit required for tokenization. "
+            "Install with: uv sync --extra nemo"
+        )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate speech with Magpie TTS CoreML")
+    parser.add_argument("text", type=str, help="Text to synthesize")
+    parser.add_argument("--speaker", type=int, default=0, help="Speaker index (0-4)")
+    parser.add_argument("--language", type=str, default="en", help="Language code")
+    parser.add_argument("--output", type=str, default="magpie_output.wav", help="Output WAV path")
+    parser.add_argument("--temperature", type=float, default=0.6, help="Sampling temperature")
+    parser.add_argument("--topk", type=int, default=80, help="Top-k for sampling")
+    parser.add_argument("--max-steps", type=int, default=500, help="Max decoder steps")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+    parser.add_argument("--no-cfg", action="store_true", help="Disable classifier-free guidance")
+    parser.add_argument("--cfg-scale", type=float, default=2.5, help="CFG scale")
+    args = parser.parse_args()
+
+    generate(
+        text=args.text,
+        speaker=args.speaker,
+        language=args.language,
+        output_path=args.output,
+        temperature=args.temperature,
+        topk=args.topk,
+        max_steps=args.max_steps,
+        seed=args.seed,
+        use_cfg=not args.no_cfg,
+        cfg_scale=args.cfg_scale,
+    )
diff --git a/models/tts/magpie/coreml/generate_pytorch.py b/models/tts/magpie/coreml/generate_pytorch.py
new file mode 100644
index 0000000..0a19105
--- /dev/null
+++ b/models/tts/magpie/coreml/generate_pytorch.py
@@ -0,0 +1,166 @@
+"""Generate speech using the native NeMo/PyTorch MagpieTTS pipeline.
+
+Used to produce a reference WAV for comparison against CoreML output.
+"""
+import argparse
+import time
+
+import numpy as np
+import soundfile as sf
+import torch
+
+
+def generate(
+    text: str,
+    speaker: int = 0,
+    language: str = "en",
+    output_path: str = "pytorch_output.wav",
+    seed: int = 42,
+    use_cfg: bool = True,
+):
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+
+    from nemo.collections.tts.models import MagpieTTSModel
+    from nemo.collections.tts.parts.utils.tts_dataset_utils import (
+        chunk_and_tokenize_text_by_sentence,
+    )
+
+    print("Loading MagpieTTS model...")
+    model = MagpieTTSModel.from_pretrained("nvidia/magpie_tts_multilingual_357m")
+    model.eval()
+
+    # Resolve tokenizer
+    language_tokenizer_map = {
+        "en": ["english_phoneme", "english"],
+        "es": ["spanish_phoneme", "spanish"],
+        "de": ["german_phoneme", "german"],
+        "fr": ["french_chartokenizer", "french"],
+        "zh": ["mandarin_phoneme", "mandarin"],
+        "ja": ["japanese_phoneme", "japanese"],
+        "hi": ["hindi_chartokenizer", "hindi"],
+        "it": ["italian_phoneme", "italian"],
+        "vi": ["vietnamese_phoneme", "vietnamese"],
+    }
+
+    available_tokenizers = list(model.tokenizer.tokenizers.keys())
+    tokenizer_name = available_tokenizers[0]
+    if language in language_tokenizer_map:
+        for candidate in language_tokenizer_map[language]:
+            if candidate in available_tokenizers:
+                tokenizer_name = candidate
+                break
+
+    print(f"Text: '{text}'")
+    print(f"Speaker: {speaker}, Language: {language}, Tokenizer: {tokenizer_name}")
+
+    # Tokenize text — returns lists (one per chunk)
+    token_chunks, token_lens, text_chunks = chunk_and_tokenize_text_by_sentence(
+        text=text,
+        tokenizer_name=tokenizer_name,
+        text_tokenizer=model.tokenizer,
+        eos_token_id=model.eos_id,
+    )
+
+    # Use first chunk (short text fits in one)
+    tokens_tensor = token_chunks[0]  # already a tensor
+    token_len = token_lens[0]
+    print(f"Tokens: {token_len}, shape: {tokens_tensor.shape}")
+
+    # Build batch dict matching prepare_context_tensors expectations
+    batch = {
+        "text": tokens_tensor.unsqueeze(0) if tokens_tensor.ndim == 1 else tokens_tensor,
+        "text_lens": torch.tensor([token_len], dtype=torch.long),
+        "speaker_indices": torch.tensor([speaker], dtype=torch.long),
+    }
+
+    print(f"\nGenerating (use_cfg={use_cfg})...")
+    start = time.time()
+
+    with torch.no_grad():
+        output = model.infer_batch(
+            batch,
+            use_cfg=use_cfg,
+            use_local_transformer_for_inference=True,
+        )
+
+    elapsed = time.time() - start
+
+    # Extract audio from output
+    print(f"Output type: {type(output).__name__}")
+
+    # Inspect all fields
+    if hasattr(output, '__dict__'):
+        for k, v in vars(output).items():
+            if isinstance(v, torch.Tensor):
+                print(f"  {k}: shape={tuple(v.shape)}, dtype={v.dtype}")
+            elif isinstance(v, (int, float)):
+                print(f"  {k}: {v}")
+            elif v is not None:
+                print(f"  {k}: type={type(v).__name__}")
+    elif isinstance(output, dict):
+        for k, v in output.items():
+            if isinstance(v, torch.Tensor):
+                print(f"  {k}: shape={tuple(v.shape)}, dtype={v.dtype}")
+
+    # Find audio — try common attribute names
+    audio = None
+    for attr in ["predicted_audio", "audio", "audio_pred", "audio_output", "waveform"]:
+        if hasattr(output, attr) and getattr(output, attr) is not None:
+            audio = getattr(output, attr)
+            print(f"\nUsing attribute '{attr}' for audio")
+            break
+
+    # Fall back to decoding codec tokens
+    if audio is None:
+        token_preds = getattr(output, "token_predictions", None)
+        if isinstance(output, dict):
+            token_preds = output.get("token_predictions", token_preds)
+        if token_preds is not None:
+            print("\nDecoding codec tokens to audio via NanoCodec...")
+            print(f"  token_predictions shape: {tuple(token_preds.shape)}")
+            audio = model.codec_model.decode(tokens=token_preds.long())
+            if isinstance(audio, tuple):
+                audio = audio[0]
+            print(f"  Decoded audio shape: {tuple(audio.shape)}")
+
+    if audio is None:
+        raise ValueError(f"Could not find audio in output: {type(output)}")
+
+    if isinstance(audio, torch.Tensor):
+        audio = audio.cpu().numpy()
+    if hasattr(audio, 'squeeze'):
+        audio = audio.squeeze()
+
+    # Normalize same as CoreML pipeline
+    peak = np.abs(audio).max()
+    if peak > 0:
+        audio = audio / peak * 0.9
+
+    sample_rate = 22050
+    sf.write(output_path, audio, sample_rate)
+    duration = len(audio) / sample_rate
+    print(f"\nSaved to {output_path}")
+    print(f"Duration: {duration:.2f}s")
+    print(f"Generation time: {elapsed:.2f}s")
+    print(f"RTF: {elapsed / duration:.2f}x")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate speech with PyTorch MagpieTTS")
+    parser.add_argument("text", type=str, help="Text to synthesize")
+    parser.add_argument("--speaker", type=int, default=0, help="Speaker index (0-4)")
+    parser.add_argument("--language", type=str, default="en", help="Language code")
+    parser.add_argument("--output", type=str, default="pytorch_output.wav")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--no-cfg", action="store_true")
+    args = parser.parse_args()
+
+    generate(
+        text=args.text,
+        speaker=args.speaker,
+        language=args.language,
+        output_path=args.output,
+        seed=args.seed,
+        use_cfg=not args.no_cfg,
+    )
diff --git a/models/tts/magpie/coreml/openjtalk_wrapper.cpp b/models/tts/magpie/coreml/openjtalk_wrapper.cpp
new file mode 100644
index 0000000..df7846c
--- /dev/null
+++ b/models/tts/magpie/coreml/openjtalk_wrapper.cpp
@@ -0,0 +1,202 @@
+/**
+ * OpenJTalk G2P Wrapper Implementation
+ *
+ * Runs the OpenJTalk frontend pipeline (text2mecab → MeCab → NJD processing)
+ * and returns word features as JSON for the Swift-side pitch accent calculator.
+ *
+ * Based on pyopenjtalk's openjtalk.pyx implementation.
+ */
+
+#include "openjtalk_wrapper.h"
+
+#include <cstdlib>
+#include <cstdio>
+#include <cstring>
+
+// OpenJTalk headers — mecab.h contains C++ code (templates), so it must
+// be included outside extern "C". The other headers are pure C.
+#include "mecab.h"
+
+extern "C" {
+#include "njd.h"
+#include "jpcommon.h"
+#include "text2mecab.h"
+#include "mecab2njd.h"
+#include "njd2jpcommon.h"
+#include "njd_set_pronunciation.h"
+#include "njd_set_digit.h"
+#include "njd_set_accent_phrase.h"
+#include "njd_set_accent_type.h"
+#include "njd_set_unvoiced_vowel.h"
+#include "njd_set_long_vowel.h"
+}
+
+// Buffer size for text2mecab (matches pyopenjtalk)
+#define TEXT2MECAB_BUFFER_SIZE 8192
+
+// Global state (matches pyopenjtalk's singleton pattern)
+static Mecab g_mecab;
+static NJD g_njd;
+static JPCommon g_jpcommon;
+static int g_initialized = 0;
+
+// JSON string builder helpers
+static void json_append(char** buf, size_t* len, size_t* cap, const char* str) {
+    size_t slen = strlen(str);
+    while (*len + slen + 1 > *cap) {
+        *cap *= 2;
+        *buf = (char*)realloc(*buf, *cap);
+    }
+    memcpy(*buf + *len, str, slen);
+    *len += slen;
+    (*buf)[*len] = '\0';
+}
+
+// Escape a string for JSON (handles ", \, control chars)
+static void json_append_escaped(char** buf, size_t* len, size_t* cap, const char* str) {
+    json_append(buf, len, cap, "\"");
+    if (str) {
+        for (const char* p = str; *p; p++) {
+            switch (*p) {
+                case '"':  json_append(buf, len, cap, "\\\""); break;
+                case '\\': json_append(buf, len, cap, "\\\\"); break;
+                case '\n': json_append(buf, len, cap, "\\n"); break;
+                case '\r': json_append(buf, len, cap, "\\r"); break;
+                case '\t': json_append(buf, len, cap, "\\t"); break;
+                default:
+                    if ((unsigned char)*p < 0x20) {
+                        char esc[8];
+                        snprintf(esc, sizeof(esc), "\\u%04x", (unsigned char)*p);
+                        json_append(buf, len, cap, esc);
+                    } else {
+                        char c[2] = {*p, '\0'};
+                        json_append(buf, len, cap, c);
+                    }
+                    break;
+            }
+        }
+    }
+    json_append(buf, len, cap, "\"");
+}
+
+int openjtalk_init(const char* dict_path) {
+    if (g_initialized) {
+        openjtalk_destroy();
+    }
+
+    Mecab_initialize(&g_mecab);
+    NJD_initialize(&g_njd);
+    JPCommon_initialize(&g_jpcommon);
+
+    if (Mecab_load(&g_mecab, dict_path) != 1) {
+        Mecab_clear(&g_mecab);
+        NJD_clear(&g_njd);
+        JPCommon_clear(&g_jpcommon);
+        return 0;
+    }
+
+    g_initialized = 1;
+    return 1;
+}
+
+char* openjtalk_g2p(const char* text) {
+    if (!g_initialized || !text) {
+        return NULL;
+    }
+
+    char mecab_buf[TEXT2MECAB_BUFFER_SIZE];
+
+    // Step 1: Preprocess text for MeCab
+    text2mecab(mecab_buf, text);
+
+    // Step 2: Run MeCab morphological analysis
+    if (Mecab_analysis(&g_mecab, mecab_buf) != 1) {
+        return NULL;
+    }
+
+    // Step 3: Convert MeCab output to NJD
+    mecab2njd(&g_njd, Mecab_get_feature(&g_mecab), Mecab_get_size(&g_mecab));
+
+    // Step 4: Run NJD processing pipeline
+    njd_set_pronunciation(&g_njd);
+    njd_set_digit(&g_njd);
+    njd_set_accent_phrase(&g_njd);
+    njd_set_accent_type(&g_njd);
+    njd_set_unvoiced_vowel(&g_njd);
+    njd_set_long_vowel(&g_njd);
+
+    // Step 5: Build JSON array from NJD nodes
+    size_t cap = 1024;
+    size_t len = 0;
+    char* json = (char*)malloc(cap);
+    json[0] = '\0';
+
+    json_append(&json, &len, &cap, "[");
+
+    int first = 1;
+    NJDNode* node = g_njd.head;
+    while (node != NULL) {
+        if (!first) {
+            json_append(&json, &len, &cap, ",");
+        }
+        first = 0;
+
+        json_append(&json, &len, &cap, "{");
+
+        // "string" field
+        json_append(&json, &len, &cap, "\"string\":");
+        json_append_escaped(&json, &len, &cap, NJDNode_get_string(node));
+
+        // "pos" field
+        json_append(&json, &len, &cap, ",\"pos\":");
+        json_append_escaped(&json, &len, &cap, NJDNode_get_pos(node));
+
+        // "pron" field
+        json_append(&json, &len, &cap, ",\"pron\":");
+        json_append_escaped(&json, &len, &cap, NJDNode_get_pron(node));
+
+        // "acc" field
+        char num_buf[32];
+        json_append(&json, &len, &cap, ",\"acc\":");
+        snprintf(num_buf, sizeof(num_buf), "%d", NJDNode_get_acc(node));
+        json_append(&json, &len, &cap, num_buf);
+
+        // "mora_size" field
+        json_append(&json, &len, &cap, ",\"mora_size\":");
+        snprintf(num_buf, sizeof(num_buf), "%d", NJDNode_get_mora_size(node));
+        json_append(&json, &len, &cap, num_buf);
+
+        // "chain_flag" field
+        json_append(&json, &len, &cap, ",\"chain_flag\":");
+        snprintf(num_buf, sizeof(num_buf), "%d", NJDNode_get_chain_flag(node));
+        json_append(&json, &len, &cap, num_buf);
+
+        json_append(&json, &len, &cap, "}");
+
+        node = node->next;
+    }
+
+    json_append(&json, &len, &cap, "]");
+
+    // Reset NJD and JPCommon for next call
+    NJD_refresh(&g_njd);
+    JPCommon_refresh(&g_jpcommon);
+    Mecab_refresh(&g_mecab);
+
+    return json;
+}
+
+void openjtalk_free_string(char* s) {
+    if (s) {
+        free(s);
+    }
+}
+
+void openjtalk_destroy(void) {
+    if (g_initialized) {
+        Mecab_clear(&g_mecab);
+        NJD_clear(&g_njd);
+        JPCommon_clear(&g_jpcommon);
+        g_initialized = 0;
+    }
+}
diff --git a/models/tts/magpie/coreml/openjtalk_wrapper.h b/models/tts/magpie/coreml/openjtalk_wrapper.h
new file mode 100644
index 0000000..0b0fe04
--- /dev/null
+++ b/models/tts/magpie/coreml/openjtalk_wrapper.h
@@ -0,0 +1,53 @@
+/**
+ * OpenJTalk G2P Wrapper
+ *
+ * Thin C API over OpenJTalk's frontend pipeline for Japanese text-to-phoneme conversion.
+ * Returns NJD (Nihongo Jisho Data) features as JSON for pitch accent calculation in Swift.
+ */
+
+#ifndef OPENJTALK_WRAPPER_H
+#define OPENJTALK_WRAPPER_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * Initialize OpenJTalk with a MeCab dictionary.
+ *
+ * @param dict_path Path to open_jtalk_dic_utf_8-1.11 directory
+ * @return 1 on success, 0 on failure
+ */
+int openjtalk_init(const char* dict_path);
+
+/**
+ * Run G2P frontend on Japanese text.
+ *
+ * Returns NJD word features as a JSON array string. Each element:
+ *   {"string":"surface", "pos":"品詞", "pron":"カタカナ",
+ *    "acc":N, "mora_size":N, "chain_flag":N}
+ *
+ * @param text UTF-8 encoded Japanese text
+ * @return Newly allocated JSON string. Caller must free with openjtalk_free_string().
+ *         Returns NULL on error.
+ */
+char* openjtalk_g2p(const char* text);
+
+/**
+ * Free a string returned by openjtalk_g2p().
+ *
+ * @param s String to free. Safe to call with NULL.
+ */
+void openjtalk_free_string(char* s);
+
+/**
+ * Release all OpenJTalk resources.
+ * After calling this, openjtalk_init() must be called again before openjtalk_g2p().
+ */
+void openjtalk_destroy(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* OPENJTALK_WRAPPER_H */
diff --git a/models/tts/magpie/coreml/traceable/traceable_decoder_prefill.py b/models/tts/magpie/coreml/traceable/traceable_decoder_prefill.py
new file mode 100644
index 0000000..5265cb2
--- /dev/null
+++ b/models/tts/magpie/coreml/traceable/traceable_decoder_prefill.py
@@ -0,0 +1,268 @@
+"""Traceable decoder prefill model for CoreML conversion.
+
+Processes all speaker context tokens in a single forward pass instead of
+110 sequential decoder_step calls. Outputs populated KV caches that can
+be fed directly into decoder_step for autoregressive generation.
+
+Each layer:
+1. Causal self-attention over all T_ctx tokens (dense, no cache)
+2. Cross-attention to encoder output
+3. FFN
+4. Write K,V into output cache format matching decoder_step
+
+Usage from convert/:
+    python convert/convert_decoder_prefill.py
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+class PrefillCausalSelfAttention(nn.Module):
+    """Dense causal self-attention for prefill (no KV cache needed)."""
+
+    def __init__(self, d_model, n_heads, d_head=None):
+        super().__init__()
+        self.d_head = d_head or d_model // n_heads
+        self.n_heads = n_heads
+        self.scale = self.d_head ** -0.5
+        self.qkv_proj = nn.Linear(d_model, 3 * n_heads * self.d_head, bias=False)
+        self.o_proj = nn.Linear(n_heads * self.d_head, d_model, bias=False)
+
+    def forward(self, x):
+        """
+        Args:
+            x: (B, T, d_model)
+        Returns:
+            output: (B, T, d_model)
+            k: (B, T, H, D) - keys for cache
+            v: (B, T, H, D) - values for cache
+        """
+        B, T, _ = x.shape
+        qkv = self.qkv_proj(x).view(B, T, 3, self.n_heads, self.d_head)
+        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+
+        q = q.transpose(1, 2)  # (B, H, T, D)
+        k_t = k.transpose(1, 2)
+        v_t = v.transpose(1, 2)
+
+        attn = torch.matmul(q, k_t.transpose(-2, -1)) * self.scale  # (B, H, T, T)
+
+        # Causal mask: lower triangular
+        causal = torch.tril(torch.ones(T, T, dtype=x.dtype, device=x.device))
+        attn = attn + (1.0 - causal) * (-1e9)
+        attn = F.softmax(attn, dim=-1)
+
+        out = torch.matmul(attn, v_t)  # (B, H, T, D)
+        out = out.transpose(1, 2).reshape(B, T, -1)
+        out = self.o_proj(out)
+
+        return out, k, v
+
+
+class PrefillCrossAttention(nn.Module):
+    """Cross-attention to encoder output (identical to step version)."""
+
+    def __init__(self, d_model, n_heads, d_memory, d_head=None):
+        super().__init__()
+        self.d_head = d_head or d_model // n_heads
+        self.n_heads = n_heads
+        self.scale = self.d_head ** -0.5
+        self.q_proj = nn.Linear(d_model, n_heads * self.d_head, bias=False)
+        self.kv_proj = nn.Linear(d_memory, 2 * n_heads * self.d_head, bias=False)
+        self.o_proj = nn.Linear(n_heads * self.d_head, d_model, bias=False)
+
+    def forward(self, x, memory, memory_mask=None):
+        B, T_q, _ = x.shape
+        T_m = memory.shape[1]
+        q = self.q_proj(x).view(B, T_q, self.n_heads, self.d_head).transpose(1, 2)
+        kv = self.kv_proj(memory).view(B, T_m, 2, self.n_heads, self.d_head)
+        k, v = kv[:, :, 0].transpose(1, 2), kv[:, :, 1].transpose(1, 2)
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+        if memory_mask is not None:
+            attn_mask = memory_mask.unsqueeze(1).unsqueeze(2)
+            attn = attn.masked_fill(~attn_mask, float("-inf"))
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).reshape(B, T_q, -1)
+        return self.o_proj(out)
+
+
+class PrefillFFN(nn.Module):
+    """Positionwise feed-forward (identical to step version)."""
+
+    def __init__(self, d_model, d_ffn, kernel_size=1):
+        super().__init__()
+        self.conv1 = nn.Conv1d(d_model, d_ffn, kernel_size, padding=0, bias=False)
+        self.conv2 = nn.Conv1d(d_ffn, d_model, kernel_size, padding=0, bias=False)
+        self.act = nn.GELU(approximate="tanh")
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        x = self.act(self.conv1(x))
+        x = self.conv2(x)
+        return x.transpose(1, 2)
+
+
+class PrefillDecoderLayer(nn.Module):
+    """Single decoder layer for prefill: self-attn + cross-attn + FFN."""
+
+    def __init__(self, d_model, d_ffn, sa_n_heads, xa_n_heads, xa_d_memory, kernel_size=1, xa_d_head=None):
+        super().__init__()
+        self.norm_sa = nn.LayerNorm(d_model, bias=False)
+        self.self_attn = PrefillCausalSelfAttention(d_model, sa_n_heads)
+
+        self.has_xattn = xa_n_heads is not None
+        if self.has_xattn:
+            self.norm_xa_query = nn.LayerNorm(d_model, bias=False)
+            self.norm_xa_memory = nn.LayerNorm(xa_d_memory, bias=False)
+            self.cross_attn = PrefillCrossAttention(d_model, xa_n_heads, xa_d_memory, xa_d_head)
+
+        self.norm_ff = nn.LayerNorm(d_model, bias=False)
+        self.ffn = PrefillFFN(d_model, d_ffn, kernel_size)
+
+    def forward(self, x, encoder_output=None, encoder_mask=None):
+        """
+        Returns:
+            x: (B, T, d_model)
+            k: (B, T, H, D) - self-attn keys
+            v: (B, T, H, D) - self-attn values
+        """
+        residual = x
+        x_norm = self.norm_sa(x)
+        sa_out, k, v = self.self_attn(x_norm)
+        x = residual + sa_out
+
+        if self.has_xattn and encoder_output is not None:
+            residual = x
+            q_norm = self.norm_xa_query(x)
+            m_norm = self.norm_xa_memory(encoder_output)
+            xa_out = self.cross_attn(q_norm, m_norm, encoder_mask)
+            x = residual + xa_out
+
+        residual = x
+        x = self.norm_ff(x)
+        x = self.ffn(x)
+        x = residual + x
+
+        return x, k, v
+
+
+class TraceableDecoderPrefill(nn.Module):
+    """Batched prefill decoder for CoreML.
+
+    Processes T_ctx speaker context tokens in one pass and outputs
+    KV caches compatible with decoder_step for subsequent generation.
+
+    Inputs:
+        audio_embed: (B, T_ctx, d_model) - all context embeddings
+        encoder_output: (B, T_enc, d_model) - text encoder output
+        encoder_mask: (B, T_enc) - text mask
+
+    Outputs:
+        cache{i}: (2, B, max_seq_len, H, D) - KV cache per layer
+            First T_ctx positions populated, rest zero-padded.
+    """
+
+    def __init__(self, n_layers, d_model, d_ffn, sa_n_heads, xa_n_heads, xa_d_memory,
+                 kernel_size=1, xa_d_head=None, max_seq_len=512,
+                 use_pos_emb=False, max_pos=2048, t_ctx=110):
+        super().__init__()
+        self.n_layers = n_layers
+        self.d_model = d_model
+        self.max_seq_len = max_seq_len
+        self.t_ctx = t_ctx
+        self.use_pos_emb = use_pos_emb
+        self.n_heads = sa_n_heads
+        self.d_head = d_model // sa_n_heads
+
+        if use_pos_emb:
+            self.position_embeddings = nn.Embedding(max_pos, d_model)
+
+        self.layers = nn.ModuleList([
+            PrefillDecoderLayer(
+                d_model, d_ffn, sa_n_heads, xa_n_heads, xa_d_memory, kernel_size, xa_d_head
+            )
+            for _ in range(n_layers)
+        ])
+
+    def forward(self, audio_embed, encoder_output, encoder_mask):
+        H, D = self.n_heads, self.d_head
+        max_seq = self.max_seq_len
+        # Use concrete Python int for pad_len so CoreML tracing doesn't
+        # produce a symbolic tensor that fails in F.pad / _int cast.
+        pad_len = max_seq - self.t_ctx
+
+        x = audio_embed
+
+        if self.use_pos_emb:
+            pos_ids = torch.arange(self.t_ctx, device=x.device).unsqueeze(0)
+            x = x + self.position_embeddings(pos_ids)
+
+        # Collect KV caches from all layers
+        all_caches = []
+        for layer in self.layers:
+            x, k, v = layer(x, encoder_output, encoder_mask)
+            # k, v: (B, T_ctx, H, D) -> pad to (B, max_seq, H, D)
+            k_padded = F.pad(k, (0, 0, 0, 0, 0, pad_len))  # (B, max_seq, H, D)
+            v_padded = F.pad(v, (0, 0, 0, 0, 0, pad_len))
+            # Stack to (2, B, max_seq, H, D)
+            cache = torch.stack([k_padded, v_padded], dim=0)
+            all_caches.append(cache)
+
+        # Return flat caches (matching decoder_step format)
+        return tuple(all_caches)
+
+    @classmethod
+    def from_magpie(cls, model, t_ctx=None):
+        """Create from a loaded MagpieTTSModel, copying decoder weights."""
+        cfg = model.cfg
+        dec_cfg = dict(cfg.decoder)
+
+        # Determine T_ctx from baked speaker embeddings (if not overridden)
+        if t_ctx is None:
+            t_ctx = 110  # default
+            if model.has_baked_context_embedding and model._baked_embedding_T is not None:
+                t_ctx = int(model._baked_embedding_T.item())
+
+        wrapper = cls(
+            n_layers=dec_cfg["n_layers"],
+            d_model=dec_cfg["d_model"],
+            d_ffn=dec_cfg["d_ffn"],
+            sa_n_heads=dec_cfg["sa_n_heads"],
+            xa_n_heads=dec_cfg.get("xa_n_heads"),
+            xa_d_memory=dec_cfg.get("xa_d_memory"),
+            kernel_size=dec_cfg.get("kernel_size", 1),
+            xa_d_head=dec_cfg.get("xa_d_head"),
+            max_seq_len=512,
+            use_pos_emb=dec_cfg.get("use_learnable_pos_emb", False),
+            max_pos=dec_cfg.get("max_length_causal_mask", 2048),
+            t_ctx=t_ctx,
+        )
+
+        # Copy positional embeddings
+        if wrapper.use_pos_emb and model.decoder.position_embeddings is not None:
+            wrapper.position_embeddings.weight.data.copy_(model.decoder.position_embeddings.weight.data)
+
+        # Copy decoder layers (same weight names as traceable_decoder_step)
+        for src_layer, dst_layer in zip(model.decoder.layers, wrapper.layers):
+            # Self-attention
+            dst_layer.self_attn.qkv_proj.weight.data.copy_(src_layer.self_attention.qkv_net.weight.data)
+            dst_layer.self_attn.o_proj.weight.data.copy_(src_layer.self_attention.o_net.weight.data)
+            dst_layer.norm_sa.weight.data.copy_(src_layer.norm_self.weight.data)
+
+            # Cross-attention
+            if dst_layer.has_xattn and hasattr(src_layer, "cross_attention"):
+                dst_layer.cross_attn.q_proj.weight.data.copy_(src_layer.cross_attention.q_net.weight.data)
+                dst_layer.cross_attn.kv_proj.weight.data.copy_(src_layer.cross_attention.kv_net.weight.data)
+                dst_layer.cross_attn.o_proj.weight.data.copy_(src_layer.cross_attention.o_net.weight.data)
+                dst_layer.norm_xa_query.weight.data.copy_(src_layer.norm_xattn_query.weight.data)
+                dst_layer.norm_xa_memory.weight.data.copy_(src_layer.norm_xattn_memory.weight.data)
+
+            # FFN
+            dst_layer.norm_ff.weight.data.copy_(src_layer.norm_pos_ff.weight.data)
+            dst_layer.ffn.conv1.weight.data.copy_(src_layer.pos_ff.proj.conv.weight.data)
+            dst_layer.ffn.conv2.weight.data.copy_(src_layer.pos_ff.o_net.conv.weight.data)
+
+        return wrapper
diff --git a/models/tts/magpie/coreml/traceable/traceable_decoder_step.py b/models/tts/magpie/coreml/traceable/traceable_decoder_step.py
new file mode 100644
index 0000000..3cfe896
--- /dev/null
+++ b/models/tts/magpie/coreml/traceable/traceable_decoder_step.py
@@ -0,0 +1,359 @@
+"""Traceable decoder step wrapper for CoreML conversion.
+
+The decoder is a causal transformer with cross-attention to the encoder output.
+For CoreML, we implement it as a single-step model with explicit KV cache I/O,
+following the PocketTTS pattern.
+
+Each step:
+1. Takes one audio embedding token + encoder output
+2. Runs through all decoder layers with causal self-attention + cross-attention
+3. Returns logits for next token + updated KV caches
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from typing import Tuple, List
+
+
+class TraceableCausalSelfAttention(nn.Module):
+    """Single-step causal self-attention with KV cache."""
+
+    def __init__(self, d_model, n_heads, d_head=None):
+        super().__init__()
+        self.d_head = d_head or d_model // n_heads
+        self.n_heads = n_heads
+        self.scale = self.d_head ** -0.5
+        self.qkv_proj = nn.Linear(d_model, 3 * n_heads * self.d_head, bias=False)
+        self.o_proj = nn.Linear(n_heads * self.d_head, d_model, bias=False)
+
+    def forward(self, x, kv_cache, position):
+        """
+        Args:
+            x: (B, 1, d_model) - single token embedding
+            kv_cache: (2, B, max_seq, H, D) - [key, value] cache
+            position: (1,) - current write position in cache
+        Returns:
+            output: (B, 1, d_model)
+            new_kv_cache: (2, B, max_seq, H, D) - updated cache
+            new_position: (1,) - incremented position
+        """
+        B, T, _ = x.shape  # T=1 for single step
+        max_seq = kv_cache.shape[2]
+
+        qkv = self.qkv_proj(x)
+        qkv = qkv.view(B, T, 3, self.n_heads, self.d_head)
+        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+
+        # Write new k,v to cache using scatter (CoreML-compatible)
+        # Build one-hot mask for position to avoid advanced indexing
+        pos_idx = position.to(torch.long)
+        one_hot = torch.zeros(max_seq, dtype=x.dtype, device=x.device)
+        one_hot[pos_idx] = 1.0
+        # one_hot: (max_seq,) → broadcast to (1, B, max_seq, H, D)
+        mask = one_hot.view(1, 1, max_seq, 1, 1)
+
+        k_new = k.squeeze(1).unsqueeze(0).unsqueeze(2)  # (1, B, 1, H, D) → broadcast to (1, B, max_seq, H, D)
+        v_new = v.squeeze(1).unsqueeze(0).unsqueeze(2)
+
+        # new_cache = (1-mask)*old_cache + mask*new_kv
+        new_cache_k = kv_cache[0:1] * (1.0 - mask) + k_new * mask  # (1, B, max_seq, H, D)
+        new_cache_v = kv_cache[1:2] * (1.0 - mask) + v_new * mask
+        new_cache = torch.cat([new_cache_k, new_cache_v], dim=0)  # (2, B, max_seq, H, D)
+
+        # Attend to all positions with a causal mask (positions > pos_idx are masked out)
+        # Build mask: 1 for positions <= pos_idx, 0 for positions > pos_idx
+        positions_range = torch.arange(max_seq, dtype=x.dtype, device=x.device)
+        causal_mask = (positions_range <= position).float()  # (max_seq,)
+        causal_mask = causal_mask.view(1, 1, 1, max_seq)  # (1, 1, 1, max_seq)
+
+        q = q.transpose(1, 2)  # (B, H, 1, D)
+        k_full = new_cache[0].transpose(1, 2)  # (B, H, max_seq, D)
+        v_full = new_cache[1].transpose(1, 2)
+
+        attn = torch.matmul(q, k_full.transpose(-2, -1)) * self.scale  # (B, H, 1, max_seq)
+        attn = attn + (1.0 - causal_mask) * (-1e9)  # mask future positions
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v_full)
+
+        out = out.transpose(1, 2).reshape(B, 1, -1)
+        out = self.o_proj(out)
+
+        new_position = position + 1.0
+        return out, new_cache, new_position
+
+
+class TraceableCrossAttention(nn.Module):
+    """Cross-attention to encoder output (non-causal, full attention)."""
+
+    def __init__(self, d_model, n_heads, d_memory, d_head=None):
+        super().__init__()
+        self.d_head = d_head or d_model // n_heads
+        self.n_heads = n_heads
+        self.scale = self.d_head ** -0.5
+        self.q_proj = nn.Linear(d_model, n_heads * self.d_head, bias=False)
+        self.kv_proj = nn.Linear(d_memory, 2 * n_heads * self.d_head, bias=False)
+        self.o_proj = nn.Linear(n_heads * self.d_head, d_model, bias=False)
+
+    def forward(self, x, memory, memory_mask=None):
+        """
+        Args:
+            x: (B, 1, d_model) - query
+            memory: (B, T_enc, d_memory) - encoder output
+            memory_mask: (B, T_enc) bool - True=keep
+        Returns:
+            output: (B, 1, d_model)
+        """
+        B, T_q, _ = x.shape
+        T_m = memory.shape[1]
+
+        q = self.q_proj(x).view(B, T_q, self.n_heads, self.d_head).transpose(1, 2)
+        kv = self.kv_proj(memory).view(B, T_m, 2, self.n_heads, self.d_head)
+        k, v = kv[:, :, 0].transpose(1, 2), kv[:, :, 1].transpose(1, 2)
+
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        if memory_mask is not None:
+            attn_mask = memory_mask.unsqueeze(1).unsqueeze(2)  # (B, 1, 1, T_m)
+            attn = attn.masked_fill(~attn_mask, float("-inf"))
+
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).reshape(B, T_q, -1)
+        return self.o_proj(out)
+
+
+class TraceableFFN(nn.Module):
+    """Positionwise feed-forward for decoder."""
+
+    def __init__(self, d_model, d_ffn, kernel_size=1):
+        super().__init__()
+        self.conv1 = nn.Conv1d(d_model, d_ffn, kernel_size, padding=0, bias=False)
+        self.conv2 = nn.Conv1d(d_ffn, d_model, kernel_size, padding=0, bias=False)
+        self.act = nn.GELU(approximate="tanh")
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        x = self.act(self.conv1(x))
+        x = self.conv2(x)
+        return x.transpose(1, 2)
+
+
+class TraceableDecoderLayer(nn.Module):
+    """Single decoder transformer layer with self-attn, cross-attn, and FFN."""
+
+    def __init__(self, d_model, d_ffn, sa_n_heads, xa_n_heads, xa_d_memory, kernel_size=1, xa_d_head=None):
+        super().__init__()
+        self.norm_sa = nn.LayerNorm(d_model, bias=False)
+        self.self_attn = TraceableCausalSelfAttention(d_model, sa_n_heads)
+
+        self.has_xattn = xa_n_heads is not None
+        if self.has_xattn:
+            self.norm_xa_query = nn.LayerNorm(d_model, bias=False)
+            self.norm_xa_memory = nn.LayerNorm(xa_d_memory, bias=False)
+            self.cross_attn = TraceableCrossAttention(d_model, xa_n_heads, xa_d_memory, xa_d_head)
+
+        self.norm_ff = nn.LayerNorm(d_model, bias=False)
+        self.ffn = TraceableFFN(d_model, d_ffn, kernel_size)
+
+    def forward(self, x, kv_cache, position, encoder_output=None, encoder_mask=None):
+        """
+        Returns:
+            x: (B, 1, d_model)
+            new_kv_cache: updated cache
+            new_position: incremented position
+        """
+        # Self-attention
+        residual = x
+        x_norm = self.norm_sa(x)
+        sa_out, new_kv_cache, new_position = self.self_attn(x_norm, kv_cache, position)
+        x = residual + sa_out
+
+        # Cross-attention
+        if self.has_xattn and encoder_output is not None:
+            residual = x
+            q_norm = self.norm_xa_query(x)
+            m_norm = self.norm_xa_memory(encoder_output)
+            xa_out = self.cross_attn(q_norm, m_norm, encoder_mask)
+            x = residual + xa_out
+
+        # FFN
+        residual = x
+        x = self.norm_ff(x)
+        x = self.ffn(x)
+        x = residual + x
+
+        return x, new_kv_cache, new_position
+
+
+class TraceableDecoderStep(nn.Module):
+    """Complete single-step decoder for CoreML.
+
+    Takes one audio token embedding, runs through all decoder layers with
+    KV cache, and outputs logits for next codebook tokens.
+
+    The KV caches are passed as flat arguments (not lists) for torch.jit.trace.
+    """
+
+    def __init__(self, n_layers, d_model, d_ffn, sa_n_heads, xa_n_heads, xa_d_memory,
+                 kernel_size=1, xa_d_head=None, max_seq_len=512,
+                 use_pos_emb=False, max_pos=2048,
+                 num_codebooks=8, num_tokens_per_codebook=2024, frame_stacking_factor=1):
+        super().__init__()
+        self.n_layers = n_layers
+        self.d_model = d_model
+        self.max_seq_len = max_seq_len
+        self.use_pos_emb = use_pos_emb
+        self.num_codebooks = num_codebooks
+        self.num_tokens_per_codebook = num_tokens_per_codebook
+        self.frame_stacking_factor = frame_stacking_factor
+        self.d_head = d_model // sa_n_heads
+
+        if use_pos_emb:
+            self.position_embeddings = nn.Embedding(max_pos, d_model)
+
+        self.layers = nn.ModuleList([
+            TraceableDecoderLayer(
+                d_model, d_ffn, sa_n_heads, xa_n_heads, xa_d_memory, kernel_size, xa_d_head
+            )
+            for _ in range(n_layers)
+        ])
+
+        self.norm_out = nn.Identity()  # May be replaced if model uses apply_norm_out
+
+        # Final projection: decoder hidden → codebook logits
+        self.final_proj = nn.Linear(
+            d_model,
+            num_codebooks * num_tokens_per_codebook * frame_stacking_factor,
+        )
+
+    def forward(self, audio_embed, encoder_output, encoder_mask,
+                # Flat KV cache args (one pair per layer)
+                cache0, pos0, cache1, pos1, cache2, pos2,
+                cache3, pos3, cache4, pos4, cache5, pos5,
+                cache6, pos6, cache7, pos7, cache8, pos8,
+                cache9, pos9, cache10, pos10, cache11, pos11):
+        """
+        Args:
+            audio_embed: (B, 1, d_model) - embedded audio token(s)
+            encoder_output: (B, T_enc, d_model) - text encoder output
+            encoder_mask: (B, T_enc) - text mask
+            cache{i}: (2, B, max_seq, H, D) - KV cache per layer
+            pos{i}: (1,) - current position per layer
+
+        Returns:
+            logits: (B, 1, num_codebooks * num_tokens * frame_stacking)
+            decoder_hidden: (B, 1, d_model) - for local transformer
+            new_cache{i}: updated caches
+            new_pos{i}: updated positions
+        """
+        caches = [cache0, cache1, cache2, cache3, cache4, cache5,
+                  cache6, cache7, cache8, cache9, cache10, cache11]
+        positions = [pos0, pos1, pos2, pos3, pos4, pos5,
+                     pos6, pos7, pos8, pos9, pos10, pos11]
+
+        x = audio_embed
+
+        # Add positional embedding
+        if self.use_pos_emb:
+            pos_idx = positions[0].to(torch.long)
+            x = x + self.position_embeddings(pos_idx).unsqueeze(0)
+
+        new_caches = []
+        new_positions = []
+
+        for i, layer in enumerate(self.layers):
+            x, new_cache, new_pos = layer(
+                x, caches[i], positions[i],
+                encoder_output=encoder_output,
+                encoder_mask=encoder_mask,
+            )
+            new_caches.append(new_cache)
+            new_positions.append(new_pos)
+
+        x = self.norm_out(x)
+        decoder_hidden = x
+
+        logits = self.final_proj(x)
+
+        return (logits, decoder_hidden,
+                new_caches[0], new_positions[0],
+                new_caches[1], new_positions[1],
+                new_caches[2], new_positions[2],
+                new_caches[3], new_positions[3],
+                new_caches[4], new_positions[4],
+                new_caches[5], new_positions[5],
+                new_caches[6], new_positions[6],
+                new_caches[7], new_positions[7],
+                new_caches[8], new_positions[8],
+                new_caches[9], new_positions[9],
+                new_caches[10], new_positions[10],
+                new_caches[11], new_positions[11])
+
+    @classmethod
+    def from_magpie(cls, model):
+        """Create from a loaded MagpieTTSModel."""
+        cfg = model.cfg
+        dec_cfg = dict(cfg.decoder)
+
+        wrapper = cls(
+            n_layers=dec_cfg["n_layers"],
+            d_model=dec_cfg["d_model"],
+            d_ffn=dec_cfg["d_ffn"],
+            sa_n_heads=dec_cfg["sa_n_heads"],
+            xa_n_heads=dec_cfg.get("xa_n_heads"),
+            xa_d_memory=dec_cfg.get("xa_d_memory"),
+            kernel_size=dec_cfg.get("kernel_size", 1),
+            xa_d_head=dec_cfg.get("xa_d_head"),
+            max_seq_len=512,
+            use_pos_emb=dec_cfg.get("use_learnable_pos_emb", False),
+            max_pos=dec_cfg.get("max_length_causal_mask", 2048),
+            num_codebooks=model.num_audio_codebooks,
+            num_tokens_per_codebook=model.num_all_tokens_per_codebook,
+            frame_stacking_factor=model.frame_stacking_factor,
+        )
+
+        # Copy positional embeddings
+        if wrapper.use_pos_emb and model.decoder.position_embeddings is not None:
+            wrapper.position_embeddings.weight.data.copy_(model.decoder.position_embeddings.weight.data)
+
+        # Copy decoder layers
+        # NeMo TransformerLayer attr names:
+        #   self_attention (SelfAttention) with qkv_net, o_net
+        #   cross_attention (CrossAttention) with q_net, kv_net, o_net
+        #   norm_self, norm_xattn_query, norm_xattn_memory, norm_pos_ff (LayerNorm, bias=False)
+        #   pos_ff (PositionwiseConvFF) with proj.conv, o_net.conv (Conv1d)
+        for i, (src_layer, dst_layer) in enumerate(zip(model.decoder.layers, wrapper.layers)):
+            # Self-attention
+            dst_layer.self_attn.qkv_proj.weight.data.copy_(src_layer.self_attention.qkv_net.weight.data)
+            dst_layer.self_attn.o_proj.weight.data.copy_(src_layer.self_attention.o_net.weight.data)
+
+            # Self-attn norm (bias=False in NeMo)
+            dst_layer.norm_sa.weight.data.copy_(src_layer.norm_self.weight.data)
+
+            # Cross-attention (if present)
+            if dst_layer.has_xattn and hasattr(src_layer, "cross_attention"):
+                dst_layer.cross_attn.q_proj.weight.data.copy_(src_layer.cross_attention.q_net.weight.data)
+                dst_layer.cross_attn.kv_proj.weight.data.copy_(src_layer.cross_attention.kv_net.weight.data)
+                dst_layer.cross_attn.o_proj.weight.data.copy_(src_layer.cross_attention.o_net.weight.data)
+
+                dst_layer.norm_xa_query.weight.data.copy_(src_layer.norm_xattn_query.weight.data)
+                dst_layer.norm_xa_memory.weight.data.copy_(src_layer.norm_xattn_memory.weight.data)
+
+            # FFN norm (bias=False in NeMo)
+            dst_layer.norm_ff.weight.data.copy_(src_layer.norm_pos_ff.weight.data)
+
+            # FFN (Conv1d via PositionwiseConvFF, bias=False)
+            dst_layer.ffn.conv1.weight.data.copy_(src_layer.pos_ff.proj.conv.weight.data)
+            dst_layer.ffn.conv2.weight.data.copy_(src_layer.pos_ff.o_net.conv.weight.data)
+
+        # Output norm
+        if hasattr(model.decoder, "norm_out") and isinstance(model.decoder.norm_out, nn.LayerNorm):
+            wrapper.norm_out = nn.LayerNorm(dec_cfg["d_model"], bias=False)
+            wrapper.norm_out.weight.data.copy_(model.decoder.norm_out.weight.data)
+
+        # Final projection
+        wrapper.final_proj.weight.data.copy_(model.final_proj.weight.data)
+        wrapper.final_proj.bias.data.copy_(model.final_proj.bias.data)
+
+        return wrapper
diff --git a/models/tts/magpie/coreml/traceable/traceable_text_encoder.py b/models/tts/magpie/coreml/traceable/traceable_text_encoder.py
new file mode 100644
index 0000000..50214ae
--- /dev/null
+++ b/models/tts/magpie/coreml/traceable/traceable_text_encoder.py
@@ -0,0 +1,246 @@
+"""Traceable text encoder wrapper for CoreML conversion.
+
+The text encoder is a causal transformer (transformer_2501.Transformer) that
+encodes text tokens into conditioning for the decoder's cross-attention.
+
+Architecture (verified from model inspection):
+- Causal self-attention (lower-triangular mask)
+- Causal Conv1d FFN (kernel_size=3, left-padded)
+- LayerNorm with bias=False
+- Learnable positional embeddings
+- Output LayerNorm
+
+This wrapper:
+- Handles text embedding + learnable positional encoding
+- Runs the encoder transformer stack with causal masking
+- Applies causal padding for Conv1d FFN layers
+- Produces fixed-shape outputs suitable for torch.jit.trace
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional
+
+
+class TraceableSelfAttention(nn.Module):
+    """Causal self-attention for encoder."""
+
+    def __init__(self, d_model, n_heads, d_head=None):
+        super().__init__()
+        self.d_head = d_head or d_model // n_heads
+        self.n_heads = n_heads
+        self.scale = self.d_head ** -0.5
+        self.qkv_proj = nn.Linear(d_model, 3 * n_heads * self.d_head, bias=False)
+        self.o_proj = nn.Linear(n_heads * self.d_head, d_model, bias=False)
+
+    def forward(self, x, mask=None):
+        B, T, _ = x.shape
+        qkv = self.qkv_proj(x)
+        qkv = qkv.view(B, T, 3, self.n_heads, self.d_head)
+        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+
+        q = q.transpose(1, 2)  # (B, H, T, D)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # Causal mask (lower-triangular)
+        causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=x.dtype))
+        attn = attn * causal_mask.unsqueeze(0).unsqueeze(0)
+        attn = attn.masked_fill(causal_mask.unsqueeze(0).unsqueeze(0) == 0, float("-inf"))
+
+        # Padding mask
+        if mask is not None:
+            # mask: (B, T) where True = keep, False = pad
+            attn_mask = mask.unsqueeze(1).unsqueeze(2).float()  # (B, 1, 1, T)
+            attn = attn * attn_mask
+            attn = attn.masked_fill(attn_mask == 0, float("-inf"))
+
+        attn = F.softmax(attn, dim=-1)
+        # Replace NaN from all-inf rows (padded positions) with 0
+        attn = attn.masked_fill(attn != attn, 0.0)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).reshape(B, T, -1)
+        return self.o_proj(out)
+
+
+class TraceableFFN(nn.Module):
+    """Positionwise feed-forward network with causal Conv1d.
+
+    NeMo uses ConvolutionLayer which applies causal left-padding manually:
+    F.pad(signal, (kernel_size - 1, 0)) before Conv1d(padding=0).
+    """
+
+    def __init__(self, d_model, d_ffn, kernel_size=1, is_causal=True):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.is_causal = is_causal
+        # Conv1d with no built-in padding (bias=False matching NeMo) — we handle padding manually
+        self.conv1 = nn.Conv1d(d_model, d_ffn, kernel_size, padding=0, bias=False)
+        self.conv2 = nn.Conv1d(d_ffn, d_model, kernel_size, padding=0, bias=False)
+        self.act = nn.GELU(approximate="tanh")
+
+    def forward(self, x, mask=None):
+        # x: (B, T, C) -> transpose -> (B, C, T) for conv1d
+        x = x.transpose(1, 2)
+
+        if mask is not None:
+            x = x * mask.unsqueeze(1).float()
+
+        if self.is_causal and self.kernel_size > 1:
+            x = F.pad(x, (self.kernel_size - 1, 0))
+        x = self.act(self.conv1(x))
+
+        if mask is not None:
+            x = x * mask.unsqueeze(1).float()
+
+        if self.is_causal and self.kernel_size > 1:
+            x = F.pad(x, (self.kernel_size - 1, 0))
+        x = self.conv2(x)
+
+        if mask is not None:
+            x = x * mask.unsqueeze(1).float()
+
+        return x.transpose(1, 2)
+
+
+class TraceableEncoderLayer(nn.Module):
+    """Single encoder transformer layer (causal)."""
+
+    def __init__(self, d_model, d_ffn, n_heads, kernel_size=1, is_causal=True):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(d_model, bias=False)
+        self.self_attn = TraceableSelfAttention(d_model, n_heads)
+        self.norm2 = nn.LayerNorm(d_model, bias=False)
+        self.ffn = TraceableFFN(d_model, d_ffn, kernel_size, is_causal)
+
+    def forward(self, x, mask=None):
+        # Pre-norm causal self-attention
+        residual = x
+        x = self.norm1(x)
+        x = self.self_attn(x, mask)
+        x = residual + x
+
+        # Pre-norm FFN with causal conv
+        residual = x
+        x = self.norm2(x)
+        x = self.ffn(x, mask)
+        x = residual + x
+
+        return x
+
+
+class TraceableTextEncoder(nn.Module):
+    """Complete text encoder for CoreML conversion.
+
+    Architecture (verified from NVIDIA Magpie TTS 357M):
+    - Text embedding lookup (vocab=2362, d_model=768)
+    - Learnable positional encoding (max_pos=2048)
+    - 6 causal transformer layers (causal self-attention + causal Conv1d FFN)
+    - Output LayerNorm (bias=False)
+    """
+
+    def __init__(self, n_layers, d_model, d_ffn, n_heads, kernel_size=1,
+                 vocab_size=None, use_pos_emb=False, max_pos=2048,
+                 apply_norm_out=False, is_causal=True):
+        super().__init__()
+        self.d_model = d_model
+        self.use_pos_emb = use_pos_emb
+
+        if vocab_size is not None:
+            self.text_embedding = nn.Embedding(vocab_size, d_model)
+        else:
+            self.text_embedding = None
+
+        if use_pos_emb:
+            self.position_embeddings = nn.Embedding(max_pos, d_model)
+
+        self.layers = nn.ModuleList([
+            TraceableEncoderLayer(d_model, d_ffn, n_heads, kernel_size, is_causal)
+            for _ in range(n_layers)
+        ])
+
+        self.norm_out = nn.LayerNorm(d_model, bias=False) if apply_norm_out else nn.Identity()
+
+    def forward(self, text_tokens, text_mask):
+        """
+        Args:
+            text_tokens: (B, T) int32 - token IDs
+            text_mask: (B, T) bool - True where valid, False for padding
+        Returns:
+            encoder_output: (B, T, d_model) float32
+        """
+        if self.text_embedding is not None:
+            x = self.text_embedding(text_tokens)
+        else:
+            x = text_tokens  # already embedded
+
+        if self.use_pos_emb:
+            T = x.shape[1]
+            positions = torch.arange(T, device=x.device)
+            x = x + self.position_embeddings(positions)
+
+        # Mask padding
+        x = x * text_mask.unsqueeze(-1).float()
+
+        for layer in self.layers:
+            x = layer(x, mask=text_mask)
+
+        x = self.norm_out(x)
+        return x
+
+    @classmethod
+    def from_magpie(cls, model, include_text_embedding=True):
+        """Create from a loaded MagpieTTSModel.
+
+        Args:
+            model: MagpieTTSModel instance
+            include_text_embedding: Whether to include text embedding in the encoder
+        """
+        cfg = model.cfg
+        enc_cfg = dict(cfg.encoder)
+
+        wrapper = cls(
+            n_layers=enc_cfg["n_layers"],
+            d_model=enc_cfg["d_model"],
+            d_ffn=enc_cfg["d_ffn"],
+            n_heads=enc_cfg["sa_n_heads"],
+            kernel_size=enc_cfg.get("kernel_size", 1),
+            vocab_size=model.text_embedding.weight.shape[0] if include_text_embedding and hasattr(model, "text_embedding") else None,
+            use_pos_emb=enc_cfg.get("use_learnable_pos_emb", False),
+            max_pos=enc_cfg.get("max_length_causal_mask", 2048),
+            apply_norm_out=enc_cfg.get("apply_norm_out", False),
+            is_causal=enc_cfg.get("is_causal", True),
+        )
+
+        # Copy weights
+        if include_text_embedding and hasattr(model, "text_embedding"):
+            wrapper.text_embedding.weight.data.copy_(model.text_embedding.weight.data)
+
+        if wrapper.use_pos_emb and model.encoder.position_embeddings is not None:
+            wrapper.position_embeddings.weight.data.copy_(model.encoder.position_embeddings.weight.data)
+
+        # Copy transformer layers
+        # NeMo TransformerLayer attr names (verified):
+        #   self_attention (SelfAttention) with qkv_net, o_net
+        #   norm_self, norm_pos_ff (LayerNorm, bias=False)
+        #   pos_ff (PositionwiseConvFF) with proj.conv, o_net.conv (Conv1d)
+        for i, (src_layer, dst_layer) in enumerate(zip(model.encoder.layers, wrapper.layers)):
+            # Self-attention
+            dst_layer.self_attn.qkv_proj.weight.data.copy_(src_layer.self_attention.qkv_net.weight.data)
+            dst_layer.self_attn.o_proj.weight.data.copy_(src_layer.self_attention.o_net.weight.data)
+
+            # Norms (bias=False in NeMo)
+            dst_layer.norm1.weight.data.copy_(src_layer.norm_self.weight.data)
+            dst_layer.norm2.weight.data.copy_(src_layer.norm_pos_ff.weight.data)
+
+            # FFN (Conv1d weights via PositionwiseConvFF, bias=False)
+            dst_layer.ffn.conv1.weight.data.copy_(src_layer.pos_ff.proj.conv.weight.data)
+            dst_layer.ffn.conv2.weight.data.copy_(src_layer.pos_ff.o_net.conv.weight.data)
+
+        # Output norm
+        if enc_cfg.get("apply_norm_out", False):
+            wrapper.norm_out.weight.data.copy_(model.encoder.norm_out.weight.data)
+
+        return wrapper
diff --git a/models/tts/magpie/pyproject.toml b/models/tts/magpie/pyproject.toml
new file mode 100644
index 0000000..9b438c8
--- /dev/null
+++ b/models/tts/magpie/pyproject.toml
@@ -0,0 +1,41 @@
+[project]
+name = "magpie-tts-coreml"
+requires-python = ">= 3.10,<3.13"
+description = "NVIDIA Magpie TTS 357M CoreML conversion"
+version = "0.1.0"
+dependencies = [
+    "numpy>=1.24",
+    "torch>=2.5.0",
+    "coremltools>=8.0",
+    "soundfile>=0.12.0",
+    "scipy>=1.5.0",
+    "huggingface_hub>=0.10",
+]
+
+[project.optional-dependencies]
+nemo = [
+    "nemo_toolkit[tts]",
+    "hydra-core>=1.3",
+    "omegaconf>=2.3",
+    "lightning>=2.0",
+]
+
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cpu" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.uv]
+python-preference = "only-managed"
diff --git a/models/tts/magpie/uv.lock b/models/tts/magpie/uv.lock
new file mode 100644
index 0000000..0d4f276
--- /dev/null
+++ b/models/tts/magpie/uv.lock
@@ -0,0 +1,4393 @@
+version = 1
+revision = 1
+requires-python = ">=3.10, <3.13"
+resolution-markers = [
+    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'emscripten'",
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+    "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
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+    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
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