Unity-Sherpa-ONNX

Unity integration plugin for sherpa-onnx — an open-source speech toolkit powered by ONNX Runtime.

🗺️ Feature Roadmap

Feature	Description	Status
Text-to-Speech (TTS)	Offline speech synthesis — VITS, Matcha, Kokoro, Kitten, ZipVoice, Pocket (voice cloning)	✅ Done
Speech Recognition (ASR)	Offline and streaming speech-to-text — Zipformer, Paraformer, Whisper, SenseVoice, Moonshine	✅ Done
Voice Activity Detection (VAD)	Speech/silence segmentation for efficient ASR — Silero VAD, TEN-VAD	✅ Done
Keyword Spotting (KWS)	Lightweight always-on keyword detection from microphone	📋 Planned
Speaker ID & Diarization	Speaker identification by voice, who-spoke-when segmentation	📋 Planned
Audio Tools	Audio tagging, speech enhancement, punctuation restoration, language identification	📋 Planned

🖥️ Supported Platforms

Platform	Architectures
🪟 Windows	x64, x86, arm64
🍎 macOS	x64, arm64
🐧 Linux	x64, arm64
🤖 Android	arm64-v8a, armeabi-v7a, x86, x86_64
📱 iOS	arm64, x86_64-simulator

💡 Why This Plugin

Integrating sherpa-onnx into a Unity project normally requires manual native library setup, platform-specific workarounds, and custom C# bindings. This plugin handles all of that out of the box.

⚡ Easy Setup

• 🔌 One-click library install — open Project Settings, pick a version, click Install. Native libraries for Windows, macOS, Linux, Android, and iOS are downloaded and configured automatically.
• 📥 One-click model import — paste a model URL, the importer downloads, extracts, auto-detects the model type, and creates a ready-to-use profile. No manual config editing.
• 🔄 Update All — change the version number and update every installed platform at once.

🔧 Platform Solutions

The plugin solves real-world platform issues that are not addressed by sherpa-onnx itself:

Problem	Platform	What the plugin does
🔇 Unity Microphone returns silence	Android	Detects silence automatically, falls back to native AudioRecord via JNI with audio source cascade (VOICE_RECOGNITION → VOICE_COMMUNICATION → MIC). Disables NoiseSuppressor, AGC, and AEC that can mute the signal. Fires SilenceDetected event with full diagnostics.
📦 StreamingAssets locked inside APK	Android	Extracts model files to persistentDataPath on first launch with version tracking and progress reporting. Skips re-extraction on subsequent launches.
🌍 Non-US locale breaks native code	Android	Wraps native calls with a locale guard that temporarily sets LC_NUMERIC to "C", preventing comma-as-decimal crashes in sherpa-onnx's float parsing.
🍏 No dynamic library loading	iOS	Builds a patched sherpa-onnx.dll with DllImport("__Internal") and downloads it automatically during install.
✂️ Xcframework architecture bloat	iOS	Filters xcframeworks to only the target architecture (device or simulator) during install.
🎙️ Microphone not actually recording	Unity (all)	Plays a silent AudioSource on the mic clip to force the device to start recording — a known Unity workaround.
⏳ Microphone readiness delay	Unity (all)	Polls Microphone.GetPosition() with a configurable timeout before starting capture.
🎵 Sample rate mismatch	All	Built-in resampler converts any input rate to the model's expected rate (typically 16 kHz).
🔐 Microphone permission	Android / iOS	Async permission request with UniTask — returns false gracefully if denied.

⚙️ All microphone settings (silence threshold, fallback timing, diagnostics) are configurable via a JSON file in StreamingAssets — no code changes needed.

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📦 Installation

Option 1 - Installer

• ⬇️ Download Installer
• 📂 Import installer into Unity project
  • Double-click the file — Unity will open it
  • OR: Unity Editor → Assets → Import Package → Custom Package, then choose the file
• The installer adds OpenUPM scoped registry and resolves the package automatically

Option 2 - OpenUPM (Scoped Registry)

• 📂 Open Packages/manifest.json in your project
• ✏️ Add the scoped registry and dependency:

  {
    "scopedRegistries": [
      {
        "name": "OpenUPM",
        "url": "https://package.openupm.com",
        "scopes": [
          "com.ponyudev.sherpa-onnx",
          "com.cysharp.unitask"
        ]
      }
    ],
    "dependencies": {
      "com.ponyudev.sherpa-onnx": "0.1.0"
    }
  }
  

• ✅ Unity will resolve and download the package automatically

Option 3 - OpenUPM CLI

• 📦 Install openupm-cli
• ▶️ Run the command in your project folder:

  openupm add com.ponyudev.sherpa-onnx
  

• ✅ Dependencies are resolved automatically

Option 4 - Git URL

• ⚠️ Install UniTask first — open Window → Package Manager, click + → Add package from git URL... and paste:

  https://github.com/Cysharp/UniTask.git?path=src/UniTask/Assets/Plugins/UniTask
  

• 🔗 Then add Sherpa-ONNX the same way:

  https://github.com/Ponyu-dev/Unity-Sherpa-ONNX.git
  

---

🔌 Installing Native Libraries

1. Open Edit → Project Settings → Sherpa ONNX
2. Set the desired sherpa-onnx version (e.g. 1.12.25)
3. Click Install for each platform you need
4. Use Update All when you change the version to update all installed libraries at once

📥 Libraries are downloaded from:

• Desktop (Windows, macOS, Linux): NuGet
• Android / iOS native: sherpa-onnx GitHub releases
• iOS managed DLL: this repository's GitHub releases (see below)

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🗣️ Text-to-Speech (TTS)

Offline speech synthesis with pooling and caching. Supports 6 model architectures.

Setting Up TTS Models

1. Open Project Settings > Sherpa-ONNX > TTS
2. Click Import from URL and paste a model archive link
3. The importer downloads, extracts, and auto-configures the profile
4. Select the Active profile to use at runtime

Key features:

• 🧠 6 model architectures — Vits (Piper), Matcha, Kokoro, Kitten, ZipVoice, Pocket
• 🔍 Auto-detection — model type and paths are configured automatically from the archive
• ⚡ Int8 quantization — one-click switch between normal and int8 models
• 🚀 Flexible deployment — Local (StreamingAssets), Remote (runtime download), or LocalZip (compressed at build time)
• 🎛️ Matcha vocoder selector — choose and download vocoders independently
• ♻️ Cache pooling — configurable pools for audio buffers, AudioClips, and AudioSources

📖 Documentation

• Models Setup Guide — Editor UI, importing, profiles, deployment options
• Runtime Usage Guide — MonoBehaviour, VContainer, Zenject examples, API reference

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👂 Speech Recognition (ASR)

Offline file recognition and real-time streaming with microphone. Supports 15 offline and 5 online model architectures.

Setting Up ASR Models

1. Open Project Settings > Sherpa-ONNX > ASR
2. Select the Offline or Online tab
3. Click Import from URL and paste a model archive link
4. The importer downloads, extracts, and auto-configures the profile
5. Select the Active profile to use at runtime

Key features:

• 🧠 15 offline + 5 online architectures — Zipformer, Paraformer, Whisper, SenseVoice, Moonshine, and more
• 🔍 Auto-detection — model type and paths are configured automatically from the archive
• ⚡ Int8 quantization — one-click switch between normal and int8 models
• 🎙️ Streaming recognition — real-time microphone capture with partial and final results
• 🏊 Engine pool — multiple concurrent recognizer instances for offline ASR
• ⏹️ Endpoint detection — configurable silence rules for automatic utterance segmentation

📖 Documentation

• Models Setup Guide — Editor UI, importing, profiles, offline/online tabs
• Runtime Usage Guide — MonoBehaviour, VContainer, Zenject examples, API reference

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🔊 Voice Activity Detection (VAD)

Speech/silence segmentation for efficient ASR pipelines. Supports Silero VAD and TEN-VAD models.

Setting Up VAD Models

1. Open Project Settings > Sherpa-ONNX > VAD
2. Click Import from URL and paste a model archive link
3. The importer downloads, extracts, and auto-configures the profile
4. Select the Active profile to use at runtime

Key features:

• 🧠 2 model architectures — Silero VAD, TEN-VAD
• 🔍 Auto-detection — model type and paths are configured automatically from the archive
• 🎛️ Configurable parameters — threshold, min silence/speech duration, window size
• 🔗 VAD + ASR pipeline — segment audio by voice activity, then recognize each segment

📖 Documentation

• Models Setup Guide — Editor UI, importing, profiles, configuration
• Runtime Usage Guide — MonoBehaviour, VContainer, Zenject examples, API reference

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🍏 Why the iOS Managed DLL Is Hosted Here

On desktop and Android, Unity loads native code via dynamic libraries (.dll, .so, .dylib). The managed C# binding (sherpa-onnx.dll) uses DllImport("sherpa-onnx-c-api") to find them at runtime.

iOS does not support dynamic loading. All native code must be statically linked into the app binary. This means the managed DLL must use DllImport("__Internal") instead of "sherpa-onnx-c-api".

The upstream sherpa-onnx NuGet package ships with the standard "sherpa-onnx-c-api" binding, which does not work on iOS. To solve this, the Tools~/ scripts in this repository:

1. Take the official C# sources from sherpa-onnx/scripts/dotnet/
2. Patch Dll.cs to replace "sherpa-onnx-c-api" with "__Internal"
3. Build a custom sherpa-onnx.dll targeting netstandard2.0
4. Publish it as a GitHub release with tag sherpa-v{version}

The plugin's iOS install pipeline downloads this patched DLL automatically.

🏷️ Scripting Define Symbol

After installing any library, the plugin automatically adds SHERPA_ONNX to Scripting Define Symbols for all build targets. This allows you to guard runtime code that depends on sherpa-onnx:

#if SHERPA_ONNX
    var recognizer = new OnlineRecognizer(config);
#endif

The define is removed automatically when all libraries are uninstalled.

📋 Requirements

• Unity 2022.3 or later
• com.unity.sharp-zip-lib 1.4.1+ (added automatically as a dependency)

📄 License

Apache 2.0