# Argmax Open-Source SDK [![Tests](https://github.com/argmaxinc/argmax-oss-swift/actions/workflows/release-tests.yml/badge.svg)](https://github.com/argmaxinc/argmax-oss-swift/actions/workflows/release-tests.yml) [![Supported Swift Version](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fargmaxinc%2Fargmax-oss-swift%2Fbadge%3Ftype%3Dswift-versions&labelColor=353a41&color=32d058)](https://swiftpackageindex.com/argmaxinc/argmax-oss-swift) [![Supported Platforms](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fargmaxinc%2Fargmax-oss-swift%2Fbadge%3Ftype%3Dplatforms&labelColor=353a41&color=32d058)](https://swiftpackageindex.com/argmaxinc/argmax-oss-swift) [![License](https://img.shields.io/github/license/argmaxinc/argmax-oss-swift?logo=github&logoColor=969da4&label=License&labelColor=353a41&color=32d058)](LICENSE.md)
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[Argmax](https://argmaxinc.com/blog) Open-Source SDK Swift is a collection of turn-key on-device inference frameworks: - **WhisperKit** for speech-to-text with OpenAI Whisper - **SpeakerKit** for speaker diarization with Pyannote - **TTSKit** for text-to-speech with Qwen-TTS > [!IMPORTANT] > [Argmax Pro SDK](https://www.argmaxinc.com/blog/argmax-sdk-2) supports additional models and advanced features such as: > - Real-time transcription with speakers > - Frontier accuracy for your use case with custom vocabulary > - Argmax Local Server for non-native apps > - Android support with Argmax Pro SDK Kotlin > > Further resources: > - [Open-source vs Pro SDK](https://app.argmaxinc.com/docs/wiki/open-source-vs-pro-sdk) > - [Try Pro SDK on TestFlight](https://testflight.apple.com/join/Q1cywTJw) > - [Model Gallery](https://app.argmaxinc.com/docs/models) ## Table of Contents - [Installation](#installation) - [Swift Package Manager](#swift-package-manager) - [Prerequisites](#prerequisites) - [Xcode Steps](#xcode-steps) - [Package.swift](#packageswift) - [Homebrew](#homebrew) - [WhisperKit](#whisperkit) - [Quick Example](#quick-example) - [Memory-Efficient Loading for Large Files](#memory-efficient-loading-for-large-files) - [Model Selection](#model-selection) - [Generating Models](#generating-models) - [Swift CLI](#swift-cli) - [Local Server](#local-server) - [Building the Server](#building-the-server) - [Starting the Server](#starting-the-server) - [API Endpoints](#api-endpoints) - [Supported Parameters](#supported-parameters) - [Client Examples](#client-examples) - [Generating the API Specification](#generating-the-api-specification) - [Client Generation](#client-generation) - [API Limitations](#api-limitations) - [Fully Supported Features](#fully-supported-features) - [TTSKit](#ttskit) - [Quick Example](#quick-example-1) - [Model Selection](#model-selection-1) - [Custom Voices](#custom-voices) - [Real-Time Streaming Playback](#real-time-streaming-playback) - [Generation Options](#generation-options) - [Style Instructions (1.7B only)](#style-instructions-17b-only) - [Saving Audio](#saving-audio) - [Progress Callbacks](#progress-callbacks) - [Swift CLI](#swift-cli-1) - [Demo App](#demo-app) - [SpeakerKit](#speakerkit) - [Quick Example](#quick-example-2) - [Diarization Options](#diarization-options) - [Combining with Transcription](#combining-with-transcription) - [RTTM Output](#rttm-output) - [Swift CLI](#swift-cli-2) - [Contributing \& Roadmap](#contributing--roadmap) - [License](#license) - [Citation](#citation) ## Installation ### Swift Package Manager WhisperKit, TTSKit, and SpeakerKit are separate library products in the same Swift package. Add the package once and pick the products you need. You can also use the `ArgmaxOSS` umbrella product to import everything at once. ### Prerequisites - macOS 14.0 or later. - Xcode 16.0 or later. ### Xcode Steps 1. Open your Swift project in Xcode. 2. Navigate to `File` > `Add Package Dependencies...`. 3. Enter the package repository URL: `https://github.com/argmaxinc/argmax-oss-swift`. 4. Choose the version range or specific version. 5. When prompted to choose library products, select **ArgmaxOSS** (all kits), or individual kits: **WhisperKit**, **TTSKit**, **SpeakerKit**. ### Package.swift Add the package dependency: ```swift dependencies: [ .package(url: "https://github.com/argmaxinc/argmax-oss-swift.git", from: "0.9.0"), ], ``` Then add the products you need as target dependencies: ```swift .target( name: "YourApp", dependencies: [ // Import everything at once: .product(name: "ArgmaxOSS", package: "argmax-oss-swift"), // Or pick individual kits: // .product(name: "WhisperKit", package: "argmax-oss-swift"), // speech-to-text // .product(name: "TTSKit", package: "argmax-oss-swift"), // text-to-speech // .product(name: "SpeakerKit", package: "argmax-oss-swift"), // speaker diarization ] ), ``` ### Homebrew You can install the command line app using [Homebrew](https://brew.sh) by running the following command: ```bash brew install whisperkit-cli ``` ## WhisperKit To get started with WhisperKit, you need to initialize it in your project. ### Quick Example This example demonstrates how to transcribe a local audio file: ```swift import WhisperKit // Initialize WhisperKit with default settings Task { let pipe = try? await WhisperKit() let results = try? await pipe?.transcribe(audioPath: "path/to/your/audio.{wav,mp3,m4a,flac}") let transcription = results?.map(\.text).joined(separator: " ") print(transcription ?? "") } ``` ### Memory-Efficient Loading for Large Files By default WhisperKit loads the whole audio file into memory before transcribing. For long recordings, `.incremental` streams it from disk in bounded-memory chunks instead: ```swift import WhisperKit let pipe = try await WhisperKit() let options = AudioInputOptions(audioLoadingMode: .incremental) let results = try await pipe.transcribe( audioPath: "path/to/large-audio.wav", audioInputOptions: options ) print(results.map(\.text).joined(separator: " ")) ``` It splits the audio at silence (VAD) boundaries, so the result matches a full-file transcription run with `chunkingStrategy: .vad` — only peak memory differs. Tune the chunking with `.incremental(chunkDuration:chunkBufferSize:)`, or pick channels with `AudioInputOptions(channelMode:)`. From the CLI, add `--incremental-loading` (optionally `--incremental-chunk-duration` / `--incremental-chunk-buffer-size`): ```bash swift run argmax-cli transcribe --model large-v3-v20240930_626MB --audio-path "path/to/large-audio.wav" --incremental-loading ``` ### Model Selection > [!NOTE] > Argmax recommends `large-v3-v20240930_626MB` for maximum multilingual accuracy and `tiny` for the fastest debugging workflow. | Whisper Version | WhisperKit Variant | Description | |----------------------------------|------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------| | Large v3 Turbo (compressed) | [large-v3-v20240930_626MB](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3-v20240930_626MB) | Recommended across iOS and macOS for maximum accuracy | | Large v3 Turbo | [large-v3-v20240930_turbo](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-large-v3-v20240930_turbo) | Recommended on macOS for maximum speed and accuracy | | Base (multilingual) | [base](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-base) | | | Base (English-only) | [base.en](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-base.en) | | | Small (Multilingual) | [small](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-small) | | | Small (English-only) | [small.en](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-small.en) | | | Tiny (Multilingual) | [tiny](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-tiny) | | | Tiny (English-only) | [tiny.en](https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/openai_whisper-tiny.en) | Smallest size, lowest accuracy. Only recommended for development & debugging. | WhisperKit automatically downloads the recommended model for the device if not specified. You can also select a specific model by passing in the model name: ```swift let pipe = try? await WhisperKit(WhisperKitConfig(model: "large-v3-v20240930_626MB")) ``` This method also supports glob search, so you can use wildcards to select a model: ```swift let pipe = try? await WhisperKit(WhisperKitConfig(model: "large-v3-v20240930_626MB")) ``` Note that the model search must return a single model from the source repo, otherwise an error will be thrown. For a list of available models, see our [HuggingFace repo](https://huggingface.co/argmaxinc/whisperkit-coreml). ### Generating Models WhisperKit also comes with the supporting repo [`whisperkittools`](https://github.com/argmaxinc/whisperkittools) which lets you create and deploy your own fine tuned versions of Whisper in CoreML format to HuggingFace. Once generated, they can be loaded by simply changing the repo name to the one used to upload the model: ```swift let config = WhisperKitConfig(model: "large-v3-v20240930_626MB", modelRepo: "username/your-model-repo") let pipe = try? await WhisperKit(config) ``` ### Swift CLI The Swift CLI allows for quick testing and debugging outside of an Xcode project. To install it, run the following: ```bash git clone https://github.com/argmaxinc/argmax-oss-swift.git cd argmax-oss-swift ``` Then, setup the environment and download your desired model. ```bash make setup make download-model MODEL=large-v3-v20240930_626MB ``` **Note**: 1. This will download only the model specified by `MODEL` (see what's available in our [HuggingFace repo](https://huggingface.co/argmaxinc/whisperkit-coreml), where we use the prefix `openai_whisper-{MODEL}`) 2. Before running `download-model`, make sure [git-lfs](https://git-lfs.com) is installed If you would like download all available models to your local folder, use this command instead: ```bash make download-models ``` You can then run them via the CLI with: ```bash swift run argmax-cli transcribe --model-path "Models/whisperkit-coreml/openai_whisper-large-v3-v20240930_626MB" --audio-path "path/to/your/audio.{wav,mp3,m4a,flac}" ``` Which should print a transcription of the audio file. If you would like to stream the audio directly from a microphone, use: ```bash swift run argmax-cli transcribe --model-path "Models/whisperkit-coreml/openai_whisper-large-v3-v20240930_626MB" --stream ``` ### Local Server The Argmax CLI includes a local server that implements the OpenAI Audio API, allowing you to use existing OpenAI SDK clients or generate new ones. The server supports transcription and translation with **output streaming** capabilities (real-time transcription results as they're generated). > [!NOTE] > [Argmax Pro Local Server](https://www.argmaxinc.com/blog/argmax-local-server) provides a real-time streaming transcription with a WebSocket local server that is API-compatible with cloud-based providers such as Deepgram. #### Building the Server ```bash # Build with server support make build-local-server # Or manually with the build flag BUILD_ALL=1 swift build --product argmax-cli ``` #### Starting the Server ```bash # Start server with default settings BUILD_ALL=1 swift run argmax-cli serve # Custom host and port BUILD_ALL=1 swift run argmax-cli serve --host 0.0.0.0 --port 8080 # With specific model and verbose logging BUILD_ALL=1 swift run argmax-cli serve --model tiny --verbose # See all configurable parameters BUILD_ALL=1 swift run argmax-cli serve --help ``` #### API Endpoints - **POST** `/v1/audio/transcriptions` - Transcribe audio to text - **POST** `/v1/audio/translations` - Translate audio to English #### Supported Parameters | Parameter | Description | Default | |-----------|-------------|---------| | `file` | Audio file (wav, mp3, m4a, flac) | Required | | `model` | Model identifier | Server default | | `language` | Source language code | Auto-detect | | `prompt` | Text to guide transcription | None | | `response_format` | Output format (json, verbose_json) | verbose_json | | `temperature` | Sampling temperature (0.0-1.0) | 0.0 | | `timestamp_granularities[]` | Timing detail (word, segment) | segment | | `stream` | Enable streaming | false | #### Client Examples **Python Client (OpenAI SDK)** ```bash cd Examples/ServeCLIClient/Python uv sync python whisperkit_client.py transcribe --file audio.wav --language en python whisperkit_client.py translate --file audio.wav ``` Quick Python example: ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:50060/v1") result = client.audio.transcriptions.create( file=open("audio.wav", "rb"), model="tiny" # Model parameter is required ) print(result.text) ``` **Swift Client (Generated from OpenAPI Spec, see ServeCLIClient/Swift/updateClient.sh)** ```bash cd Examples/ServeCLIClient/Swift swift run whisperkit-client transcribe audio.wav --language en swift run whisperkit-client translate audio.wav ``` **CurlClient (Shell Scripts)** ```bash cd Examples/ServeCLIClient/Curl chmod +x *.sh ./transcribe.sh audio.wav --language en ./translate.sh audio.wav --language es ./test.sh # Run comprehensive test suite ``` #### Generating the API Specification The server's OpenAPI specification and code are generated from the official OpenAI API: ```bash # Generate latest spec and server code make generate-server ``` #### Client Generation You can generate clients for any language using the OpenAPI specification, for example: ```bash # Generate Python client swift run swift-openapi-generator generate scripts/specs/localserver_openapi.yaml \ --output-directory python-client \ --mode client \ --mode types # Generate TypeScript client npx @openapitools/openapi-generator-cli generate \ -i scripts/specs/localserver_openapi.yaml \ -g typescript-fetch \ -o typescript-client ``` #### API Limitations Compared to the official OpenAI API, the local server has these limitations: - **Response formats**: Only `json` and `verbose_json` supported (no plain text, SRT, VTT formats) - **Model selection**: Client must launch server with desired model via `--model` flag #### Fully Supported Features The local server fully supports these OpenAI API features: - **Include parameters**: `logprobs` parameter for detailed token-level log probabilities - **Streaming responses**: Server-Sent Events (SSE) for real-time transcription - **Timestamp granularities**: Both `word` and `segment` level timing - **Language detection**: Automatic language detection or manual specification - **Temperature control**: Sampling temperature for transcription randomness - **Prompt text**: Text guidance for transcription style and context ## TTSKit TTSKit is an on-device text-to-speech framework built on Core ML. It runs [Qwen3-TTS](https://github.com/QwenLM/Qwen3-TTS) models entirely on Apple silicon with real-time streaming playback, no server required. - macOS 15.0 or later. - iOS 18.0 or later. ### Quick Example This example demonstrates how to generate speech from text: ```swift import TTSKit Task { let tts = try await TTSKit() let result = try await tts.generate(text: "Hello from TTSKit!") print("Generated \(result.audioDuration)s of audio at \(result.sampleRate)Hz") } ``` `TTSKit()` automatically downloads the default 0.6B model on first run. The tokenizer and CoreML models are loaded lazily on the first `generate()` call. ### Model Selection TTSKit ships two model sizes. You can select the model by passing a variant to `TTSKitConfig`: ```swift // Fast, runs on all platforms (~1 GB download) let tts = try await TTSKit(TTSKitConfig(model: .qwen3TTS_0_6b)) // Higher quality, macOS only (~2.2 GB download, supports style instructions) let tts = try await TTSKit(TTSKitConfig(model: .qwen3TTS_1_7b)) ``` Models are hosted on [HuggingFace](https://huggingface.co/argmaxinc/ttskit-coreml) and cached locally after the first download. #### Custom Voices You can choose from 9 built-in voices and 10 languages: ```swift let result = try await tts.generate( text: "こんにちは世界", speaker: .onoAnna, language: .japanese ) ``` **Voices:** `.ryan`, `.aiden`, `.onoAnna`, `.sohee`, `.eric`, `.dylan`, `.serena`, `.vivian`, `.uncleFu` **Languages:** `.english`, `.chinese`, `.japanese`, `.korean`, `.german`, `.french`, `.russian`, `.portuguese`, `.spanish`, `.italian` #### Real-Time Streaming Playback `play` streams audio to the device speakers frame-by-frame as it is generated: ```swift try await tts.play(text: "This starts playing before generation finishes.") ``` You can control how much audio is buffered before playback begins. The default `.auto` strategy measures the first generation step and pre-buffers just enough to avoid underruns: ```swift try await tts.play( text: "Long passage...", playbackStrategy: .auto ) ``` Other strategies include `.stream` (immediate, no buffer), `.buffered(seconds:)` (fixed pre-buffer), and `.generateFirst` (generate all audio first, then play). ### Speech Decoder Mode TTSKit's default speech decoder bundles two functions, selectable via `TTSKitConfig.speechDecoderMode`: | Mode | RVQ frames / call | Audio / call | Use case | |------|-------------------|--------------|----------| | `.latencyOptimized` (default) | 1 | ~80 ms | Lowest time-to-first-audio for streaming. | | `.throughputOptimized` | 4 | ~320 ms | Amortizes decoder overhead for higher throughput, at the cost of a ~4× larger first-buffer latency. | ```swift // Default: latency-optimized (lowest time-to-first-audio) let tts = try await TTSKit() // Opt into throughput-optimized generation let config = TTSKitConfig(speechDecoderMode: .throughputOptimized) let throughputTTS = try await TTSKit(config) ``` The mode is read once when models are loaded; set it before constructing `TTSKit` (or reload the model to switch at runtime). ### Generation Options You can customize sampling, chunking, and concurrency via `GenerationOptions`: ```swift // Defaults recommended by Qwen var options = GenerationOptions() options.temperature = 0.9 options.topK = 50 options.repetitionPenalty = 1.05 options.maxNewTokens = 245 // Long text is automatically split at sentence boundaries options.chunkingStrategy = .sentence options.concurrentWorkerCount = nil // nil = all chunks run concurrently with a good default for the device let result = try await tts.generate(text: longArticle, options: options) ``` #### Style Instructions (1.7B only) The 1.7B model accepts a natural-language style instruction that controls prosody: ```swift var options = GenerationOptions() options.instruction = "Speak slowly and warmly, like a storyteller." let result = try await tts.generate( text: "Once upon a time...", speaker: .ryan, options: options ) ``` ### Saving Audio Generated audio can be saved to WAV or M4A: ```swift let result = try await tts.generate(text: "Save me!") let outputDir = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0] // Save as .wav or .m4a (AAC) try await AudioOutput.saveAudio(result.audio, toFolder: outputDir, filename: "output", format: .m4a) ``` ### Progress Callbacks You can receive per-step audio during generation. Return `false` from the callback to cancel early: ```swift let result = try await tts.generate(text: "Hello!") { progress in print("Audio chunk: \(progress.audio.count) samples") if let stepTime = progress.stepTime { print("First step took \(stepTime)s") } return true // return false to cancel } ``` ### Swift CLI The TTS command is available through the `argmax-cli` tool. You can generate speech and optionally play it back in real time: ```bash swift run argmax-cli tts --text "Hello from the command line" --play swift run argmax-cli tts --text "Save to file" --output-path output.wav swift run argmax-cli tts --text "日本語テスト" --speaker ono-anna --language japanese swift run argmax-cli tts --text-file article.txt --model 1.7b --instruction "Read cheerfully" swift run argmax-cli tts --help ``` ### Demo App The [TTSKitExample](Examples/TTS/TTSKitExample/) example app showcases real-time streaming, model management, waveform visualization, and generation history on macOS and iOS. See the [TTSKitExample README](Examples/TTS/TTSKitExample/README.md) for build instructions. ## SpeakerKit SpeakerKit is an on-device speaker diarization framework built on Core ML. It runs [Pyannote v4 (community-1)](https://huggingface.co/argmaxinc/speakerkit-coreml) on Apple silicon to label speakers in audio. Read the [blog post](https://www.argmaxinc.com/blog/speakerkit) for architecture details and benchmarks. - macOS 13.0 or later. - iOS 16.0 or later. ### Quick Example This example demonstrates how to diarize an audio file: ```swift import SpeakerKit Task { let speakerKit = try await SpeakerKit() let audioArray = try AudioProcessor.loadAudioAsFloatArray(fromPath: "audio.wav") let result = try await speakerKit.diarize(audioArray: audioArray) print("Detected \(result.speakerCount) speakers") for segment in result.segments { print(segment) } } ``` `SpeakerKit()` uses `PyannoteConfig()` defaults, automatically downloading models from [HuggingFace](https://huggingface.co/argmaxinc/speakerkit-coreml) on first run. The segmenter and embedder CoreML models are loaded lazily (unless `load` is set on config) on the first `diarize()` call. ### Diarization Options You can control speaker detection via `PyannoteDiarizationOptions`: ```swift let audioArray = try AudioProcessor.loadAudioAsFloatArray(fromPath: "audio.wav") let options = PyannoteDiarizationOptions( numberOfSpeakers: 2, // nil = automatic detection clusterDistanceThreshold: 0.6, // clustering threshold useExclusiveReconciliation: false // exclusive speaker assignment per frame ) let result = try await speakerKit.diarize(audioArray: audioArray, options: options) ``` For local models, skip the download step: ```swift let config = PyannoteConfig(modelFolder: "/path/to/models") let speakerKit = try await SpeakerKit(config) ``` ### Combining with Transcription SpeakerKit can merge diarization results with WhisperKit transcriptions to produce speaker-attributed segments: ```swift import WhisperKit import SpeakerKit let whisperKit = try await WhisperKit() let speakerKit = try await SpeakerKit() let audioArray = try AudioProcessor.loadAudioAsFloatArray(fromPath: "audio.wav") let transcription = try await whisperKit.transcribe(audioArray: audioArray) let diarization = try await speakerKit.diarize(audioArray: audioArray) let speakerSegments = diarization.addSpeakerInfo(to: transcription) for group in speakerSegments { for segment in group { print("\(segment.speaker): \(segment.text)") } } ``` Two strategies are available for matching speakers to transcription: - `.subsegment` (default) -- splits segments at word gaps, then assigns speakers - `.segment` -- assigns a speaker to each transcription segment as a whole ### RTTM Output Generate RTTM output: ```swift let speakerKit = try await SpeakerKit() let audioArray = try AudioProcessor.loadAudioAsFloatArray(fromPath: "meeting.wav") let diarization = try await speakerKit.diarize(audioArray: audioArray) let rttmLines = SpeakerKit.generateRTTM(from: diarization, fileName: "meeting") for line in rttmLines { print(line) } ``` ### Swift CLI The diarization commands are available through the `argmax-cli` tool: ```bash # Standalone diarization swift run argmax-cli diarize --audio-path audio.wav --verbose # Save RTTM output swift run argmax-cli diarize --audio-path audio.wav --rttm-path output.rttm # Specify number of speakers swift run argmax-cli diarize --audio-path audio.wav --num-speakers 3 # Transcription with diarization swift run argmax-cli transcribe --audio-path audio.wav --diarization # See all options swift run argmax-cli diarize --help ``` ## Contributing & Roadmap Our goal is to make this SDK better and better over time and we'd love your help! Just search the code for "TODO" for a variety of features that are yet to be built. Please refer to our [contribution guidelines](CONTRIBUTING.md) for submitting issues, pull requests, and coding standards, where we also have a public roadmap of features we are looking forward to building in the future. **External dependencies:** `Sources/ArgmaxCore/External/` contains a copy of [swift-transformers](https://github.com/huggingface/swift-transformers) (Hub and Tokenizers modules, v1.1.6) with Jinja-dependent code removed. When updating to a newer version, copy the fresh sources over that directory and re-apply the patches marked with `// Argmax-modification:` (`grep -r "Argmax-modification:" Sources/ArgmaxCore/External/`). The matching upstream tests are vendored under `Tests/ArgmaxCoreTests/External/` using the same convention. ## License Argmax OSS is released under the MIT License. See [LICENSE](LICENSE) for more details. This project incorporates third-party software under their own license terms. See [NOTICES](NOTICES) for attributions. ## Citation If you use this SDK for something cool or just find it useful, please drop us a note at [info@argmaxinc.com](mailto:info@argmaxinc.com)! If you use WhisperKit, SpeakerKit or TTSKit for academic work, please cite the project using the following BibTeX: ```bibtex @misc{whisperkit-argmax, title = {Argmax OSS: On-device Speech AI with WhisperKit, SpeakerKit and TTSKit}, author = {Argmax, Inc.}, year = {2024}, URL = {https://github.com/argmaxinc/argmax-oss-swift} } ```