MLX Audio Swift
github.com/blaizzy/mlx-audio-swift- Category
- Developer Tools
- Rank
- No. 754Tools index
Previous survey · No. 745 ·
- Pricing
- Open Source
- Type
- TOOL
- Builder
- blaizzy
- GitHub
- 776 stars
- Latest release
- v0.1.3
- Date
About
Modular Swift SDK for audio processing with MLX on Apple Silicon — TTS, STT, and audio analysis for iOS and macOS apps.
What it does
MLX Audio Swift turns downloaded model checkpoints into native, typed audio workflows. Applications can synthesize or transcribe speech, identify speakers and languages, detect voice activity, transform speech, run codecs, and phonemize text. Its components share audio utilities while model loaders resolve compatible repositories or local directories. Streaming generation can emit token, audio, and informational events as work progresses.
Why it's ranked here
This is a compelling option for Apple-focused audio ML because one package covers an unusually broad model roster without forcing every capability into one import. Separate libraries cover core processing, codecs, generation, recognition, detection, transformation, language identification, phonemization, and interface components. Executable tools and a shared test target make the project more than a collection of model ports.
What's good
The modular package surface is genuinely useful: teams can select focused libraries while retaining a combined compatibility product. Loaders support Hugging Face repositories and, for several model families, local model directories. The core includes concrete signal-processing machinery such as window functions, Fourier transforms, mel filterbanks, padding modes, and spectrogram normalization. Streaming output supports incremental progress handling.
Tradeoffs
The supported deployment range is narrow: macOS 14 or newer and iOS 17 or newer, with MLX on Apple Silicon. The manifest requires Swift tools 6.2, while the README badge says Swift 5.9 or newer, creating a compatibility-documentation mismatch. Installation follows the main branch rather than a tagged version. Many workflows also depend on separately obtained model checkpoints.
How to use it well
Use it when a Swift application needs several local audio ML capabilities and benefits from sharing model loading, tensors, codecs, and signal processing. Start with one focused library and one documented checkpoint, then add adjacent modules only when the product needs them. It suits Apple-native prototypes and applications. It does not provide a cross-platform or server-oriented audio stack.
Technical notes+
Package.swift declares nine library products, a combined MLXAudio compatibility product, five executable products, and MLX, MLX language-model, Hugging Face, and Transformers dependencies. Sources/MLXAudioCore/DSP.swift implements windowing, STFT, mel filters, decibel conversion, and mel spectrogram computation with MLX arrays. Sources/MLXAudioTTS/TTSModel.swift and Sources/MLXAudioSTS/STSModel.swift resolve model types from repository metadata, local configuration, or name inference. Sources/MLXAudioG2P/G2P.swift performs greedy ByT5 decoding with a key-value cache, while Sources/MLXAudioG2P/Weights.swift maps external weight names into the Swift module structure and loads safetensors. Package.swift also declares MLXAudioTests at Tests with copied media resources.
Observed
- License
- MIT
- Primary language
- Swift
- Packaging
- Swift Package Manager package with modular and combined library products
- Interfaces
- Swift libraries plus command-line executables for TTS, STT, codecs, speech transformation, and language identification
- Platform support
- macOS 14 or newer and iOS 17 or newer on Apple Silicon
- Model sources
- Supports Hugging Face repositories and local directories for documented model loaders
- Testing structure
- A shared test target uses the Tests directory and includes media resources
Read from README.md, Package.swift, Sources/MLXAudioG2P/G2P.swift, Sources/MLXAudioCore/DSP.swift, Sources/MLXAudioG2P/Model.swift, Sources/MLXAudioG2P/Config.swift, Sources/MLXAudioG2P/Decoder.swift, Sources/MLXAudioG2P/Encoder.swift, Sources/MLXAudioG2P/Weights.swift, Sources/MLXAudioG2P/G2PTypes.swift, Sources/MLXAudioSTS/STSModel.swift, Sources/MLXAudioTTS/TTSModel.swift, Sources/MLXAudioG2P/Attention.swift, Sources/MLXAudioG2P/Tokenizer.swift, Sources/MLXAudioLID/LIDOutput.swift.
What it can do
Convert text to speech
Text string → Audio file or audio stream
Convert speech to text
Audio file or audio stream → Transcribed text
Analyze audio content
Audio file or audio stream → Audio analysis data or metadata
Process audio using MLX models
Audio data and MLX model parameters → Processed audio or analysis results
Integrate audio processing into iOS apps
iOS app project and audio processing requirements → iOS app with audio processing capabilities
Integrate audio processing into macOS apps
macOS app project and audio processing requirements → macOS app with audio processing capabilities
Tags
Tech Stack
Comments (0)
No comments yet
Editorially curated, with community endorsements as a secondary signal. Corrections welcome.