LFM3-Audio-2B for Core ML

LiquidAI/LFM3-Audio-2B converted to Core ML for Apple devices (macOS 15 / iOS 18 and newer): offline speech recognition (long-form included), LiveASR (streaming English transcription in 0.96 s chunks) and text-to-speech, all from one 2B audio language model. Run it with FluidAudio (LFM3AudioManager).

This is a modified version of the original model: the weights were converted to Core ML and the language model was quantized to int8 (block size 32). See Quantization and parity.

A Core AI build of the language model (macOS 27 / iOS 27) is in FluidInference/lfm3-audio-2b-coreai.

Files

ASR and LiveASR need about 2.5 GB of files; TTS adds the depth decoder, codec and tables (~0.4 GB). Context: 4096 positions.

Use (Swift)

import FluidAudio

let manager = try await LFM3AudioManager.downloadAndLoad()  // .coreML; includeSpeech: true

// Offline ASR (16 kHz mono); language: "English", "French", ... or nil to infer
let result = try await manager.transcribe(samples)
print(result.text)

// LiveASR (English)
try await manager.startLiveTranscription()
let partial = try await manager.appendLiveAudio(microphoneChunk)
let final = try await manager.finishLiveTranscription()

// TTS (24 kHz), streamed as it is decoded
let speech = try await manager.synthesize(
    "Hello from LFM3 Audio.", voice: .female, language: "American English",
    onAudio: { chunk in /* play */ })

CLI: fluidaudiocli lfm3-audio transcribe|live|tts|benchmark.

Speed and accuracy

Apple M5 Pro (24 GB), macOS 27.0, FluidAudio release build, Core ML backend. Wall time includes model warm-up (the first calls after load), audio decoding excluded.

Per speech frame (80 ms): LM 9.4 ms + depth decoder 4.9 ms on the GPU; the codec decodes on the ANE in parallel. Peak memory footprint for ASR: 1.9 GB.

Quantization and parity

• LM: int8 per-block-32 weights. Teacher-forced TTS hidden states match fp32 at cosine 0.9999; offline ASR transcripts are identical to the original model on the 100 utterances above. (An int4 build failed the speech-frame check.)
• Audio encoder, depth decoder and codec: fp16. Depth decoder codes are 99.6% identical to the original on 319 golden frames (sampling noise injected); codec waveform SNR 43-49 dB vs fp32.
• Host log-mel matches the original feature extractor to 8.3e-5.

License

LFM Open License v1.0 (Liquid AI). Commercial use by organizations with annual revenue of USD 10 million or more requires a separate license from Liquid AI; see the license for the full terms.

Changelog