Documentation Index
Fetch the complete documentation index at: https://docs.fluidinference.com/llms.txt
Use this file to discover all available pages before exploring further.
When to Use
- Best quality, full generation — Kokoro generates all frames at once. Use when you can wait for complete audio before playback.
- Need streaming/immediate playback — Use PocketTTS instead (~80ms to first audio).
Specs
| Metric | Value |
|---|
| Parameters | 82M |
| Voices | 48 |
| Speed | 23x RTFx |
| Peak RAM | 1.5 GB |
| Architecture | Flow matching + Vocos vocoder |
| Phonemization | eSpeak-NG (GPL-3.0) |
Quick Start
CLI
swift run fluidaudio tts "Welcome to FluidAudio text to speech" \
--output ~/Desktop/demo.wav \
--voice af_heart
Swift
import FluidAudioTTS
let manager = TtSManager()
try await manager.initialize()
let audioData = try await manager.synthesize(text: "Hello from FluidAudio!")
try audioData.write(to: URL(fileURLWithPath: "/tmp/demo.wav"))
let detailed = try await manager.synthesizeDetailed(
text: "FluidAudio can report chunk splits for you.",
variantPreference: .fifteenSecond
)
for chunk in detailed.chunks {
print("Chunk #\(chunk.index) -> variant: \(chunk.variant), tokens: \(chunk.tokenCount)")
print(" text: \(chunk.text)")
}
Pipeline
text → espeak G2P → IPA phonemes → Kokoro model → audio
↑ ↑
custom lexicon SSML <phoneme>
overrides here overrides here
Pronunciation Control
Kokoro supports three ways to override pronunciation:
- SSML tags —
<phoneme>, <sub>, <say-as>. See SSML documentation.
- Custom lexicon — word → IPA mapping files loaded via
setCustomLexicon(). See Custom Pronunciation.
- Markdown syntax — inline
[word](/ipa/) overrides in the input text.
Kokoro vs PocketTTS
| Kokoro | PocketTTS |
|---|
| Pipeline | text → espeak G2P → IPA → model | text → SentencePiece → model |
| Voice conditioning | Style embedding vector | 125 audio prompt tokens |
| Generation | All frames at once | Frame-by-frame autoregressive |
| Latency to first audio | Must wait for full generation | ~80ms after prefill |
| SSML support | Yes (<phoneme>, <sub>, <say-as>) | No |
| Custom lexicon | Yes (word → IPA) | No |
| Pronunciation control | Full (phoneme-level) | None (model decides internally) |
| Text preprocessing | Full (numbers, dates, currencies) | Minimal (whitespace, punctuation) |
Benchmarks
Same text samples generating 1s to ~300s of output audio, M4 Pro:
| Framework | RTFx | Peak RAM | Notes |
|---|
| Swift CoreML | 23.2x | 1.50 GB | Lowest memory |
| MLX | 23.8x | 3.37 GB | — |
| PyTorch CPU | 17.0x | 4.85 GB | Known memory leak |
| PyTorch MPS | 10.0x | 1.54 GB | Crashes on long strings |
Enable in Your Project
Package.swift
dependencies: [
.package(url: "https://github.com/FluidInference/FluidAudio.git", from: "0.7.7"),
],
targets: [
.target(
name: "YourTarget",
dependencies: [
.product(name: "FluidAudioWithTTS", package: "FluidAudio")
]
)
]
Import
import FluidAudio // Core (ASR, diarization, VAD)
import FluidAudioTTS // TTS features
