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Any-TTS is an open-source, high-quality text-to-speech (TTS) toolkit for generating natural-sounding speech from text—featuring easy setup, flexible voices/languages, and developer-friendly APIs for apps, bots, and automation.

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# any-tts

Rust-native text-to-speech and speech synthesis for modern open-weight models.

any-tts is a Rust TTS library built around Candle with one trait-based API for Kokoro, OmniVoice, Qwen3-TTS, VibeVoice, VibeVoice Realtime, and Voxtral. It is aimed at developers who want local speech synthesis, multilingual TTS, reference-audio prompting, preset-voice workflows, or low-latency voice agents without rewriting their application around each model family.

You can point it at local files, hand it explicit paths from your own cache, feed it named in-memory byte assets from an object store, or let it resolve missing assets from Hugging Face while keeping the synthesis call site unchanged.

For Flow-like specifically: every public backend can now load from relative-path byte assets, so `object_store` reads can go straight into `TtsConfig` without writing temp files first.

## Jump to

- [Supported TTS models](#supported-tts-models)
- [Installation](#installation)
- [Quick start](#quick-start)
- [Examples in this repo](#examples-in-this-repo)
- [Model guide](#model-guide)

## Why this repo exists

- One API for Kokoro, OmniVoice, Qwen3-TTS, VibeVoice, VibeVoice Realtime, and Voxtral.
- Native Rust backends across the public model surface.
- Local path loading, in-memory byte bundles, per-file wiring, or Hugging Face fallback.
- GPU first when available through CUDA or Metal, with CPU fallback and optional Accelerate support for Apple CPU builds.
- Request-level control for `language`, `voice`, `reference_audio`, `instruct`, `max_tokens`, `temperature`, and `cfg_scale`.
- WAV output everywhere, with built-in WAV and MP3 input decoding for cleanup and reference-audio workflows.

## Supported TTS models

| Model | Status in any-tts | Default upstream | Best at | Main tradeoff | Model license |
| --- | --- | --- | --- | --- | --- |
| Kokoro-82M | Public, native, lightweight | `hexgrad/Kokoro-82M` | Fast local TTS with small weights | Uses an in-tree pure-Rust phonemizer compatible with Kokoro's current public language set; parity tuning is still ongoing | Apache-2.0 |
| OmniVoice | Public, native | `k2-fsa/OmniVoice` | Huge language coverage and instruct-driven voice design | The current Rust backend does not yet expose upstream zero-shot cloning | Apache-2.0 |
| Qwen3-TTS-12Hz-1.7B | Public, native | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | Strong multilingual control, named speakers, and instruct handling | Heavy weights and extra speech-tokenizer assets | Apache-2.0 |
| VibeVoice-1.5B | Public, native | `microsoft/VibeVoice-1.5B` | Long-form multi-speaker speech diffusion with native Rust inference | Still early and currently optimized for single-request parity work rather than streaming performance | MIT |
| VibeVoice-Realtime-0.5B | Public, native | `microsoft/VibeVoice-Realtime-0.5B` | Low-latency preset-voice TTS with native Rust inference | English-first upstream and depends on cached `voices/*.pt` presets instead of reference audio | MIT |
| Voxtral-4B-TTS-2603 | Public, native | `mistralai/Voxtral-4B-TTS-2603` | Production-style voice agents, preset voices, low-latency oriented stack | Largest backend here and not commercially permissive | CC BY-NC 4.0 |

Important: the Rust crate is dual licensed under `MIT OR Apache-2.0`. The model weights are not. Always check the model-specific license before shipping. Voxtral is the one that changes the deployment story the most because its published checkpoint is `CC BY-NC 4.0`.

All six models above are exposed through the public `ModelType` enum and `load_model()` API.

## Choose a backend

- Use `Kokoro` when you want the smallest local deployment and simple preset-voice TTS.
- Use `OmniVoice` when language coverage and `instruct` matter more than named voices.
- Use `Qwen3Tts` when you want the strongest public request-control surface and named speakers.
- Use `VibeVoice` when you need long-form or reference-audio-conditioned generation.
- Use `VibeVoiceRealtime` when you want cached-prompt preset voices and faster time-to-first-audio.
- Use `Voxtral` when you want preset-voice, voice-agent-style deployment and can accept its model license.

## Installation

For CPU-only builds, a recent stable Rust toolchain is enough. For GPU builds, compile the feature set that matches your machine. Kokoro no longer requires a system `espeak-ng` install: the repo now ships an in-tree pure-Rust phonemizer with an `espeak-rs`-compatible interface for the language set exposed by the current Kokoro backend.

Add the crate from crates.io:

```toml
[dependencies]
any-tts = "0.1"
```

Or opt into a smaller feature set:

```toml
[dependencies]
any-tts = { version = "0.1", default-features = false, features = ["kokoro", "download", "metal"] }
```

### Feature flags

By default the crate enables `qwen3-tts`, `kokoro`, `omnivoice`, `vibevoice`, `voxtral`, and `download`. The `vibevoice` feature exposes both `ModelType::VibeVoice` and `ModelType::VibeVoiceRealtime`.

| Feature | What it does |
| --- | --- |
| `kokoro` | Enables the Kokoro backend. |
| `omnivoice` | Enables the native OmniVoice backend. |
| `qwen3-tts` | Enables the Qwen3-TTS backend. |
| `vibevoice` | Enables the native VibeVoice and VibeVoice Realtime backends. |
| `voxtral` | Enables the native Voxtral backend. |
| `download` | Allows missing model files to be pulled from Hugging Face Hub through the crate's built-in downloader. |
| `cuda` | Builds Candle with CUDA support. |
| `metal` | Builds Candle with Metal support for Apple GPUs. |
| `accelerate` | Enables Apple Accelerate support for CPU-heavy Apple builds. |

### Backend selection

- `DeviceSelection::Auto` tries CUDA first, then Metal, then CPU.
- `DeviceSelection::Cpu`, `DeviceSelection::Cuda(0)`, and `DeviceSelection::Metal(0)` let you force the runtime target.
- `preferred_runtime_choice(ModelType::...)` returns the fastest safe device and dtype for the current machine.
- `TtsConfig::with_preferred_runtime()` applies that runtime choice in one builder call.
- `DType` can be set to `F32`, `F16`, or `BF16`.
- On CPU, models that cannot safely run BF16 fall back to `F32`.
- The native OmniVoice helper prefers `cuda:0 (bf16)`, then `metal:0 (f32)`, then `cpu (f32)`.

## Quick start

```rust,no_run
use any_tts::{load_model, ModelType, SynthesisRequest, TtsConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
 let model = load_model(
 TtsConfig::new(ModelType::Qwen3Tts)
 .with_model_path("./models/Qwen3-TTS")
 )?;

let audio = model.synthesize(
        &SynthesisRequest::new("Hello from Rust TTS.")
            .with_language("English")
            .with_voice("Ryan")
            .with_instruct("Calm, clear, slightly upbeat."),
    )?;

audio.save_wav("hello.wav")?;
    Ok(())
}
```

## Byte-first loading

If your runtime already has model artifacts in memory, use `ModelAssetBundle` or the `with_*_bytes()` builders instead of writing them to disk first.

```rust,no_run
use any_tts::{load_model, ModelAssetBundle, ModelType, SynthesisRequest, TtsConfig};

fn read_object(_key: &str) -> Result<Vec<u8>, Box<dyn std::error::Error>> {
 Ok(Vec::new())
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
 let assets = ModelAssetBundle::new()
 .with_bytes("config.json", read_object("config.json")?)
 .with_bytes("tokenizer.json", read_object("tokenizer.json")?)
 .with_bytes("model.safetensors", read_object("model.safetensors")?)
 .with_bytes(
 "speech_tokenizer/model.safetensors",
 read_object("speech_tokenizer/model.safetensors")?,
 );

let model = load_model(
    TtsConfig::new(ModelType::Qwen3Tts)
      .with_asset_bundle(assets)
  )?;

let audio = model.synthesize(&SynthesisRequest::new("Hello from byte-backed assets."))?;
  let wav_bytes = audio.get_wav();
  let _ = wav_bytes;
  Ok(())
}
```

The relative paths in the asset bundle should match the model layout documented below, for example `config.json`, `audio_tokenizer/model.safetensors`, or `voice_embedding/Aurora.pt`.

## Audio bytes

Generated audio already comes back as `AudioSamples`, so output does not need filesystem paths either.

- `audio.get_wav()` returns a complete WAV file as `Vec<u8>` for every backend.
- `audio.save_wav()` is a convenience helper on top of the same byte encoder.

## Audio cleanup

```rust,no_run
use any_tts::{AudioSamples, DenoiseOptions};
use std::io::Cursor;

fn main() -> Result<(), Box<dyn std::error::Error>> {
 let input = std::fs::read("speech-with-music.mp3")?;
 let cleaned = AudioSamples::denoise_audio_stream(
 Cursor::new(input),
 DenoiseOptions::default(),
 )?;

cleaned.save_wav("speech-cleaned.wav")?;
  Ok(())
}
```

The denoiser auto-detects WAV and MP3 input streams and applies a speech-band
filter plus a short-time spectral gate. It is useful for attenuating steady
background noise and background music, but it is not a full voice-isolation or
source-separation model.

## File resolution flow

any-tts resolves model assets in four tiers, in this order:

1. Explicit files you set on `TtsConfig` with methods like `with_config_file()` or `with_weight_file()`.
2. Auto-discovery from `with_asset_bundle()` or `with_asset_bytes()` using model-relative paths.
3. Auto-discovery from `with_model_path()` using the expected filenames for that backend.
4. Hugging Face fallback through the `download` feature.

That means you can mix strategies. A service with its own artifact cache can hand over a few exact files and let the crate discover or download the rest.

## Model asset layouts

You can inspect the documented manifest programmatically through `ModelType::asset_requirements()`. The expected relative paths are:

| Model | Required asset patterns | Optional asset patterns |
| --- | --- | --- |
| Kokoro | `config.json`, `model.safetensors` or `*.pth` | `voices/*.pt` |
| OmniVoice | `config.json`, `tokenizer.json`, `model.safetensors` or `model-*-of-*.safetensors`, `audio_tokenizer/config.json`, `audio_tokenizer/model.safetensors` or `audio_tokenizer/model-*-of-*.safetensors` | `generation_config.json` |
| Qwen3-TTS | `config.json`, `tokenizer.json`, `model.safetensors` or `model-*-of-*.safetensors`, `speech_tokenizer/model.safetensors` or `speech_tokenizer/model-*-of-*.safetensors` | `speech_tokenizer/config.json`, `generation_config.json` |
| VibeVoice | `config.json`, `tokenizer.json`, `model.safetensors` or `model-*-of-*.safetensors` | `preprocessor_config.json`, `generation_config.json` |
| VibeVoice Realtime | `config.json`, `tokenizer.json`, `model.safetensors` | `preprocessor_config.json`, `voices/*.pt` |
| Voxtral | `params.json`, `tekken.json`, `consolidated.safetensors`, `voice_embedding/*.pt` | none |

Using these exact relative paths makes the byte-based API foolproof because the same names are what `with_model_path()` auto-discovery expects on disk.

## Request controls

`SynthesisRequest` keeps the per-call control surface stable across models.

| Field | Purpose | Notes |
| --- | --- | --- |
| `text` | Input text to synthesize | Required for every backend. |
| `language` | Language tag or model-specific language name | Supports ISO tags in several backends and `auto` where available. |
| `voice` | Named speaker or preset voice | Works for Kokoro, Qwen3 CustomVoice, VibeVoice Realtime, and Voxtral. OmniVoice rejects named voices, and full VibeVoice expects `reference_audio` instead. |
| `instruct` | Natural-language style control | Most useful on OmniVoice and Qwen3. |
| `max_tokens` | Upper bound on generated codec/audio tokens | Helpful for latency testing and smoke tests. |
| `temperature` | Sampling temperature | Supported where the backend uses it. |
| `cfg_scale` | Classifier-free guidance scale | Used by OmniVoice and other backends that expose CFG-like control. |
| `reference_audio` | Reference clip for voice cloning or prompt conditioning | Used by backends such as VibeVoice when conditioning from speech; unsupported backends return an explicit error. |
| `voice_embedding` | Precomputed embedding payload | Currently reusable with backends that accept embeddings directly. |

## Examples in this repo

These are the example entry points that match the current public crate surface:

```bash
cargo run --example generate_kokoro --release
cargo run --example generate_qwen3_tts --release
cargo run --example generate_vibevoice --release --no-default-features --features vibevoice,download,metal
cargo run --example generate_vibevoice_realtime --release --no-default-features --features vibevoice,download,metal
cargo run --example generate_voxtral --release
cargo run --example generate_omnivoice --release --no-default-features --features omnivoice,download,metal
cargo run --example generate_comparison_suite --release --features metal -- --runtime all
cargo run --example benchmark_omnivoice --release --no-default-features --features omnivoice,download,metal -- --warmup 1 --iterations 3
```

Outputs are written under `output/` by the example binaries.

`generate_vibevoice` keeps writing the main raw render to the configured
`VIBEVOICE_OUTPUT` path and also writes `*_base.wav`,
`*_denoised_default.wav`, and `*_denoised_aggressive.wav` under
`output/denoise/` by default. You can override that folder with
`VIBEVOICE_DENOISE_DIR`.

`generate_vibevoice_realtime` targets `ModelType::VibeVoiceRealtime` and expects cached prompt presets under `models/VibeVoice-Realtime-0.5B/voices/` by default. Use `VIBEVOICE_REALTIME_MODEL_PATH`, `VIBEVOICE_REALTIME_VOICES_DIR`, `VIBEVOICE_REALTIME_VOICE`, `VIBEVOICE_REALTIME_DEVICE`, and `VIBEVOICE_REALTIME_OUTPUT` to override the defaults.

`generate_comparison_suite` writes a shared English and German comparison set under `output/model_comparison/cpu/` and `output/model_comparison/metal/`, plus `report.json` files with per-model load time, per-sample synthesis time, audio duration, and realtime factor. It loads one model at a time so the full suite can run sequentially on tighter memory budgets.

## Model guide

### Kokoro-82M

**What it is**

Kokoro is the compact option in this repo: an 82M-parameter StyleTTS2 plus ISTFTNet stack with Apache-licensed weights. In practice, it is the backend you reach for when you want a fast local model, simple deployment, and a much smaller download than the larger multilingual checkpoints.

**What works in any-tts today**

- Native Rust inference.
- Default output at 24 kHz.
- Named preset voices discovered from the `voices/` directory.
- Language tags exposed by the current backend: `en`, `ja`, `zh`, `ko`, `fr`, `de`, `it`, `pt`, `es`, `hi`.
- Optional voice-cloning support only when a checkpoint includes style-encoder weights.

**Pros**

- Small enough to be the practical local-first choice.
- Apache-2.0 model license makes deployment straightforward.
- Good fit for desktop apps, tools, and low-latency local generation.
- Simple model layout relative to the larger codebook-based stacks.

**Cons**

- Uses a pure-Rust phonemizer for English input, so deployment is simpler than the previous espeak-based setup.
- The common open release is mostly about preset voice packs, not raw zero-shot cloning.
- Less expressive control than the bigger instruct-heavy model families.

**License**

- Upstream model weights: Apache-2.0.
- Crate code using the model: `MIT OR Apache-2.0`.

### OmniVoice

**What it is**

OmniVoice is the ambition play in this repo. Upstream, it is a diffusion language model TTS stack aimed at omnilingual zero-shot speech generation with voice design and massive language coverage.

**What works in any-tts today**

- Native Candle backend.
- `language`, `instruct`, `cfg_scale`, and `max_tokens` request controls.
- Automatic runtime preference selection for CPU, CUDA, or Metal.
- Repo-exposed language set: `auto`, `en`, `zh`, `ja`, `ko`, `de`, `fr`, `es`, `pt`, `ru`, `it`.

**What does not work yet in the Rust backend**

- Named voices.
- Reference-audio voice cloning.
- Reusable voice embeddings.

The code returns explicit errors for those cases instead of silently falling back to Python.

**Pros**

- Strong upstream story for language coverage.
- Good fit for instruction-driven voice design.
- Benchmark helper in this repo already makes backend comparisons easy.
- Apache-2.0 model license.

**Cons**

- The current Rust implementation exposes less than the upstream model card promises.
- If your main requirement is zero-shot cloning from reference audio, this backend is not there yet in this crate.
- Heavier than Kokoro and less turnkey than the small local-first path.

**License**

- Upstream model weights: Apache-2.0.
- Crate code using the model: `MIT OR Apache-2.0`.

### Qwen3-TTS

**What it is**

Qwen3-TTS is the control-heavy multilingual option. It uses a discrete multi-codebook language model plus a speech-tokenizer decoder and is designed for named speakers, instruction-following, and multiple TTS operating modes.

**What works in any-tts today**

- Native Rust backend.
- Default path points to `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`.
- Named speaker generation for CustomVoice checkpoints.
- VoiceDesign checkpoints also work when selected through `with_hf_model_id()` or local files.
- 24 kHz output with the extra speech-tokenizer weights resolved alongside the main model.
- Repo-level language support tracks the current checkpoint config and includes `auto`.

**Example: switch from CustomVoice to VoiceDesign**

```rust,no_run
use any_tts::{load_model, ModelType, SynthesisRequest, TtsConfig};

let model = load_model(
    TtsConfig::new(ModelType::Qwen3Tts)
        .with_hf_model_id("Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign")
)?;

let audio = model.synthesize(
    &SynthesisRequest::new("This voice should sound sharp, precise, and quietly confident.")
        .with_language("English")
        .with_instruct("Female presenter voice, low warmth, clear diction, subtle authority."),
)?;
```

**Pros**

- Best overall control surface in the current public crate API.
- Strong multilingual coverage: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian upstream.
- Named speakers are easy to use from a single request builder.
- VoiceDesign gives you a second mode without changing the main crate API.

**Cons**

- Large download and memory footprint compared with Kokoro.
- Requires the separate speech-tokenizer decoder assets in addition to the main weights.
- Upstream Base-model voice cloning exists, but reference-audio cloning is not implemented in this crate yet.

**License**

- Upstream model weights: Apache-2.0.
- Crate code using the model: `MIT OR Apache-2.0`.

### VibeVoice-1.5B

**What it is**

VibeVoice-1.5B is the long-form diffusion backend in this crate. It is the VibeVoice option to choose when you want native Rust inference with reference-audio-conditioned prompting instead of named preset voices.

**What works in any-tts today**

- Native Rust backend.
- `reference_audio`, `language`, `instruct`, `cfg_scale`, `max_tokens`, and `temperature` request controls.
- 24 kHz output with automatic runtime selection across CPU, CUDA, or Metal.
- Public `ModelType::VibeVoice` loading through the shared `vibevoice` feature.

**What does not work yet in the Rust backend**

- Named voice presets.
- Pre-extracted reusable voice embeddings.
- Low-latency streaming generation; the current path is still optimized for correctness and parity work.

**Pros**

- Best fit in this repo for long-form, reference-audio-conditioned synthesis.
- Native Rust inference for a model family that is often driven from Python examples upstream.
- Shares the same trait-based API as the smaller and faster backends.

**Cons**

- Heavier and slower than Kokoro or VibeVoice Realtime.
- Still an early backend compared with the simpler local-first paths.
- Not the right choice if your application needs preset voice names or fast startup latency.

**License**

- Upstream model weights: MIT.
- Crate code using the model: `MIT OR Apache-2.0`.

### VibeVoice-Realtime-0.5B

**What it is**

VibeVoice Realtime is the smaller low-latency VibeVoice variant. In this crate it is exposed as `ModelType::VibeVoiceRealtime` and centers on cached-prompt voice presets rather than reference-audio cloning.

**What works in any-tts today**

- Native Rust backend.
- Cached-prompt voice presets discovered from `voices/*.pt`.
- Public example coverage through `generate_vibevoice_realtime`.
- 24 kHz output with the same Candle runtime selection flow used by the rest of the crate.
- Voice selection through `SynthesisRequest::with_voice()` when matching preset files are present.

**What does not work yet in the Rust backend**

- Reference-audio input.
- Pre-extracted voice embeddings.
- Arbitrary speaker cloning without upstream preset cache files.

**Pros**

- Best fit here for low-latency preset-voice generation.
- Smaller checkpoint than the full VibeVoice-1.5B model.
- Good developer path for apps that reuse approved voices and want predictable startup behavior.

**Cons**

- Depends on `voices/*.pt` preset caches and fails explicitly if they are missing.
- The upstream model card is English-first and research-oriented.
- Less flexible than full VibeVoice when you need reference-audio conditioning or multi-speaker behavior.

**License**

- Upstream model weights: MIT.
- Crate code using the model: `MIT OR Apache-2.0`.

### Voxtral-4B-TTS-2603

**What it is**

Voxtral is the biggest public backend in the repo and the most obviously voice-agent-oriented. It pairs a language model with acoustic generation and preset voice embeddings, and the published checkpoint is tuned for multilingual, low-latency TTS deployment scenarios.

**What works in any-tts today**

- Native Rust backend.
- Preset voice selection from the checkpoint's `voice_embedding/` assets.
- Optional direct `voice_embedding` reuse when you already have a compatible embedding.
- Repo-exposed languages: `en`, `fr`, `es`, `de`, `it`, `pt`, `nl`, `ar`, `hi`.
- Default sample rate is resolved from the model config and outputs at 24 kHz with the published checkpoint.

**Pros**

- Best fit here for production-style voice-agent workloads.
- Upstream model card emphasizes streaming and low time-to-first-audio.
- Comes with preset voices and a clear multilingual story.

**Cons**

- The open checkpoint does not ship reference-audio encoder weights, so raw voice cloning is unavailable.
- This is the heaviest public backend in the crate.
- The published model license is `CC BY-NC 4.0`, so commercial deployment needs extra care or a different model choice.

**License**

- Upstream model weights: CC BY-NC 4.0.
- Crate code using the model: `MIT OR Apache-2.0`.

## Usage patterns that hold across models

### Local directory loading

```rust,no_run
use any_tts::{load_model, ModelType, TtsConfig};

let model = load_model(
    TtsConfig::new(ModelType::Kokoro)
        .with_model_path("./models/Kokoro-82M")
)?;
```

### Explicit file-path loading

```rust,no_run
use any_tts::{load_model, ModelType, TtsConfig};

let model = load_model(
    TtsConfig::new(ModelType::Qwen3Tts)
        .with_config_file("/cache/config.json")
        .with_tokenizer_file("/cache/tokenizer.json")
        .with_weight_file("/cache/model-00001-of-00002.safetensors")
        .with_weight_file("/cache/model-00002-of-00002.safetensors")
        .with_speech_tokenizer_weight_file("/cache/speech-tokenizer.safetensors")
)?;
```

### Device and dtype selection

```rust,no_run
use any_tts::config::DType;
use any_tts::{load_model, DeviceSelection, ModelType, TtsConfig};

let model = load_model(
    TtsConfig::new(ModelType::OmniVoice)
        .with_device(DeviceSelection::Metal(0))
        .with_dtype(DType::F16)
)?;
```

## Repo health files

This repo now includes the standard GitHub community files you would expect for an active project:

- [Contributing](CONTRIBUTING.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Security Policy](SECURITY.md)
- [Support Guide](SUPPORT.md)
- Issue templates under `.github/ISSUE_TEMPLATE/`
- A pull request template at `.github/PULL_REQUEST_TEMPLATE.md`

## Contributing

If you are adding a backend, model variant, or new loading flow, keep the public story honest: unsupported features should fail explicitly, examples should match exported API, and docs should separate experimental repo work from supported top-level surfaces.

The short version is in [CONTRIBUTING.md](CONTRIBUTING.md).

## Security

Model weights, runtime backends, and artifact loading all change the risk profile of TTS systems. Please read [SECURITY.md](SECURITY.md) before disclosing a vulnerability publicly.

## License

The crate metadata declares `MIT OR Apache-2.0` for this repository's Rust code. That does not supersede the terms attached to any model weights you download and run through it.

## Status

This repo now has a clear public shape: six native backends, trait-based loading, byte-first asset support, and example coverage for local, multilingual, long-form, preset-voice, and realtime TTS workflows. The right way to think about it is not "a single-model wrapper" but "a Rust TTS platform layer that lets one application target multiple open model ecosystems without hiding their differences."

any-tts

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