| --- |
| license: apache-2.0 |
| language: |
| - en |
| library_name: onnx |
| pipeline_tag: text-to-speech |
| tags: |
| - text-to-speech |
| - tts |
| - speech-synthesis |
| - neural-tts |
| - offline-tts |
| - on-device |
| - edge-ai |
| - edge |
| - offline |
| - cpu |
| - onnx |
| - onnxruntime |
| - lightweight |
| - small-model |
| - flow-matching |
| - vocos |
| - english |
| - raspberry-pi |
| - flockspeech |
| - accessibility |
| - screen-reader |
| - voice-assistant |
| - narration |
| datasets: |
| - keithito/lj_speech |
| --- |
| |
| # Junco v0.1: tiny offline text-to-speech (TTS) for CPU |
|
|
| **A small, natural English text-to-speech model that runs offline on any CPU. 44 MB, no GPU, no cloud.** |
|
|
| Junco is an 11.4M-parameter flow-matching TTS model from [Flock Speech](https://huggingface.co/flockspeech). It turns |
| English text into natural 24 kHz speech on an ordinary CPU, faster than real time. It has no GPU, network or PyTorch |
| dependency, and the full model is a 44 MB download. |
|
|
| Junco is the clear, neutral **reading voice** of the Flock Speech family: a good fit for voice assistants, screen |
| readers, notifications, narration and other on-device or edge AI speech output. Install it with |
| `pip install flockspeech`. |
|
|
| | Flock Speech voices | Style | Model card | |
| |---|---|---| |
| | **Junco** | clear, neutral reading voice | this page | |
| | **Goldfinch** | conversational voice that laughs and breathes on cue with `[laugh]` tags | [flockspeech/goldfinch](https://huggingface.co/flockspeech/goldfinch) | |
|
|
| | | | |
| |---|---| |
| | **Developed by** | Flock Speech | |
| | **Model type** | Non-autoregressive TTS: transformer encoder, duration predictor, conditional flow-matching decoder, Vocos vocoder | |
| | **Language** | English (US) | |
| | **Voices** | 1 (female, read-aloud style) | |
| | **Output** | 24 kHz mono audio | |
| | **Parameters** | 11.4M acoustic model + 13.5M vocoder | |
| | **Download** | 44 MB (ONNX; int8 decoder, fp16 encoder and vocoder) | |
| | **Runtime** | ONNX Runtime on CPU (Linux, macOS, Windows; x86-64 and ARM64) | |
| | **License** | Apache-2.0 (vocoder weights MIT) | |
| | **Version** | v0.1, November 2026 | |
|
|
| ## Samples |
|
|
| All samples were made with the files in this repository at default settings (8 steps, seed 0), with no editing. |
|
|
| | Use | Text | Audio | |
| |---|---|---| |
| | Greeting | *Hello! I'm Junco, a tiny voice that runs entirely on your own device.* | <audio controls src="https://huggingface.co/flockspeech/junco/resolve/main/samples/greeting.wav"></audio> | |
| | Assistant | *Your next meeting starts in fifteen minutes. Would you like me to read the agenda?* | <audio controls src="https://huggingface.co/flockspeech/junco/resolve/main/samples/assistant.wav"></audio> | |
| | Narration | *The old lighthouse keeper climbed the spiral stairs one last time, and watched the ships come home through the fog.* | <audio controls src="https://huggingface.co/flockspeech/junco/resolve/main/samples/story.wav"></audio> | |
| | News | *Researchers announced on Tuesday that the new battery can be charged in under ten minutes, and lasts twice as long.* | <audio controls src="https://huggingface.co/flockspeech/junco/resolve/main/samples/news.wav"></audio> | |
|
|
| ## Quick start |
|
|
| The [`flockspeech`](https://pypi.org/project/flockspeech/) Python package bundles these weights, so nothing is downloaded |
| at run time. |
|
|
| ```sh |
| pip install flockspeech |
| flockspeech "Hello! I'm Junco." -o hello.wav --play |
| ``` |
|
|
| ```python |
| from flockspeech import Junco |
| |
| tts = Junco() # loads in under a second |
| audio = tts("Hello there.") # float32 numpy array, 24 kHz mono |
| tts.save("Hello there.", "hello.wav") |
| |
| # Low latency: audio is produced sentence by sentence |
| for chunk in tts.stream(long_text): |
| play(chunk) |
| ``` |
|
|
| To use the files in this repository directly: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| from flockspeech import Junco |
| |
| path = snapshot_download("flockspeech/junco", allow_patterns=["onnx/*"]) |
| tts = Junco(model_dir=f"{path}/onnx") |
| ``` |
|
|
| ### Options |
|
|
| | Parameter | Default | Effect | |
| |---|---|---| |
| | `speed` | 1.0 | Speaking rate; 1.2 is 20% faster | |
| | `steps` | 8 | Flow-matching steps; 4 is about 1.5× faster with slightly lower quality | |
| | `seed` | 0 | Fixes the small random variation in delivery, so output is reproducible | |
| | `threads` | up to 4 | CPU threads for ONNX Runtime | |
|
|
| ## Intended use |
|
|
| **Suitable for:** |
| - Voice output on devices and in apps that must work offline or keep text private: assistants, accessibility and |
| screen-reading tools, kiosks, robotics, games and embedded products. |
| - CPU-only servers where cost or latency matters. |
| - Prototyping and research on small TTS models. |
|
|
| **Out of scope:** |
| - Languages other than English, or speaker voices other than the one provided. Junco has no voice cloning. |
| - Expressive, emotional or conversational styles. The voice reads in a neutral, read-aloud style. |
| - Impersonating a real person, or producing audio meant to deceive listeners about who is speaking or whether the |
| speech is synthetic. |
|
|
| ## Limitations |
|
|
| - **No text normalisation.** Write numbers, currency, dates and abbreviations in words: "five dollars", not "$5"; |
| "Doctor", not "Dr.". Raw digits and symbols are read by espeak-ng's rules, which may not be what you want. |
| - **Pronunciation comes from espeak-ng.** Rare names, brands and some homographs, such as *read* and *lead*, can be |
| mispronounced. |
| - **One voice, one accent.** The model learned from a single US English speaker. |
| - **Long-form audio.** Text is synthesised sentence by sentence. Prosody does not carry across sentences. |
| - **Bright high frequencies.** About 14% of Junco's energy lies above 4 kHz, against 2% for real recordings. On some |
| playback systems this may sound slightly bright or hissy. |
|
|
| ## Risks and responsible use |
|
|
| Synthetic speech can be misused for deception. Junco produces a single fixed voice and cannot clone speakers, which |
| limits impersonation risk. We still ask that you: |
| - tell listeners when speech is synthetic, where that matters; |
| - not use the model for fraud, harassment or deceptive impersonation; |
| - review output before you use it in safety-critical or high-stakes settings. |
|
|
| ## Model details |
|
|
| ### Architecture |
|
|
| The model runs in four stages: |
| 1. **Text front end:** espeak-ng converts text to IPA phonemes (en-us, with stress marks). A blank token is inserted |
| between phonemes. |
| 2. **Text encoder (3.2M parameters):** 3 ConvNeXt pre-net blocks, then 6 transformer layers (width 192, RoPE, SwiGLU, |
| RMSNorm). It predicts a per-phoneme mel prior. |
| 3. **Duration predictor (0.3M parameters):** a convolutional regressor of log-durations. It was trained on alignments from Monotonic |
| Alignment Search, so no external aligner is needed. |
| 4. **Decoder (7.9M parameters):** a flow-matching U-Net hybrid. |
| - ConvNeXt blocks run at the full frame rate, and four adaLN transformer blocks run at half the frame rate. |
| - It uses classifier-free guidance (weight 1.0) and 8 Euler steps with a sway-sampling schedule. |
| - It outputs a 100-band log-mel spectrogram (hop 256, 24 kHz). |
|
|
| The vocoder is [Vocos](https://huggingface.co/charactr/vocos-mel-24khz) (charactr/vocos-mel-24khz, MIT). In ONNX it |
| predicts the STFT magnitude and phase, and the inverse STFT runs in numpy. |
|
|
| ### Files |
|
|
| | File | Contents | Size | |
| |---|---|---:| |
| | `onnx/encoder.onnx` | Text encoder and duration predictor (fp16 weights, fp32 compute) | 7.2 MB | |
| | `onnx/decoder-int8.onnx` | One guided flow-matching step (int8 dynamic quantisation) | 9.9 MB | |
| | `onnx/vocoder.onnx` | Vocos backbone and spectral head (fp16 weights, fp32 compute) | 27.1 MB | |
| | `onnx/junco.json` | Mel normalisation statistics and default inference settings | <1 kB | |
| | `samples/` | The audio samples above | | |
|
|
| ### Training |
|
|
| | | | |
| |---|---| |
| | **Data** | [LJ Speech](https://keithito.com/LJ-Speech-Dataset/): 13,100 clips, about 24 hours, one speaker, public domain | |
| | **Objective** | Conditional flow matching (logit-normal time sampling, σ_min 1e-4), MAS prior loss, duration MSE | |
| | **Guidance** | Condition dropped for 10% of training examples, for classifier-free guidance | |
| | **Optimiser** | AdamW (lr 2e-4, β 0.9/0.98, weight decay 0.01), 1,000 warm-up steps, EMA 0.999 | |
| | **Batching** | Dynamic batches of up to 20,000 mel frames | |
| | **Compute** | 44,485 steps in 3 hours on one NVIDIA T4 GPU | |
| | **Released weights** | EMA weights | |
| |
| **Environmental impact.** The final training run used about 3 GPU-hours on one NVIDIA T4 (70 W TDP), or about |
| 0.2 kWh. Architecture experiments before it used more. |
| |
| ## FAQ |
| |
| **Does Junco need a GPU or an internet connection?** |
| No. It runs on an ordinary CPU with ONNX Runtime. Once installed, speech is generated fully offline and your text |
| never leaves the machine. |
| |
| **How big is it?** |
| 44.2 MB in total: a 17.1 MB acoustic model plus the 27.1 MB Vocos vocoder. Junco and Goldfinch share the vocoder, so |
| both voices together take 61.3 MB. |
| |
| **Can it run on a Raspberry Pi or other ARM boards?** |
| It runs on 64-bit ARM Linux wherever `onnxruntime` and `espeakng-loader` publish wheels, including 64-bit Raspberry |
| Pi OS. |
| |
| **Can I stream audio for low latency?** |
| Yes. `tts.stream(text)` yields audio sentence by sentence, so playback can start before the whole text is done. |
| |
| **Can I use it without Python?** |
| The ONNX files run in any ONNX Runtime binding. The text front end (espeak-ng phonemes) and the small loop around the |
| three models live in the [`flockspeech`](https://github.com/flockspeech/flockspeech) package, which you can port. |
| |
| **Does it support voice cloning or other languages?** |
| No. Junco is one English (US) voice. For a casual, conversational voice, use |
| [Goldfinch](https://huggingface.co/flockspeech/goldfinch). |
| |
| **Can I use it commercially?** |
| Yes. The weights are Apache-2.0 and the vocoder is MIT. The phonemiser (espeak-ng, phonemizer) is GPL-3.0 and is |
| installed separately; see the License section below. |
| |
| ## License |
| |
| - **Junco weights and configuration:** [Apache License 2.0](LICENSE). Commercial use, modification and redistribution |
| are allowed. |
| - **Vocos vocoder weights** (`onnx/vocoder.onnx`): MIT, © 2023 Charactr Inc. See `onnx/LICENSE-vocos`. |
| - **Training data:** LJ Speech, public domain. |
| |
| **Run-time dependency.** Text-to-phoneme conversion uses espeak-ng and phonemizer. Both are GPL-3.0 and are **not** |
| included in this repository. The `flockspeech` package installs them as separate dependencies. If you plan to ship |
| Junco inside a closed-source product, review their licence terms, or replace the phonemiser with one under a licence |
| that suits you. See [NOTICE](NOTICE). |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{flockspeech2026junco, |
| title = {Junco: a small flow-matching text-to-speech model for on-device use}, |
| author = {{Flock Speech}}, |
| year = {2026}, |
| howpublished = {\url{https://huggingface.co/flockspeech/junco}}, |
| note = {Version 0.1} |
| } |
| ``` |
| |
| ## Links |
| |
| - Python package: [pypi.org/project/flockspeech](https://pypi.org/project/flockspeech/) |
| - Source code: [github.com/flockspeech/flockspeech](https://github.com/flockspeech/flockspeech) |
| - Conversational voice: [flockspeech/goldfinch](https://huggingface.co/flockspeech/goldfinch) |
| |
| ## Acknowledgements |
| |
| Junco builds on ideas from Matcha-TTS (conditional flow matching for TTS), Glow-TTS (Monotonic Alignment Search), |
| F5-TTS (sway sampling), ZipVoice and SupertonicTTS (efficient decoder design), and Vocos (vocoder). We thank the authors |
| of these works and of LJ Speech. |
| |
