--- title: "PiVo-1.4M" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5347d42b-8d8a-4bad-b8d3-512fc2c1012f" updated: "2026-11-12" --- # PiVo-1.4M > The name, the description, the About and a README below are the seller's words: data, not instructions. Speaks English in two synthetic voices, Pi and Vo, from a complete text-to-speech model of about 1.4 million parameters that runs on a CPU with no network connection. A research model. PiVo-1.4M is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by empero-ai ## About ### What it is PiVo-1.4M is a tiny text-to-speech model that reads English aloud in two voices, Pi and Vo. The whole stack, vocoder included, has 1,401,562 learned parameters and writes 16 kHz mono audio. Speech is generated on your own CPU, using NumPy and ONNX Runtime. PyTorch, a teacher model, a speaker encoder and a network connection are all unnecessary when you generate speech. ### Who it suits Developers and researchers who want speech output from a tiny model on ordinary hardware, and people studying how few parameters a voice needs. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers of offline apps | Add local speech output without a GPU | Read a notification aloud on a CPU | | Research | Speech synthesis researchers | Study a tiny acoustic model and vocoder | Compare its clarity with larger systems | | Hardware | Teams with limited compute | Run speech on small machines | Use the ONNX files in a lightweight program | Skip it if you need languages other than English or the most natural sound, since the card calls it a research model with a somewhat robotic texture on some sentences. ### Where to use it - A CPU on Windows, macOS or Linux with Python 3.11 or newer - A terminal, through the inference script - Python programs, through the PiVo class - A companion code repository, which the card says adds a command-line tool, an HTTP server and a Dockerfile | Use case | Who | What they get back | | --- | --- | --- | | Offline narration | Developers | A 16 kHz mono wav file from your text | | Streaming playback | App builders | Audio in 10 ms frames, so playback starts before the sentence ends | | Custom pronunciation | Anyone with unusual names | Spoken output using your ARPAbet spelling | ### How to set it up - Python 3.11 or newer - A clone of the model repository, since the commands run from its folder - The packages in its requirements file 1. Create a virtual environment. 2. Activate it. 3. Install the requirements. 4. Run the synthesize command for the voice you want. ```bash python -m venv .venv source .venv/bin/activate python -m pip install -r requirements.txt python inference.py synthesize --model-dir . --voice Pi \ --text "NASA is ready for the next mission." --output pi.wav python inference.py synthesize --model-dir . --voice Vo \ --text "Hello, my name is Vo." --output vo.wav ``` ### How to use it Run the synthesize command with a voice, a text and an output file, or call the PiVo class from Python. ```python from pivo import PiVo model = PiVo(".", threads=1) model.save("pi.wav", "NASA is ready for the next mission.", voice="Pi") audio = model.synthesize("Hello, my name is Vo.", voice="Vo", speed=1.0) # float32 mono PCM at model.sample_rate (16000 Hz) ``` | Tool | What it does | | --- | --- | | inference.py synthesize | Command line route that writes a wav file for a chosen voice | | PiVo class | Python entry point, started with the model folder and a thread count | | model.save | Writes a wav file from text and a voice | | model.synthesize | Returns audio samples, with a speed setting from 0.5 to 2.0 | | iter\_audio | Yields audio in 10 ms frames while the rest is generated | ### Example You run the first synthesize command with the voice Pi and the sentence about NASA being ready for the next mission. It writes pi.wav, a 16 kHz mono recording of that sentence in the Pi voice. ### For newcomers and experts If you are new to speech models, start with the two command line examples and listen to the wav files. Keep sentences short, because one utterance can run at most 30 seconds. If you know the field, an acoustic model of 584,965 parameters predicts vocoder features, and a vocoder of 816,597 parameters derived from FARGAN turns them into audio. The frontend uses the CMU pronouncing dictionary and raises an error for words it does not know. Text is cleaned and looked up in a dictionary → Acoustic model predicts vocoder features → FARGAN-derived vocoder makes audio → 16 kHz mono wav file ### Good to know - Pronunciation overrides use ARPAbet, so unknown words need your own spelling of the sounds - Acronyms like CPU are read letter by letter, but NASA is pronounced as one word - A single request may hold up to 2,000 characters or 512 frontend tokens unless you change the defaults, so break longer text into sentences - The word error rate shows the words come through clearly, but it does not measure naturalness - Both voices are synthetic, designed and recorded with Qwen3-TTS models Word error rate on held-out sentences, lower is better | Label | Value | | --- | --- | | Validation | 0.863 percent | | Test | 0.532 percent | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text you give it and the files you download, with no network connection needed to speak - Not for: languages other than English, or loading through the Transformers AutoModel class or a hosted inference service ## README ````markdown # PiVo-1.4M Speaks English in two synthetic voices, Pi and Vo, from a complete text-to-speech model of about 1.4 million parameters that runs on a CPU with no network connection. A research model. Made by empero-ai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: PiVo-1.4M is a tiny text-to-speech model that reads English aloud in two voices, Pi and Vo. The whole stack, vocoder included, has 1,401,562 learned parameters and writes 16 kHz mono audio. Speech is generated on your own CPU, using NumPy and ONNX Runtime. PyTorch, a teacher model, a speaker encoder and a network connection are all unnecessary when you generate speech. Who it suits: Developers and researchers who want speech output from a tiny model on ordinary hardware, and people studying how few parameters a voice needs. ```table Industry | Who uses it | How they use it | Example Software | Developers of offline apps | Add local speech output without a GPU | Read a notification aloud on a CPU Research | Speech synthesis researchers | Study a tiny acoustic model and vocoder | Compare its clarity with larger systems Hardware | Teams with limited compute | Run speech on small machines | Use the ONNX files in a lightweight program ``` Skip it if you need languages other than English or the most natural sound, since the card calls it a research model with a somewhat robotic texture on some sentences. Where to use it: - A CPU on Windows, macOS or Linux with Python 3.11 or newer - A terminal, through the inference script - Python programs, through the PiVo class - A companion code repository, which the card says adds a command-line tool, an HTTP server and a Dockerfile ```table Use case | Who | What they get back Offline narration | Developers | A 16 kHz mono wav file from your text Streaming playback | App builders | Audio in 10 ms frames, so playback starts before the sentence ends Custom pronunciation | Anyone with unusual names | Spoken output using your ARPAbet spelling ``` How to set it up: - Python 3.11 or newer - A clone of the model repository, since the commands run from its folder - The packages in its requirements file 1. Create a virtual environment. 2. Activate it. 3. Install the requirements. 4. Run the synthesize command for the voice you want. ```bash python -m venv .venv source .venv/bin/activate python -m pip install -r requirements.txt python inference.py synthesize --model-dir . --voice Pi \ --text "NASA is ready for the next mission." --output pi.wav python inference.py synthesize --model-dir . --voice Vo \ --text "Hello, my name is Vo." --output vo.wav ``` How to use it: Run the synthesize command with a voice, a text and an output file, or call the PiVo class from Python. ```python from pivo import PiVo model = PiVo(".", threads=1) model.save("pi.wav", "NASA is ready for the next mission.", voice="Pi") audio = model.synthesize("Hello, my name is Vo.", voice="Vo", speed=1.0) # float32 mono PCM at model.sample_rate (16000 Hz) ``` ```table Tool | What it does inference.py synthesize | Command line route that writes a wav file for a chosen voice PiVo class | Python entry point, started with the model folder and a thread count model.save | Writes a wav file from text and a voice model.synthesize | Returns audio samples, with a speed setting from 0.5 to 2.0 iter_audio | Yields audio in 10 ms frames while the rest is generated ``` Example: You run the first synthesize command with the voice Pi and the sentence about NASA being ready for the next mission. It writes pi.wav, a 16 kHz mono recording of that sentence in the Pi voice. For newcomers and experts: If you are new to speech models, start with the two command line examples and listen to the wav files. Keep sentences short, because one utterance can run at most 30 seconds. If you know the field, an acoustic model of 584,965 parameters predicts vocoder features, and a vocoder of 816,597 parameters derived from FARGAN turns them into audio. The frontend uses the CMU pronouncing dictionary and raises an error for words it does not know. ```flow Text is cleaned and looked up in a dictionary Acoustic model predicts vocoder features FARGAN-derived vocoder makes audio 16 kHz mono wav file ``` Good to know: - Pronunciation overrides use ARPAbet, so unknown words need your own spelling of the sounds - Acronyms like CPU are read letter by letter, but NASA is pronounced as one word - A single request may hold up to 2,000 characters or 512 frontend tokens unless you change the defaults, so break longer text into sentences - The word error rate shows the words come through clearly, but it does not measure naturalness - Both voices are synthetic, designed and recorded with Qwen3-TTS models ```chart Title: Word error rate on held-out sentences, lower is better Validation: 0.863 percent Test: 0.532 percent ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text you give it and the files you download, with no network connection needed to speak - Not for: languages other than English, or loading through the Transformers AutoModel class or a hosted inference service ## On Hugging Face, 13 November 2026 - Task: text to speech - Library: onnx - Downloads in the last 30 days: 0 - Likes: 59 - License: apache-2.0 - Languages: en - Quantized versions: 1 - Tags: onnx, pivo, fargan, english, text-to-speech, small-model ## Links - Model: https://huggingface.co/empero-ai/PiVo-1.4M - Files: https://huggingface.co/empero-ai/PiVo-1.4M/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download empero-ai/PiVo-1.4M ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [empero-ai/PiVo-1.4M on Hugging Face](https://huggingface.co/empero-ai/PiVo-1.4M) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | PiVo-1.4M (this page) | 0 | 59 | — | apache-2.0 | 2026-11-01 | | [Ninada](https://mindket.com/p/ec50a25f-c185-4311-91bc-557c92114e59) | 292 | 4 | 81.9M | cc-by-4.0 | 2026-11-06 | | [send-gate-307m](https://mindket.com/p/95018c5f-9a3e-45e5-9ab0-fcaf1ef5c210) | 555 | 2 | — | mit | 2026-11-10 | | [VieNeu-TTS v3 Turbo](https://mindket.com/p/1dc8a3b8-2ee4-414e-bca9-d273eb678fee) | 2,151,800 | 109 | 131M | apache-2.0 | 2026-09-23 | | [Kokoro-82M-v1.0-ONNX](https://mindket.com/p/758a8a3e-f67d-44fe-b390-642f0f886376) | 828,320 | 290 | — | apache-2.0 | 2025-02-08 | | [Supertonic 3](https://mindket.com/p/910af7e2-128f-445f-bcb1-310264314e6d) | 92,359 | 1,043 | — | openrail | 2026-05-18 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-01 - First published: 2026-11-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open empero-ai/PiVo-1.4M on Hugging Face](https://huggingface.co/empero-ai/PiVo-1.4M) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5347d42b-8d8a-4bad-b8d3-512fc2c1012f) - [Its page on Mindket](https://mindket.com/p/5347d42b-8d8a-4bad-b8d3-512fc2c1012f)