--- title: "Mach Boom" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9c03e356-11af-4e9a-9c5a-9050365d5f46" updated: "2026-11-12" --- # Mach Boom > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English text into speech on a single CPU core, using a 3.5-bit version of Gradium's Phonon 31m model that runs several times faster than real time, for developers who need small, local voice output. Mach Boom 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 SyzygyResearch ## About ### What it is Mach Boom is a text-to-speech model for English. It squeezes Gradium's Phonon 31m model down to 3.5 bits per weight, which leaves a file of under 20 MB. On a single CPU core it produces audio roughly eight to ten times quicker than playback, per the card, and it needs no GPU. ### Who it suits Developers who want spoken output from a small local model will find it fits, especially on machines with only a CPU. Four voices come with it, and more voice files can be added. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add spoken output to a program without a GPU or a hosted service | Reading status messages aloud | | Accessibility | Tool builders | Read written text aloud on ordinary CPU machines | A screen reading helper | | Bookings and travel | Service developers | Speak confirmations that hold codes, times and dates | A spoken booking reference | | Edge computing | Embedded developers | Run speech on small, low-memory machines | Voice output on a modest server | If you work on Windows or macOS, you can skip it, since the card lists Linux on x86-64 as the requirement. ### Where to use it - Linux on x86-64, from the command line - Python scripts, through the MachBoom class - A pipe into an audio player, for streaming playback | Use case | Who | What they get back | | --- | --- | --- | | Make a WAV file | Anyone with a text | A spoken file in the voice you pick | | Stream speech | App developers | Audio pieces as soon as the first chunk is ready | | Tune threads | Performance engineers | Faster speech with more CPU threads | ### How to set it up You need Linux on x86-64 and a C compiler, since the first run builds the speech kernels for your CPU in about 5 seconds. - Linux on x86-64 - Python 3.8 or newer - numpy and tokenizers - gcc or clang 1. Install the Hugging Face hub package. 2. Download the model into a folder named mach-boom and move into it. 3. Install numpy and tokenizers. ```sh pip install -U huggingface_hub hf download SyzygyResearch/Mach-Boom --local-dir mach-boom && cd mach-boom pip install numpy tokenizers ``` ### How to use it Run tts.py with your text, an output file and a voice name. Add threads to speed it up, or stream raw audio to a player. ```sh python tts.py "Your booking reference is AB-4471-Z, and the meeting moved to 3:30 p.m." -o out.wav --voice Zoey python tts.py "..." -o out.wav --threads 4 python tts.py "..." --pcm --first 2 | aplay -q -r 24000 -f S16_LE ``` From inside the mach-boom folder, the same thing works in Python. ```python from mach import MachBoom tts = MachBoom("mach-boom.bin", voices_dir="voices", tokenizer="tokenizer.json") wav = tts.synth("Call 020 7946 0018 before 5:30 p.m. on 14/03.", "Zoey", seed=0) # float32, 24 kHz for piece in tts.synth_stream("Your table is booked for 8 p.m.", "Zoey", first=2): ... ``` | Tool | What it does | | --- | --- | | tts.py | Command-line script that writes a WAV file or streams raw audio | | MachBoom class | Python interface with synth for a whole clip and synth\_stream for pieces | | voices folder | Holds the four shipped voices, Zoey, Juniper, Everly and Harper | ### Example You give tts.py a sentence with a booking code and a meeting time, pick the voice Zoey and name out.wav as the output. The file comes back with that sentence spoken, numbers and times included. ### For newcomers and experts Text to speech reads written words aloud. A good first step is to download the folder, install the two packages and run the one-line command with a sentence of your own. The code builds for several CPU instruction sets, from the newest AVX-512 forms down to plain C. Newer Xeons with AMX also decode audio in bf16 tiles. Fix the seed and the chunk size, and the audio stays identical whatever thread count you pick. In its own tests on one core, peak memory was 93 MB, and first audio came in 61 to 78 ms when streaming. The scores come from the authors, on their own hard test set. Your text and a voice name go in → Kernels compile for your CPU on the first run → The 3.5-bit model generates the speech → A 24 kHz WAV file or audio stream comes out Word error rate on hard English sentences, lower is better | Label | Value | | --- | --- | | Mach Boom | 5.18 percent | | Underdog Woof Juno | 6.50 percent | | Fish Audio Drama 3 | 7.62 percent | | Phonon 31m fp32 | 8.61 percent | | Phonon 31m 4-bit | 10.61 percent | | Kokoro 82M | 14.07 percent | | Pocket TTS | 18.13 percent | | Kitten Nano 0.8 | 18.56 percent | ### Good to know - License: Apache-2.0, shared with Phonon 31m and its voices - Cost to run: none named - Data it reaches: it reads the text you give it and writes audio files, and the source names no web or account access - Four voices ship with it, and voice files from Gradium's Phonon voices set drop in - Not for: other languages or systems, since the card lists English and Linux on x86-64 - Setting PHM\_AMX=0 turns off the AMX path on newer Xeons ## README ````markdown # Mach Boom Turns English text into speech on a single CPU core, using a 3.5-bit version of Gradium's Phonon 31m model that runs several times faster than real time, for developers who need small, local voice output. Made by SyzygyResearch 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: Mach Boom is a text-to-speech model for English. It squeezes Gradium's Phonon 31m model down to 3.5 bits per weight, which leaves a file of under 20 MB. On a single CPU core it produces audio roughly eight to ten times quicker than playback, per the card, and it needs no GPU. Who it suits: Developers who want spoken output from a small local model will find it fits, especially on machines with only a CPU. Four voices come with it, and more voice files can be added. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add spoken output to a program without a GPU or a hosted service | Reading status messages aloud Accessibility | Tool builders | Read written text aloud on ordinary CPU machines | A screen reading helper Bookings and travel | Service developers | Speak confirmations that hold codes, times and dates | A spoken booking reference Edge computing | Embedded developers | Run speech on small, low-memory machines | Voice output on a modest server ``` If you work on Windows or macOS, you can skip it, since the card lists Linux on x86-64 as the requirement. Where to use it: - Linux on x86-64, from the command line - Python scripts, through the MachBoom class - A pipe into an audio player, for streaming playback ```table Use case | Who | What they get back Make a WAV file | Anyone with a text | A spoken file in the voice you pick Stream speech | App developers | Audio pieces as soon as the first chunk is ready Tune threads | Performance engineers | Faster speech with more CPU threads ``` How to set it up: You need Linux on x86-64 and a C compiler, since the first run builds the speech kernels for your CPU in about 5 seconds. - Linux on x86-64 - Python 3.8 or newer - numpy and tokenizers - gcc or clang 1. Install the Hugging Face hub package. 2. Download the model into a folder named mach-boom and move into it. 3. Install numpy and tokenizers. ```sh pip install -U huggingface_hub hf download SyzygyResearch/Mach-Boom --local-dir mach-boom && cd mach-boom pip install numpy tokenizers ``` How to use it: Run tts.py with your text, an output file and a voice name. Add threads to speed it up, or stream raw audio to a player. ```sh python tts.py "Your booking reference is AB-4471-Z, and the meeting moved to 3:30 p.m." -o out.wav --voice Zoey python tts.py "..." -o out.wav --threads 4 python tts.py "..." --pcm --first 2 | aplay -q -r 24000 -f S16_LE ``` From inside the mach-boom folder, the same thing works in Python. ```python from mach import MachBoom tts = MachBoom("mach-boom.bin", voices_dir="voices", tokenizer="tokenizer.json") wav = tts.synth("Call 020 7946 0018 before 5:30 p.m. on 14/03.", "Zoey", seed=0) # float32, 24 kHz for piece in tts.synth_stream("Your table is booked for 8 p.m.", "Zoey", first=2): ... ``` ```table Tool | What it does tts.py | Command-line script that writes a WAV file or streams raw audio MachBoom class | Python interface with synth for a whole clip and synth_stream for pieces voices folder | Holds the four shipped voices, Zoey, Juniper, Everly and Harper ``` Example: You give tts.py a sentence with a booking code and a meeting time, pick the voice Zoey and name out.wav as the output. The file comes back with that sentence spoken, numbers and times included. For newcomers and experts: Text to speech reads written words aloud. A good first step is to download the folder, install the two packages and run the one-line command with a sentence of your own. The code builds for several CPU instruction sets, from the newest AVX-512 forms down to plain C. Newer Xeons with AMX also decode audio in bf16 tiles. Fix the seed and the chunk size, and the audio stays identical whatever thread count you pick. In its own tests on one core, peak memory was 93 MB, and first audio came in 61 to 78 ms when streaming. The scores come from the authors, on their own hard test set. ```flow Your text and a voice name go in Kernels compile for your CPU on the first run The 3.5-bit model generates the speech A 24 kHz WAV file or audio stream comes out ``` ```chart Title: Word error rate on hard English sentences, lower is better Mach Boom: 5.18 percent Underdog Woof Juno: 6.50 percent Fish Audio Drama 3: 7.62 percent Phonon 31m fp32: 8.61 percent Phonon 31m 4-bit: 10.61 percent Kokoro 82M: 14.07 percent Pocket TTS: 18.13 percent Kitten Nano 0.8: 18.56 percent ``` Good to know: - License: Apache-2.0, shared with Phonon 31m and its voices - Cost to run: none named - Data it reaches: it reads the text you give it and writes audio files, and the source names no web or account access - Four voices ship with it, and voice files from Gradium's Phonon voices set drop in - Not for: other languages or systems, since the card lists English and Linux on x86-64 - Setting PHM_AMX=0 turns off the AMX path on newer Xeons ## On Hugging Face, 13 November 2026 - Task: text to speech - Downloads in the last 30 days: 0 - Likes: 23 - License: apache-2.0 - Languages: en - Based on: gradium/phonon-31m-en - Tags: text-to-speech, mach, on-device ## Links - Model: https://huggingface.co/SyzygyResearch/Mach-Boom - Files: https://huggingface.co/SyzygyResearch/Mach-Boom/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download SyzygyResearch/Mach-Boom ``` 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 [SyzygyResearch/Mach-Boom on Hugging Face](https://huggingface.co/SyzygyResearch/Mach-Boom) ## 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 | | --- | --- | --- | --- | --- | --- | | Mach Boom (this page) | 0 | 23 | — | apache-2.0 | 2026-11-10 | | [phonon-31m-en](https://mindket.com/p/8936ffbe-babc-4766-b999-5315308551db) | 248,578 | 160 | 41.1M | apache-2.0 | 2026-10-22 | | [Supertonic 3](https://mindket.com/p/910af7e2-128f-445f-bcb1-310264314e6d) | 92,359 | 1,043 | — | openrail | 2026-05-18 | | [Indic Tiny TTS: Hindi](https://mindket.com/p/689a3aa1-dbbe-4eb0-a816-383030be1806) | 508 | 2 | 8.9M | cc-by-4.0 | 2026-11-04 | | [Silencio-2](https://mindket.com/p/b6ecc2a3-db42-481d-add7-56b692bd8675) | 351 | 22 | 27.9M | cc-by-4.0 | 2026-11-01 | | [Klein-TTS-X1.5-Pro](https://mindket.com/p/c2bfb923-e753-414e-9acd-851872b8e3e6) | 258 | 10 | 13.1M | apache-2.0 | 2026-11-03 | 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: 2026-11-10 - First published: 2026-11-10 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open SyzygyResearch/Mach-Boom on Hugging Face](https://huggingface.co/SyzygyResearch/Mach-Boom) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9c03e356-11af-4e9a-9c5a-9050365d5f46) - [Its page on Mindket](https://mindket.com/p/9c03e356-11af-4e9a-9c5a-9050365d5f46)