owensong/Inflect-Micro-v3

Inflect Micro v3

A compact neural voice for the things you build.
Six English voices. Native 24 kHz speech. Run it locally.

A smaller speech core

Micro brings the Inflect family to an 11,787,634-parameter neural synthesis core. Give it English text and a preset voice; the included command-line interface writes a 24 kHz WAV file on your machine.

The core count and weight-file size are not the full installation size or runtime memory requirement. Pronunciation models, dependencies and fixed voice-conditioning assets are separate from the learned synthesis-core count. Download the whole repository, not just its weights.

Run Micro locally: this model repository is the complete v3 inference package, not just weights. Download and first generation | Choose a model and find the right files. The GitHub inflect/ folder is the older v1/v2 library, not the v3 runtime.

Voices

Six English voices. Native 24 kHz Micro output. Read each exact input below, or try your own text.

Cassidy
_{NATIVE 24 kHz}

Could you bring the smaller suitcase downstairs?

Mark
_{NATIVE 24 kHz}

Beyond the orchard, a narrow footpath curved toward the ruined tower.

Hope
_{NATIVE 24 kHz}

Press the side button once, then wait until the indicator turns green.

James
_{NATIVE 24 kHz}

A shadow becomes longer when the light source moves closer to the horizon.

Sophia
_{NATIVE 24 kHz}

We moved the chairs inside and wiped the wooden table dry.

Jasper
_{NATIVE 24 kHz}

I thought you were arriving tomorrow, but it is lovely to see you.

These are actual generated samples, not reference recordings. The same voice reads the same text across the v3 cards for model comparison. Different voices read different texts; this is not a voice-ranking test. Audio provenance.

Voice IDs: cassidy, mark, hope, james, sophia, jasper. No reference recording is needed.

Run locally

Use a fresh Python 3.12 environment outside the verified package. Keep environment files, profiles and generated audio outside the download.

Start in a Python 3.12 environment and download the complete package:

python -m pip install huggingface-hub==0.36.2
hf download owensong/Inflect-Micro-v3 --local-dir inflect-micro-v3
cd inflect-micro-v3

Install the provided CPU dependencies, verify the files, and make your first clip:

python -m pip install -r requirements-cpu.txt
python synthesize.py --verify
python synthesize.py --model micro --voice cassidy --text "A small idea can become something worth hearing." --output ../micro-cassidy.wav

Try another preset with a different output filename:

python synthesize.py --model micro --voice hope --text "A small idea can become something worth hearing." --output ../micro-hope.wav

The CLI requires --model micro. Its defaults are CPU, seed 42 and conservative English number preparation. It validates the full input before splitting longer text into chunks, with a short silence between chunks.

Use a new .wav path with an existing parent directory. The examples write beside the download; existing output files are never overwritten. Run python synthesize.py --help for the complete argument list.

Installation can require downloads. Once the dependencies and package assets are present, speech generation runs locally. --device cuda requires a compatible PyTorch/CUDA installation; the supplied CPU recipe is not a CUDA setup guide.

Features

Capability	Micro
English text to speech	Included
Six fixed voices	Included
Native 24 kHz WAV output	Included
Conservative number preparation and text chunking	Included
Package-file integrity verification	python synthesize.py --verify

Micro is a speech-only package. Event tags, the optional 48 kHz enhancer and VoiceTuner belong to Inflect Mini v3, not this runtime. Mini profile flags and bracketed directions such as [laugh] are not Micro controls.

English presets do not imply support for every language, accent, symbol or input format. Unsupported input can fail explicitly; review the spoken-text metadata when using numbers or unusual notation.

Specifications

Inside this download	Specification
Learned synthesis core	11,787,634 parameters
Core weights	models/micro/weights.pt
Serialized core weight file	48,056,467 bytes (48.06 MB, decimal)
Output	Native 24 kHz, mono PCM16 WAV
Voices	Six fixed English presets
Integrity inventory	Release manifest

Evaluation

Completed native24 base-speech results on the full 1,688-text corpus x six voices. Known development corpus; not an official submission or a new independent test. WER is percent (lower is better); quality proxies are not human MOS.

Metric	Result	Clips
WER %, equal dataset then equal voice mean	0.703185	10128
WER %, pooled word counts (separate)	0.843623	10128
UTMOS22Strong, objective score	4.392165	180
UTMOSv2, objective score	3.706836	180

Quality uses a separate 180-clip panel, 30 clips per voice. Full and subset WER scopes are labeled above.

Evaluation and methodology / Machine-readable results / Audio provenance.

Family

Explore the official Inflect v3 collection.

Three sizes, with a different synthesis core in each package.

Model	Focus
Inflect Mini v3	Larger speech core and its own optional components
Inflect Micro v3	Compact neural synthesis with an 11.79M-parameter core
Inflect Atom v3	A 157,190-parameter synthesis core with DSP waveform generation

Each repository includes its own runtime and dependencies. Family membership does not make model weights, flags or optional components interchangeable.

License and notices

Inflect-specific code and model weights are released under Apache-2.0. Read the license and component scope.

Upstream components retain their own licenses and notices. Keep the supplied third-party notices, Apache-2.0 text and StyleTTS2 MIT text with the components they cover. Pronunciation assets and installed dependencies retain their respective terms; the Inflect license does not replace those terms or grant voice/identity rights.

For questions, use model discussions, GitHub or Discord. For a reproducible issue, include the revision, voice, device and a short text example. Keep credentials and private recordings out of public reports.