Inflect-Micro

About

What Inflect-Micro is

Fixed-voice English TTS with deterministic seeds, long-text handling, and CPU or CUDA inference.</p A note from Owen Thanks so much for all the support shown on this project! I built and funded Inflect v2 independently - if this release finds a real audience, I would like to continue the project with a v3, which might include things like more langauges, voices, and voice quality improvements. If the model is useful to you, leaving a like on Hugging Face genuinely helps more people discover it. New: public adaptation toolkit Prepare data, audit train/validation splits, adapt a fixed voice or language, resume training, evaluate checkpoints, and export PyTorch or ONNX packages with the Inflect adaptation toolkit. Adapted quality is experimental and depends on the dataset, frontend, and fluent-speaker evaluation. Inflect v2 uses one public API across two sizes: Micro prioritizes quality below 10M parameters; Nano prioritizes footprint below 4M. Listen These are held-out text generations, not reconstructions of training audio. Each transcript is shown exactly as passed to the public frontend. Evaluation No single metric captures TTS quality. Inflect v2 reports human preference, predicted naturalness, multi-ASR intelligibility, complete footprint, and runtime separately rather than compressing them into one unverifiable score. The headline row always refers to Inflect-Micro-v2. Detailed competitor results and protocol boundaries are kept visible below. Comparison set. Results include KittenTTS Nano, Piper Low, and Supertonic 3, established compact or local TTS baselines with larger deployable weight footprints than both Inflect releases. Weight sizes are compared at package level, and no single metric is treated as proof of overall superiority. 1. Human blind preference Inflect-Micro-v2 recorded a 66.2% preference rate (21 wins · 10 losses · 3 ties) in the final anonymous community study. Systems were hidden, left/right order was randomized, and ties count as half a win. This is descriptive community evidence, not formal MOS. 2. Predicted naturalness versus footprint The UTMOS22 run used 500 identical unseen prompts per voice. KittenTTS and Piper are equal-weight two-

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How audio & music work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys inflect-micro for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (inflect-micro below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="inflect-micro" -F file=@audio.mp3

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

Explore

More audio & music