metadata
license: apache-2.0
language:
- en
pipeline_tag: text-to-speech
base_model: owensong/Inflect-Micro-v2
model_name: Inflect-Micro-v2-ONNX
tags:
- onnx
- onnxruntime
- text-to-speech
- speech-synthesis
- local-tts
- cpu
- cuda
- directml
- edge-ai
- small-model
- vits
- 24khz
inference: false
Inflect-Micro-v2 ONNX
Official, verified FP32 ONNX Runtime export of Inflect-Micro-v2. This repository is a format export only: no training, pruning, or quantization was applied.
- Complete neural weights: approximately 37.75 MB
- Output: 24 kHz mono waveform
- Providers: CPU, CUDA, or DirectML through ONNX Runtime
- Frontend: Python and eSpeak-ng
- License: Apache 2.0
The learned model is split into:
| Graph | Purpose | Size |
|---|---|---|
onnx/duration.onnx |
Tokens to aligned acoustic distribution | 7.32 MB |
onnx/decode.onnx |
Acoustic distribution and seeded noise to waveform | 30.43 MB |
Quick start
python -m pip install -r onnx/requirements.txt
python onnx/inference_onnx.py \
--text "A small voice can still have something meaningful to say." \
--output sample.wav \
--provider cpu
See the complete ONNX guide for the Python API, provider selection, long-text behavior, parity measurements, checksums, and reproducible export instructions.
Verification
The graphs pass ONNX checker validation, dynamic-length inference,
short- and long-form synthesis, seed determinism, and direct numerical parity
testing against the canonical PyTorch checkpoint. See
onnx/parity_report.json and
onnx/SOURCE.json.
The canonical architecture, evaluation, audio samples, PyTorch checkpoint, and project documentation remain at owensong/Inflect-Micro-v2.