Inflect-Nano-v1

<p align="center"> <img src="https://huggingface.co/owensong/Inflect-Nano-v1/resolve/main/assets/inflect-nano-banner.png" alt="Inflect-Nano banner" width="100%"> </p>

Inflect-Nano-v1

Inflect-Nano-v1 is a tiny English text-to-speech model with 4.63M total inference parameters, including its vocoder.

It is not trying to beat large TTS models. It is a small, local, complete text-to-waveform stack built to test how far ultra-lightweight speech synthesis can go.

Highlights

• 4.63M parameters total
• Includes the vocoder
• 24 kHz audio
• Single English male voice
• Runs locally with PyTorch
• Built for tiny-model experiments, local assistants, embedded demos, and efficient inference research

Listen

Install

git clone https://huggingface.co/owensong/Inflect-Nano-v1
cd Inflect-Nano-v1
pip install -r requirements.txt

Generate Speech

python inference.py --text "Wait, are you actually being for real now?" --out sample.wav

CPU:

python inference.py --device cpu --text "Please say neighborhood clearly." --out sample_cpu.wav

With simple controls:

python inference.py \
  --text "The appointment moved to 1:25." \
  --length-scale 1.03 \
  --pitch-scale 1.00 \
  --energy-scale 1.00 \
  --out sample_controlled.wav

Local Gradio demo:

python app.py

Model Size

The model files are:

weights/inflect_nano_v1_acoustic.pt
weights/inflect_nano_v1_vocoder.pt

Repo Layout

weights/                         model weights
examples/                        audio examples
assets/                          README banner
inflect_nano/                    runtime model code
third_party/tiny_tts_frontend/   vendored text frontend used for English G2P/token IDs
inference.py                     simple CLI inference
app.py                           local Gradio demo

The model itself is in weights/. The vendored frontend is included only so the released model can reproduce the same text normalization and tokenization path.

What Makes It Different

Many small TTS projects depend on a separate larger vocoder. Inflect-Nano-v1 includes the vocoder in the published inference stack, so the full text-to-waveform path stays under 5M parameters.

Pipeline:

text
-> English text frontend
-> compact FastSpeech-style acoustic model
-> 80-bin mel spectrogram
-> small Snake HiFi-GAN-style vocoder
-> 24 kHz waveform

Architecture

The acoustic model is a compact non-autoregressive FastSpeech-style network. It predicts duration, pitch, energy, and brightness, then decodes an 80-bin mel spectrogram.

The vocoder is a small Snake-activation HiFi-GAN-style generator trained for 24 kHz waveform reconstruction.

Main settings:

Good For

• Tiny local TTS experiments
• Offline assistant prototypes
• Efficient inference research
• Embedded speech demos
• Browser/WASM-style exploration
• A baseline for sub-5M TTS work

Not Good For

• Production narration
• Accessibility-critical output
• Voice cloning
• Multilingual speech
• High-fidelity audiobook generation
• Matching large modern TTS systems

Limitations

This is a very small experimental model. It can sound robotic, buzzy, or unstable, especially on difficult unseen text. Long prompts and unusual phrasing are less reliable. The vocoder is also a clear quality bottleneck.

Use it as a tiny-model research/demo release, not as a production TTS engine.

License

Apache-2.0.

This repository includes a small third-party English text frontend for tokenization/G2P compatibility. Its license is included at third_party/tiny_tts_frontend/LICENSE.