# Inflect-Nano-v1
Edit 06/17/2026 -- I'm really happy to see that this model is doing decently! If more people find it useful, I might consider training a v2 with a much larger budget. But for now, if you would like to see a v2, just like/favourite this model to get more people see it! Thank you for everyone for checking out this model.
**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
| Text | Audio |
|---|---|
| "Did the timing change?" she answered. "Then why did Logan leave?" | |
| Who puts a parking meter next to an ER label? | |
| Please say neighborhood, statistics, and anesthesiologist clearly, without rushing through the middle syllables. | |
| I said 91, not 306, which is a very different number. | |
| The inference path looked natural, but the decoder still needed a smoother transition before Marcus approved the final test. | |
| The appointment moved to 1:25, the invoice was $674.96, and the archive was labeled 1998. | |
| If Logan sounded uneasy, then it happened near Long Beach, and the pause has to carry that. | |
| The word aluminum should not steal attention from the softer ending after entrepreneur. | |
## Install
```bash
git clone https://huggingface.co/owensong/Inflect-Nano-v1
cd Inflect-Nano-v1
pip install -r requirements.txt
```
## Generate Speech
```bash
python inference.py --text "Wait, are you actually being for real now?" --out sample.wav
```
CPU:
```bash
python inference.py --device cpu --text "Please say neighborhood clearly." --out sample_cpu.wav
```
With simple controls:
```bash
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:
```bash
python app.py
```
## Model Size
| Part | Parameters |
|---|---:|
| Acoustic model | **3.465M** |
| Vocoder generator | **1.167M** |
| Total inference stack | **4.632M** |
The model files are:
```text
weights/inflect_nano_v1_acoustic.pt
weights/inflect_nano_v1_vocoder.pt
```
## Repo Layout
```text
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
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:
| Setting | Value |
|---|---:|
| Sample rate | 24 kHz |
| Mel bins | 80 |
| Acoustic hidden size | 168 |
| Encoder layers | 5 |
| Decoder layers | 6 |
| Vocoder upsample rates | 8, 8, 2, 2 |
## 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`.
