owensong
/

Inflect-Micro-v2

@@ -1,207 +0,0 @@
-<p align="center">
-  <img src="assets/inflect-hero.png" alt="Inflect hero banner">
-</p>
-<p align="center">
-  <strong>Inflect-Nano is an ultra-small local English TTS project. Inflect-Nano-v1 is released on Hugging Face as a complete 4.63M-parameter text-to-waveform stack; Inflect-Nano-v2 is active research toward stronger 4M and 10M variants.</strong>
-</p>
-<p align="center">
-  <a href="#inflect-nano-v1">v1 Release</a> ·
-  <a href="#quickstart">Quickstart</a> ·
-  <a href="#architecture">Architecture</a> ·
-  <a href="#inflect-nano-v2">v2 Research</a> ·
-  <a href="#evaluation">Evaluation</a> ·
-  <a href="PUBLISHING.md">Publishing</a>
-</p>
----
-## Inflect-Nano-v1
-Inflect-Nano-v1 is a tiny English text-to-speech model with **4.632M total inference parameters**, including its vocoder.
-Released model:
-- [owensong/Inflect-Nano-v1 on Hugging Face](https://huggingface.co/owensong/Inflect-Nano-v1)
-The goal is not to beat large TTS systems. The goal is to prove how far a complete local text-to-waveform stack can be pushed at an extremely small size.
-### Highlights
-- 4.63M total inference parameters
-- Includes the vocoder
-- 24 kHz audio
-- Single English male voice
-- Local PyTorch inference
-- Built for tiny-model experiments, local assistants, embedded demos, and efficient inference research
-### Model Size
-| Part | Parameters |
-| --- | ---: |
-| Acoustic model | 3.465M |
-| Vocoder generator | 1.167M |
-| Total inference stack | 4.632M |
-Released model files:
-```text
-weights/inflect_nano_v1_acoustic.pt
-weights/inflect_nano_v1_vocoder.pt
-```
-## Quickstart
-Use the Hugging Face release for the runnable v1 model:
-```bash
-git clone https://huggingface.co/owensong/Inflect-Nano-v1
-cd Inflect-Nano-v1
-pip install -r requirements.txt
-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
-```
-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
-```
-## Architecture
-Inflect-Nano-v1 uses a compact non-autoregressive acoustic model plus a small waveform generator.
-```mermaid
-flowchart LR
-  A["Text"] --> B["English text frontend"]
-  B --> C["Compact FastSpeech-style acoustic model"]
-  C --> D["80-bin mel spectrogram"]
-  D --> E["Small Snake HiFi-GAN-style vocoder"]
-  E --> F["24 kHz waveform"]
-```
-Main v1 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 |
-The acoustic model predicts duration, pitch, energy, and brightness before decoding mel frames. The vocoder is a small Snake-activation HiFi-GAN-style generator trained for 24 kHz reconstruction.
-## Good For
-- Tiny local TTS experiments
-- Offline assistant prototypes
-- Efficient inference research
-- Embedded speech demos
-- Browser/WASM-style exploration
-- Baseline comparisons 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
-Inflect-Nano-v1 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, and the vocoder is a clear quality bottleneck.
-Use v1 as a tiny-model research/demo release, not as a production TTS engine.
-## Inflect-Nano-v2
-Inflect-Nano-v2 is the active research track. It is not released yet.
-Current direction:
-- two planned sizes: approximately 4M and 10M total inference parameters
-- single-voice English first, with cleaner finetuning paths later
-- teacher-distilled data from larger TTS systems
-- stronger acoustic model experiments around prior-plus-residual CFM
-- vocoder bakeoffs around compact HiFi-GAN, iSTFTNet, and source-filter variants
-- objective checks plus listening tests before any release claim
-The v2 rule is strict: do not trust training loss by itself. A candidate has to survive reconstruction tests, unseen-text listening, objective diagnostics, and a clear comparison against v1.
-## Evaluation
-Inflect is evaluated with both human listening and objective checks.
-```mermaid
-flowchart TD
-  A["Generate fixed prompt set"] --> B["Blind A/B listening"]
-  A --> C["Objective audio checks"]
-  B --> D["Accept, reject, or retry experiment"]
-  C --> D
-  D --> E["Document result"]
-```
-Tracked checks include:
-- pronunciation and text coverage
-- speaker consistency for single-voice releases
-- pacing and duration stability
-- skipped or invented words
-- leading clicks, long silences, and internal dropouts
-- loudness jumps
-- real-time factor
-- vocoder artifacts
-See [docs/EVALUATION.md](docs/EVALUATION.md).
-## Repository Map
-| Path | Purpose |
-| --- | --- |
-| [`inflect/`](inflect/) | Inflect-native modules and extension experiments. |
-| [`scripts/`](scripts/) | Dataset generation, training, rendering, evaluation, and release tooling. |
-| [`inflect_asr/`](inflect_asr/) | Side project for small ASR and teacher-label pipelines. |
-| [`voice-encoder/`](voice-encoder/) | Voice conditioning and paralinguistic research. |
-| [`docs/`](docs/) | Architecture, roadmap, evaluation, media kit, and release notes. |
-| [`examples/`](examples/) | Lightweight public examples and sample assets. |
-| [`assets/`](assets/) | README and media-kit visuals. |
-Local generated outputs, checkpoints, reference voices, virtual environments, and third-party checkouts are intentionally excluded from GitHub.
-## Publishing
-GitHub is the source, docs, and experiment-planning home. Hugging Face is the release home for runnable model weights and model cards.
-Current public release:
-- [owensong/Inflect-Nano-v1](https://huggingface.co/owensong/Inflect-Nano-v1)
-Future v2 releases should not replace v1 until they have better listening results, cleaner diagnostics, and a reproducible release package.
-## License
-Repository code and documentation are licensed under Apache 2.0 unless otherwise noted.
-Generated datasets, reference voices, model checkpoints, and third-party components may have separate terms. See [LICENSE](LICENSE), [PUBLISHING.md](PUBLISHING.md), and [SECURITY.md](SECURITY.md).