taco/microWakeWord-Trainer-Nvidia-Docker
1
0
Fork 0
mirror of https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git synced 2026-08-12 07:55:33 -06:00
Code Issues Packages Projects Releases Wiki Activity

Release NVIDIA WakeWord Trainer v13

Browse Source
This commit is contained in:
MasterPhooey
2026-07-17 20:38:18 -05:00
parent 7d77f71dc3
commit 5554b2eb5e
6 changed files with 235 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
README.md
View File

@@ -22,7 +22,7 @@ docker pull ghcr.io/tatertotterson/microwakeword:latest
Tagged releases also publish matching immutable image tags:
```bash
docker pull ghcr.io/tatertotterson/microwakeword:v12
docker pull ghcr.io/tatertotterson/microwakeword:v13
```
The release tag must match `VERSION`. Update `WHATS_NEW.md` before tagging; the Docker workflow prepends it to GitHub's automatically generated release notes.
@@ -32,7 +32,7 @@ Python 3.13 TensorFlow build for `sm_120`:
```bash
docker pull ghcr.io/tatertotterson/microwakeword:blackwell
docker pull ghcr.io/tatertotterson/microwakeword:v12-blackwell
docker pull ghcr.io/tatertotterson/microwakeword:v13-blackwell
```
Use the Blackwell image only for RTX 50-series cards. It includes the
@@ -53,9 +53,9 @@ docker run -d \
ghcr.io/tatertotterson/microwakeword:latest
```
Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v12` when you want to pin a known release instead of tracking `latest`.
Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v13` when you want to pin a known release instead of tracking `latest`.
For RTX 50-series cards, use `ghcr.io/tatertotterson/microwakeword:blackwell`
or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v12-blackwell`
or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v13-blackwell`
in the same `docker run` command.
The flags:
@@ -167,14 +167,21 @@ Starting a new session does not clear samples. Use the clear buttons in `Samples
## Auto Training
`Auto Training` is an opt-in false-positive loop. It is disabled until you enter the exact wake phrase and enable it.
`Auto Training` is an opt-in sample-review and retraining loop. It is disabled until you enter the exact wake phrase and enable it.
For each new wake-trigger clip sent to the trainer:
1. Faster Whisper transcribes the audio locally.
2. If the transcript contains the configured wake phrase, the clip stays in `Captured Audio` for manual positive review.
2. If the transcript contains the configured wake phrase, the clip stays in `Captured Audio` for manual review by default.
3. If speech was transcribed but the wake phrase is absent, the clip moves to `/data/negative_samples/` as an auto-reviewed hard negative.
4. Empty transcripts, close misses, VAD-blocked captures, and captures for another wake word stay out of the automatic negative path.
4. Empty transcripts, VAD-blocked captures, and captures for another wake word stay out of the automatic negative path.
Two optional cleanup rules are available:
- `Delete confirmed good wakes` removes normal wake-trigger clips after STT confirms the configured phrase.
- `Promote confirmed close misses` checks close misses that passed VAD and moves them to the personal positive samples only when STT confirms the configured phrase.
A close miss with an empty transcript or without the configured phrase stays in `Captured Audio`; it is never turned into a negative automatically. Saving Auto Training settings also scans existing eligible captures. Enabling close-miss promotion reviews previous unreviewed close misses, while enabling cleanup removes previously confirmed good wakes without transcribing them a second time.
The default `small.en` model uses CUDA with `float16` when CTranslate2 can see an NVIDIA GPU, and falls back to CPU with `int8`. Choose a multilingual Faster Whisper model such as `small` when the wake phrase is not English. Downloaded STT models are cached in `/data/auto_train_models/`.