microWakeWord-Trainer-Nvidia-Docker

Train custom microWakeWord models in Docker with NVIDIA/CUDA acceleration, generated Piper samples, device-captured samples, reviewed false-wake negatives, live training logs, and ESPHome firmware flashing.

Real samples come from device-captured wake audio, close misses, or manual uploads. Every saved sample is normalized to 16 kHz / mono / 16-bit PCM WAV before training.

Docker Image

docker pull ghcr.io/tatertotterson/microwakeword:latest

Run The Container

docker run -d \
  --gpus all \
  --network host \
  -e REC_PORT=8789 \
  -v $(pwd):/data \
  ghcr.io/tatertotterson/microwakeword:latest

The flags:

--gpus all enables GPU acceleration.
--network host lets the container receive mDNS/zeroconf traffic for ESPHome auto-detect.
-e REC_PORT=8789 sets the trainer web UI and captured-audio port. Change this value if 8789 is already in use.
-v $(pwd):/data persists models, downloaded voices, datasets, samples, and firmware caches.

Host networking is recommended for the Firmware tab's mDNS device discovery. Manual IP flashing and captured-audio uploads can still work without host networking if the trainer port is reachable, but auto-detect may not see devices from Docker bridge networking.

Open:

http://localhost:8789

If you change REC_PORT, open that port instead and use the same port in the ESPHome Trainer App URL.

What The UI Does

Trainer starts a wake-word session, shows positive/negative sample counts, and launches training.
Captured Audio reviews clips sent by ESPHome sats, including wake hits, close misses, and false wakes.
Samples plays, removes, clears, and manually imports personal or negative samples.
Firmware builds the latest microWakeWords ESPHome YAMLs from GitHub and flashes VoicePE or Satellite1 over OTA.
Popup consoles show colorized training and firmware logs while long-running jobs are active.

Captured Audio Workflow

To collect samples from a sat, flash it with the Tater firmware from TaterTotterson/microWakeWords. The Firmware tab can build and flash the VoicePE or Satellite1 YAMLs directly from that repo.

After flashing, the device exposes ESPHome entities for capture setup:

Capture Wake Audio toggles upload of wake-word triggers.
Capture Close Misses toggles upload of near misses.
Trainer App URL sets the trainer address, for example http://<trainer-ip>:8789.

ESPHome devices can send raw captured audio to:

/api/upload_captured_audio_raw

Keep the training app running and reachable at the Trainer App URL while capture is enabled. The sats upload clips live; if the app is stopped or the URL is wrong, captured audio will not be saved.

In the Captured Audio tab:

play each clip from the inbox
mark good wake-word clips as This is good
mark bad triggers as False wake
discard clips that should not be used

Approved clips move into:

/data/personal_samples/

False wakes move into:

/data/negative_samples/

Captured audio is boosted for easier playback in the UI, then kept in the correct training format.

Samples

The Samples tab is the sample library.

Personal samples are positive examples of the wake word.
Negative samples are reviewed false wakes or hard negatives.
Both can be played back and removed one at a time.
Manual upload is available here as an optional seed path.

Accepted manual upload formats include:

WAV
MP3
M4A
FLAC
OGG
AAC
OPUS
WEBM

Uploads are validated or converted with ffmpeg into:

16 kHz / mono / 16-bit PCM WAV

Starting a new session does not clear samples. Use the clear buttons in Samples if you want to remove saved personal or negative clips.

Training Flow

Enter the wake phrase in Trainer.
Choose the language.
Optionally test pronunciation with Test TTS.
Review the positive and negative sample counts.
Click Start training.
Watch the popup training console.

Personal samples are optional. Training can run with zero personal samples after confirmation, using generated TTS samples and the stock negative datasets.

Reviewed negative samples are converted into /data/work/reviewed_negative_features/ and inserted into the training YAML as a hard-negative feature set when present.

Language Support

The language picker is dynamic.

en is always available.
English keeps the existing dedicated generator model path.
Non-English languages are discovered from the Piper voices catalog and any local Piper voice metadata.
When a non-English language is selected, the trainer downloads all voices for that selected language only.
Already-downloaded voices are reused.
It does not download every language up front.

If the upstream Piper catalog is unavailable, already-installed local voices are used when available.

Dataset Behavior

The first training run downloads and prepares missing training assets into /data, including:

Piper voices for the selected language
negative datasets and background data
the Python training environment
generated samples and augmented feature caches

After those assets are prepared, later runs reuse the local copies unless the mounted /data contents are deleted.

Firmware Flashing

The Firmware tab builds and flashes Tater firmware for supported ESPHome sats.

Downloads the latest firmware YAML templates from TaterTotterson/microWakeWords on GitHub.
Lets you choose VoicePE or Satellite1.
Auto-detects ESPHome devices with mDNS when the container is running with host networking.
Allows manual IP or hostname entry if discovery does not find the device.
Saves firmware form values so you do not re-enter sounds and URLs every run.
Lists locally trained wake words from /data/trained_wake_words/ for easy model selection.
Builds with ESPHome and flashes OTA.
Streams ESPHome output in a colorized firmware console.

Firmware YAMLs are intentionally pulled from GitHub each time. There is no local fallback path in the trainer UI.

Output Files

Successful runs produce timestamped training output folders such as:

/data/output/<timestamp>-<wake_word>-<samples>-<steps>/<wake_word>.tflite
/data/output/<timestamp>-<wake_word>-<samples>-<steps>/<wake_word>.json

The trainer also syncs firmware-ready artifacts into:

/data/trained_wake_words/<wake_word>.tflite
/data/trained_wake_words/<wake_word>.json

The firmware tab uses /data/trained_wake_words/ to populate the wake-word dropdown.

Resetting Everything

If you want a clean slate, stop the container and remove the contents of the mounted /data directory.

That removes:

personal samples
negative samples
captured inbox clips
downloaded Piper voices
cached datasets
training environments
trained models
firmware build caches

Important Notes

Personal samples are optional.
Negative samples are optional but useful for reducing false wakes.
The UI server is trainer_server.py.
The launcher is run.sh.
Firmware capture settings live on the ESPHome device and can be toggled from the device entities after flashing.

Credits

Built on top of:

README.md

taterassistant.com