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175 lines
4.0 KiB
Markdown
175 lines
4.0 KiB
Markdown
<div align="center">
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<h1>microWakeWord NVIDIA Docker Trainer UI</h1>
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<img width="800" alt="Screenshot 2026-04-14 at 11 02 06 PM" src="https://github.com/user-attachments/assets/694f4cb7-e4d8-4e2b-80ec-b40fb41cbfff" />
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</div>
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Train custom microWakeWord models in Docker with:
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- uploaded personal voice samples
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- automatically generated Piper TTS samples
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- a browser-based trainer UI
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- live training logs in a popup console
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This project no longer records audio in the browser. The UI is now upload-first: users add their own audio files, the app validates or converts them, and training runs from the same page.
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---
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## Docker Image
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```bash
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docker pull ghcr.io/tatertotterson/microwakeword:latest
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```
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---
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## Run The Container
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```bash
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docker run -d \
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--gpus all \
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-p 8888:8888 \
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-v $(pwd):/data \
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ghcr.io/tatertotterson/microwakeword:latest
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```
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What these flags do:
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- `--gpus all` enables GPU acceleration
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- `-p 8888:8888` exposes the trainer UI
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- `-v $(pwd):/data` persists models, downloaded voices, datasets, and personal samples
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Then open:
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```text
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http://localhost:8888
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```
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---
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## What The UI Does
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- Start a wake word session
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- Test TTS pronunciation
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- Upload one or many personal samples
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- Normalize uploads to `16 kHz / mono / 16-bit PCM WAV`
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- Train with or without personal samples
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- Show a popup console with live progress and logs
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Personal samples are optional. If none are uploaded, the trainer can still proceed with TTS-only data after confirmation.
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---
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## Personal Samples
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Accepted upload formats include:
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- WAV
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- MP3
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- M4A
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- FLAC
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- OGG
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- AAC
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- OPUS
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- WEBM
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The backend validates or converts uploads with `ffmpeg` and stores the normalized files in:
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```text
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/data/personal_samples/
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```
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Notes:
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- starting a new session does not clear personal samples
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- use the `Clear personal samples` button if you want to wipe them
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- any uploaded personal samples are automatically included in training
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---
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## Language Support
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The language selector is dynamic.
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- `en` is always available
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- non-English languages are populated from Piper voice metadata
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- when you train with a non-English language, the backend downloads all Piper ONNX voices for that selected language only
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- it does not pre-download every language
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- already-downloaded voices are reused on later runs
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English stays on its existing dedicated generator model path. Non-English languages use the selected language's ONNX Piper voices.
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If the Piper catalog is unavailable, already-installed local voices can still be used.
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---
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## Training Behavior
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1. Enter the wake word
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2. Optionally test pronunciation
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3. Optionally upload personal samples
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4. Click `Start training`
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5. Watch the popup console for:
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- selected-language voice downloads when needed
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- sample generation progress
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- dataset setup
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- training progress and completion
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The `Open console` button lets you reopen the log window after closing it.
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---
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## First Run Notes
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The first real training run may download large training assets into `/data`, such as:
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- Piper voices for the selected language
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- training datasets and background data
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- Python training environment dependencies
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These are reused later unless you delete `/data`.
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---
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## Output Files
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Successful runs produce:
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```text
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/data/output/<wake_word>.tflite
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/data/output/<wake_word>.json
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```
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If those files already exist, the trainer creates timestamped backups before replacing them.
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---
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## Resetting Everything
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If you want a clean slate, stop the container and remove the contents of your mounted `/data` directory.
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That will remove:
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- personal samples
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- downloaded Piper voices
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- cached datasets
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- training environments
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- trained models
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---
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## Notes
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- browser microphone recording has been removed
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- personal samples are optional
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- the server module is now `trainer_server.py`
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- the launcher script is still named `run_recorder.sh` for compatibility
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---
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## Credits
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Built on top of:
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- [microWakeWord](https://github.com/kahrendt/microWakeWord)
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- [piper-sample-generator](https://github.com/rhasspy/piper-sample-generator)
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