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167 lines
4.1 KiB
Markdown
167 lines
4.1 KiB
Markdown
<div align="center">
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<h1>🎙️ microWakeWord Nvidia Trainer & Recorder</h1>
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<img width="1002" height="593" alt="Screenshot 2026-01-18 at 8 13 35 AM" src="https://github.com/user-attachments/assets/e1411d8a-8638-4df8-992b-09a46c6e5ddc" />
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</div>
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Train **microWakeWord** detection models using a simple **web-based recorder + trainer UI**, packaged in a Docker container.
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No Jupyter notebooks required. No manual cell execution. Just record your voice (optional) and train.
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---
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<img width="100" height="44" alt="unraid_logo_black-339076895" src="https://github.com/user-attachments/assets/87351bed-3321-4a43-924f-fecf2e4e700f" />
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**microWakeWord_Trainer-Nvidia** is available in the **Unraid Community Apps** store.
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Intall directly from the Unraid App Store with a one-click template.
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### 🚀 Docker
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### 1️⃣ Pull the 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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### 2️⃣ Run the Container
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```bash
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docker run --rm -it \
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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 Recorder + Trainer WebUI
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- `-v $(pwd):/data` → Persists all models, datasets, and cache
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---
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### 3️⃣ Open the Recorder WebUI
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Open your browser and go to:
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👉 **http://localhost:8888**
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You’ll see the **microWakeWord Recorder & Trainer UI**.
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---
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## 🎤 Recording Voice Samples (Optional)
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Personal voice recordings are **optional**.
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- You may **record your own voice** for better accuracy
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- Or simply **click “Train” without recording anything**
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If no recordings are present, training will proceed using **synthetic TTS samples only**.
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### Remote systems (important)
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If you are running this on a **remote PC / server**, browser-based recording will not work unless:
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- You use a **reverse proxy** (HTTPS + mic permissions), **or**
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- You access the UI via **localhost** on the same machine
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Training itself works fine remotely — only recording requires local microphone access.
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---
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### 🎙️ Recording Flow
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1. Enter your wake word
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2. Test pronunciation with **Test TTS**
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3. Choose:
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- Number of speakers (e.g. family members)
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- Takes per speaker (default: 10)
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4. Click **Begin recording**
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5. Speak naturally — recording:
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- Starts when you talk
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- Stops automatically after silence
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6. Repeat for each speaker
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Files are saved automatically to:
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```
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personal_samples/
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speaker01_take01.wav
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speaker01_take02.wav
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speaker02_take01.wav
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...
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```
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---
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## 🧠 Training Behavior (Important Notes)
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### ⏬ First training run
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The **first time you click Train**, the system will download **large training datasets** (background noise, speech corpora, etc.).
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- This can take **several minutes**
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- This happens **only once**
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- Data is cached inside `/data`
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You **will NOT need to download these again** unless you delete `/data`.
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---
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### 🔁 Re-training is safe and incremental
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- You can train **multiple wake words** back-to-back
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- You do **NOT** need to clear any folders between runs
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- Old models are preserved in timestamped output directories
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- All required cleanup and reuse logic is handled automatically
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---
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## 📦 Output Files
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When training completes, you’ll get:
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- `<wake_word>.tflite` – quantized streaming model
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- `<wake_word>.json` – ESPHome-compatible metadata
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Both are saved under:
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```text
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/data/output/
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```
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Each run is placed in its own timestamped folder.
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---
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## 🎤 Optional: Personal Voice Samples (Advanced)
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If you record personal samples:
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- They are automatically augmented
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- They are **up-weighted during training**
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- This significantly improves real-world accuracy
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No configuration required — detection is automatic.
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---
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## 🔄 Resetting Everything (Optional)
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If you want a **completely clean slate**:
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Delete the /data folder
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Then restart the container.
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⚠️ This will:
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- Remove cached datasets
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- Require re-downloading training data
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- Delete trained models
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---
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## 🙌 Credits
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Built on top of the excellent
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**https://github.com/kahrendt/microWakeWord**
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Huge thanks to the original authors ❤️
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