Add support for personal samples

microWakeWord_training_notebook.ipynb

@@ -463,6 +463,7 @@
     "# To improve your model, experiment with these settings and use more sources of\n",
     "# background clips.\n",
     "import sys, os\n",
+    "from pathlib import Path\n",
     "\n",
     "# try the common places we’ve used\n",
     "candidates = [\n",
@@ -498,7 +499,8 @@
     "if not validate_directories(impulse_paths + background_paths):\n",
     "    raise ValueError(\"One or more required directories are missing.\")\n",
     "\n",
-    "clips = Clips(\n",
+    "# Process TTS generated samples (default)\n",
+    "clips_tts = Clips(\n",
     "    input_directory='./generated_samples',\n",
     "    file_pattern='*.wav',\n",
     "    max_clip_duration_s=5,\n",
@@ -507,6 +509,19 @@
     "    split_count=0.1,\n",
     ")\n",
     "\n",
+    "# Process personal recordings if available (optional)\n",
+    "clips_personal = None\n",
+    "if os.path.exists(\"./personal_samples\") and any(Path(\"./personal_samples\").glob(\"*.wav\")):\n",
+    "    clips_personal = Clips(\n",
+    "        input_directory=\"./personal_samples\",\n",
+    "        file_pattern=\"*.wav\",\n",
+    "        max_clip_duration_s=5,\n",
+    "        remove_silence=True,\n",
+    "        random_split_seed=10,\n",
+    "        split_count=0.1,\n",
+    "    )\n",
+    "    print(\"✅ Found personal samples, will create separate feature set\")\n",
+    "\n",
     "augmenter = Augmentation(\n",
     "    augmentation_duration_s=3.2,\n",
     "    augmentation_probabilities={\n",
@@ -593,14 +608,14 @@
     "from microwakeword.audio.spectrograms import SpectrogramGeneration\n",
     "\n",
     "# ---- Patch: drive clips from generated_samples/*.wav (no datasets.Audio, no torchcodec) ----\n",
-    "def audio_generator_from_wavs(self, split=\"train\", repeat=1):\n",
+    "def audio_generator_from_wavs(self, split=\"train\", repeat=1, source_dir=\"generated_samples\"):\n",
     "    \"\"\"\n",
-    "    Yield 1-D float32 arrays loaded via librosa from generated_samples/*.wav.\n",
+    "    Yield 1-D float32 arrays loaded via librosa from source_dir/*.wav.\n",
     "    Deterministic 80/10/10 split with seed 10 to mirror original Clips behavior.\n",
     "    \"\"\"\n",
-    "    files = sorted(glob.glob(\"generated_samples/*.wav\"))\n",
+    "    files = sorted(glob.glob(f\"{source_dir}/*.wav\"))\n",
     "    if not files:\n",
-    "        raise SystemExit(\"❌ No WAVs in generated_samples/. Generate TTS samples first.\")\n",
+    "        raise SystemExit(f\"❌ No WAVs in {source_dir}/. Generate samples first.\")\n",
     "\n",
     "    rng = random.Random(10)   # deterministic shuffling like Clips(random_split_seed=10)\n",
     "    files_shuf = files[:]\n",
@@ -624,9 +639,19 @@
     "            y, sr = librosa.load(p, sr=16000, mono=True)\n",
     "            yield y.astype(np.float32, copy=False)\n",
     "\n",
-    "# Bind the patched generator to your existing `clips` instance\n",
-    "clips.audio_generator = types.MethodType(audio_generator_from_wavs, clips)\n",
-    "print(\"✅ Patched clips.audio_generator to stream from generated_samples/*.wav (no torchcodec).\")\n",
+    "# Bind the patched generator to clips_tts instance\n",
+    "def audio_generator_tts(self, split=\"train\", repeat=1):\n",
+    "    return audio_generator_from_wavs(self, split, repeat, \"generated_samples\")\n",
+    "\n",
+    "clips_tts.audio_generator = types.MethodType(audio_generator_tts, clips_tts)\n",
+    "print(\"✅ Patched clips_tts.audio_generator to stream from generated_samples/*.wav (no torchcodec).\")\n",
+    "\n",
+    "# Bind the patched generator to clips_personal if it exists\n",
+    "if clips_personal is not None:\n",
+    "    def audio_generator_personal(self, split=\"train\", repeat=1):\n",
+    "        return audio_generator_from_wavs(self, split, repeat, \"personal_samples\")\n",
+    "    clips_personal.audio_generator = types.MethodType(audio_generator_personal, clips_personal)\n",
+    "    print(\"✅ Patched clips_personal.audio_generator to stream from personal_samples/*.wav (no torchcodec).\")\n",
     "\n",
     "# ---- Validate augmentation asset folders exist ----\n",
     "def validate(paths):\n",
@@ -649,14 +674,14 @@
     "    \"testing\":    {\"name\": \"test\",       \"repetition\": 1, \"slide_frames\": 1},\n",
     "}\n",
     "\n",
-    "# ---- Generate features ----\n",
+    "# ---- Generate features for TTS samples ----\n",
     "for split, cfg in split_cfg.items():\n",
     "    out_dir = out_root / split\n",
     "    out_dir.mkdir(parents=True, exist_ok=True)\n",
-    "    print(f\"🧪 Processing {split} …\")\n",
+    "    print(f\"🧪 Processing {split} (TTS) …\")\n",
     "\n",
     "    spectros = SpectrogramGeneration(\n",
-    "        clips=clips,                 # now backed by our WAV loader\n",
+    "        clips=clips_tts,             # now backed by our WAV loader\n",
     "        augmenter=augmenter,         # your existing augmenter\n",
     "        slide_frames=cfg[\"slide_frames\"],\n",
     "        step_ms=10,\n",
@@ -671,6 +696,27 @@
     "        verbose=True,\n",
     "    )\n",
     "\n",
+    "# ---- Generate features for personal samples if available ----\n",
+    "if clips_personal is not None:\n",
+    "    out_root_personal = Path(\"personal_augmented_features\")\n",
+    "    out_root_personal.mkdir(exist_ok=True)\n",
+    "    for split, cfg in split_cfg.items():\n",
+    "        out_dir = out_root_personal / split\n",
+    "        out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "        print(f\"🧪 Processing {split} (personal) …\")\n",
+    "        spectros = SpectrogramGeneration(\n",
+    "            clips=clips_personal,\n",
+    "            augmenter=augmenter,\n",
+    "            slide_frames=cfg[\"slide_frames\"],\n",
+    "            step_ms=10,\n",
+    "        )\n",
+    "        RaggedMmap.from_generator(\n",
+    "            out_dir=str(out_dir / \"wakeword_mmap\"),\n",
+    "            sample_generator=spectros.spectrogram_generator(split=cfg[\"name\"], repeat=cfg[\"repetition\"]),\n",
+    "            batch_size=100,\n",
+    "            verbose=True,\n",
+    "        )\n",
+    "\n",
     "print(\"✅ Features ready (generated_augmented_features/*/wakeword_mmap)\")"
    ]
   },
@@ -752,6 +798,7 @@
     "# --- Save a yaml config that controls the training process ---\n",
     "\n",
     "import os, sys, yaml\n",
+    "from pathlib import Path\n",
     "\n",
     "config = {}\n",
     "\n",
@@ -766,6 +813,11 @@
     "    {\"features_dir\":\"negative_datasets/dinner_party_eval\",\"sampling_weight\":0.0,\"penalty_weight\":1.0,\"truth\":False,\"truncation_strategy\":\"split\",\"type\":\"mmap\"},\n",
     "]\n",
     "\n",
+    "# Add personal features if they exist\n",
+    "if os.path.exists(\"personal_augmented_features/training\"):\n",
+    "    config[\"features\"].insert(1, {\"features_dir\": \"personal_augmented_features\", \"sampling_weight\": 3.0, \"penalty_weight\": 1.0, \"truth\": True, \"truncation_strategy\": \"truncate_start\", \"type\": \"mmap\"})\n",
+    "    print(\"✅ Added personal features with higher weight (3.0)\")\n",
+    "\n",
     "config[\"training_steps\"] = [40000]\n",
     "config[\"positive_class_weight\"] = [1]\n",
     "config[\"negative_class_weight\"] = [20]\n",