personal samples

cli/wake_word_sample_augmenter

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 
 import sys, os, gc, glob, random
-import types, shutil, json
+import types
 from datetime import datetime, timezone
 from pathlib import Path
 from argparse import ArgumentParser as ArgParser, ArgumentError
@@ -9,12 +9,20 @@ from argparse import ArgumentParser as ArgParser, ArgumentError
 default_data_dir = os.getcwd() if os.path.exists(".mww-data-dir") else "/data"
 
 parser = ArgParser(exit_on_error=False)
-parser.add_argument("--data-dir", type=str, help=f"Data directory.  Default: {default_data_dir}", required=False, default=default_data_dir)
+parser.add_argument("--data-dir", type=str, help=f"Data directory. Default: {default_data_dir}", required=False, default=default_data_dir)
-parser.add_argument("--input-dir", type=str, help="Sample input directory.  Default: <data-dir>/work/wake_word_samples", required=False)
+
-parser.add_argument("--output-dir", type=str, help="Sample output directory.  Default: <input-dir>_augmented", required=False)
+# Wake word (TTS/generated) inputs/outputs
-parser.add_argument("--mit-rirs-16k-dir", type=str, help="MIT RIR input directory.  Default: <data-dir>/training_datasets/mit_rirs_16k", required=False)
+parser.add_argument("--input-dir", type=str, help="Wake word input dir. Default: <data-dir>/work/wake_word_samples", required=False)
-parser.add_argument("--fma-16k-dir", type=str, help="FMA input directory.  Default: <data-dir>/training_datasets/fma_16k", required=False)
+parser.add_argument("--output-dir", type=str, help="Wake word output dir. Default: <input-dir>_augmented", required=False)
-parser.add_argument("--audioset-16k-dir", type=str, help="Audioset input directory.  Default: <data-dir>/training_datasets/audioset_16k", required=False)
+
+# Personal inputs/outputs (NEW)
+parser.add_argument("--personal-dir", type=str, help="Personal WAV dir. Default: <data-dir>/personal_samples", required=False)
+parser.add_argument("--personal-output-dir", type=str, help="Personal features output dir. Default: <data-dir>/work/personal_augmented_features", required=False)
+
+# Dataset dirs
+parser.add_argument("--mit-rirs-16k-dir", type=str, help="MIT RIR input directory. Default: <data-dir>/training_datasets/mit_rirs_16k", required=False)
+parser.add_argument("--fma-16k-dir", type=str, help="FMA input directory. Default: <data-dir>/training_datasets/fma_16k", required=False)
+parser.add_argument("--audioset-16k-dir", type=str, help="Audioset input directory. Default: <data-dir>/training_datasets/audioset_16k", required=False)
 
 try:
     args = parser.parse_args()
@@ -23,10 +31,11 @@ except ArgumentError:
     sys.exit(1)
 
 args.data_dir = os.path.realpath(args.data_dir)
-work_dir = args.data_dir + "/work"
+work_dir = os.path.join(args.data_dir, "work")
 
+# Wake word defaults
 if not args.input_dir:
-    args.input_dir = work_dir + "/wake_word_samples"
+    args.input_dir = os.path.join(work_dir, "wake_word_samples")
 else:
     args.input_dir = os.path.realpath(args.input_dir)
 
@@ -35,24 +44,33 @@ if not args.output_dir:
 else:
     args.output_dir = os.path.realpath(args.output_dir)
 
+# Personal defaults (NEW)
+if not args.personal_dir:
+    args.personal_dir = os.path.join(args.data_dir, "personal_samples")
+else:
+    args.personal_dir = os.path.realpath(args.personal_dir)
+
+if not args.personal_output_dir:
+    args.personal_output_dir = os.path.join(work_dir, "personal_augmented_features")
+else:
+    args.personal_output_dir = os.path.realpath(args.personal_output_dir)
+
+# Dataset defaults
 if not args.mit_rirs_16k_dir:
-    args.mit_rirs_16k_dir = args.data_dir + "/training_datasets/mit_rirs_16k"
+    args.mit_rirs_16k_dir = os.path.join(args.data_dir, "training_datasets", "mit_rirs_16k")
 else:
     args.mit_rirs_16k_dir = os.path.realpath(args.mit_rirs_16k_dir)
 
 if not args.fma_16k_dir:
-    args.fma_16k_dir = args.data_dir + "/training_datasets/fma_16k"
+    args.fma_16k_dir = os.path.join(args.data_dir, "training_datasets", "fma_16k")
 else:
     args.fma_16k_dir = os.path.realpath(args.fma_16k_dir)
 
 if not args.audioset_16k_dir:
-    args.audioset_16k_dir = args.data_dir + "/training_datasets/audioset_16k"
+    args.audioset_16k_dir = os.path.join(args.data_dir, "training_datasets", "audioset_16k")
 else:
     args.audioset_16k_dir = os.path.realpath(args.audioset_16k_dir)
 
-out_path = Path(args.output_dir)
-out_path.mkdir(exist_ok=True)
-
 def validate_directories(paths):
     for path in paths:
         if not os.path.exists(path):
@@ -60,17 +78,12 @@ def validate_directories(paths):
             return False
     return True
 
-paths = [ work_dir, args.input_dir, args.output_dir, args.mit_rirs_16k_dir, args.fma_16k_dir, args.audioset_16k_dir ]
+required = [work_dir, args.input_dir, args.mit_rirs_16k_dir, args.fma_16k_dir, args.audioset_16k_dir]
-if not validate_directories(paths):
+if not validate_directories(required):
     parser.print_help()
     sys.exit(1)
 
-files = glob.glob(args.input_dir + "/*.wav")
+# -------------------- TF + libs --------------------
-if not files:
-    raise RuntimeError("❌ No WAVs in wake_word_samples.")
-max_samples = len(files)
-
-print(f"\n===== Augmenting {max_samples} wake word samples =====")
 print("   Initializing libraries")
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
@@ -86,7 +99,6 @@ print("   Loading Tensorflow")
 import tensorflow as tf
 
 print("   GPU memory config")
-# Per-device memory growth (belt + suspenders)
 for g in tf.config.list_physical_devices("GPU"):
     try:
         tf.config.experimental.set_memory_growth(g, True)
@@ -97,27 +109,15 @@ gc.collect()
 
 import numpy as np
 import librosa
-from tqdm import tqdm
 from mmap_ninja.ragged import RaggedMmap
 from microwakeword.audio.augmentation import Augmentation
 from microwakeword.audio.clips import Clips
 from microwakeword.audio.spectrograms import SpectrogramGeneration
-from microwakeword.audio.audio_utils import save_clip
 
 START_TIME = datetime.now(timezone.utc).replace(microsecond=0)
 
-# Paths to augmented data
+impulse_paths = [args.mit_rirs_16k_dir]
-impulse_paths = [ args.mit_rirs_16k_dir ]
+background_paths = [args.fma_16k_dir, args.audioset_16k_dir]
-background_paths = [ args.fma_16k_dir, args.audioset_16k_dir ]
-
-clips = Clips(
-    input_directory=args.input_dir,
-    file_pattern='*.wav',
-    max_clip_duration_s=5,
-    remove_silence=True,
-    random_split_seed=10,
-    split_count=0.1,
-)
 
 augmenter = Augmentation(
     augmentation_duration_s=3.2,
@@ -139,81 +139,107 @@ augmenter = Augmentation(
     max_jitter_s=0.3,
 )
 
-def audio_generator_from_wavs(self, split="train", repeat=1):
-    """
-    Yield 1-D float32 arrays loaded via librosa from input_dir/*.wav.
-    Deterministic 80/10/10 split with seed 10 to mirror original Clips behavior.
-    """
-    files = sorted(glob.glob(args.input_dir + "/*.wav"))
-    if not files:
-        raise RuntimeError("❌ No WAVs in wake_word_samples.")
-
-    rng = random.Random(10)   # deterministic shuffling like Clips(random_split_seed=10)
-    files_shuf = files[:]
-    rng.shuffle(files_shuf)
-
-    n = len(files_shuf)
-    n_val = max(1, int(0.10 * n))
-    n_test = max(1, int(0.10 * n))
-    n_train = max(0, n - n_val - n_test)
-    splits = {
-        "train":      files_shuf[:n_train],
-        "validation": files_shuf[n_train:n_train + n_val],
-        "test":       files_shuf[n_train + n_val:],
-    }
-    file_list = splits.get(split, [])
-    if not file_list:
-        return  # nothing to yield
-
-    for _ in range(max(1, int(repeat))):
-        for p in file_list:
-            y, sr = librosa.load(p, sr=16000, mono=True)
-            yield y.astype(np.float32, copy=False)
-
-# Bind the patched generator to your existing `clips` instance
-clips.audio_generator = types.MethodType(audio_generator_from_wavs, clips)
-
-# ---- Split config ----
 split_cfg = {
     "training":   {"name": "train",      "repetition": 2, "slide_frames": 10},
     "validation": {"name": "validation", "repetition": 1, "slide_frames": 10},
     "testing":    {"name": "test",       "repetition": 1, "slide_frames": 1},
 }
 
-# ---- Generate features ----
+def bind_wav_generator(clips_obj: Clips, wav_dir: str):
-for split, cfg in split_cfg.items():
+    """
-    out_dir = out_path / split
+    Patch clips.audio_generator so we load WAVs directly (deterministic 80/10/10 split, seed=10).
-    out_dir.mkdir(parents=True, exist_ok=True)
+    Matches the notebook behavior you posted.
-    print(f"   Augmenting {split}")
+    """
+    def audio_generator_from_wavs(self, split="train", repeat=1):
+        files = sorted(glob.glob(os.path.join(wav_dir, "*.wav")))
+        if not files:
+            return
 
-    print("      Generating spectrograms")
+        rng = random.Random(10)
-    spectros = SpectrogramGeneration(
+        files_shuf = files[:]
-        clips=clips,
+        rng.shuffle(files_shuf)
-        augmenter=augmenter,
+
-        slide_frames=cfg["slide_frames"],
+        n = len(files_shuf)
-        step_ms=10,
+        n_val = max(1, int(0.10 * n))
+        n_test = max(1, int(0.10 * n))
+        n_train = max(0, n - n_val - n_test)
+
+        splits = {
+            "train":      files_shuf[:n_train],
+            "validation": files_shuf[n_train:n_train + n_val],
+            "test":       files_shuf[n_train + n_val:],
+        }
+        file_list = splits.get(split, [])
+        if not file_list:
+            return
+
+        for _ in range(max(1, int(repeat))):
+            for p in file_list:
+                y, _sr = librosa.load(p, sr=16000, mono=True)
+                yield y.astype(np.float32, copy=False)
+
+    clips_obj.audio_generator = types.MethodType(audio_generator_from_wavs, clips_obj)
+
+def generate_feature_set(input_wav_dir: str, out_root_dir: str, label: str):
+    files = glob.glob(os.path.join(input_wav_dir, "*.wav"))
+    if not files:
+        print(f"ℹ️  No WAVs found for {label} in: {input_wav_dir} (skipping)")
+        return False
+
+    max_samples = len(files)
+    print(f"\n===== Augmenting {max_samples} wake word samples ({label}) =====")
+
+    clips = Clips(
+        input_directory=input_wav_dir,
+        file_pattern="*.wav",
+        max_clip_duration_s=5,
+        remove_silence=True,
+        random_split_seed=10,
+        split_count=0.1,
     )
 
-    print("      Generating files")
+    bind_wav_generator(clips, input_wav_dir)
-    print("      Sit tight — this step can take a while.")
 
-    gen = spectros.spectrogram_generator(
+    out_root = Path(out_root_dir)
-        split=cfg["name"],
+    out_root.mkdir(parents=True, exist_ok=True)
-        repeat=cfg["repetition"],
-    )
 
-    RaggedMmap.from_generator(
+    for split, cfg in split_cfg.items():
-        out_dir=str(out_dir / "wakeword_mmap"),
+        out_dir = out_root / split
-        sample_generator=gen,
+        out_dir.mkdir(parents=True, exist_ok=True)
-        batch_size=100,
+        print(f"   Augmenting {split} ({label})")
-        verbose=False,   # keep mmap quiet
-    )
 
-    print(f"      {split} augmentation complete")
+        spectros = SpectrogramGeneration(
+            clips=clips,
+            augmenter=augmenter,
+            slide_frames=cfg["slide_frames"],
+            step_ms=10,
+        )
+
+        gen = spectros.spectrogram_generator(
+            split=cfg["name"],
+            repeat=cfg["repetition"],
+        )
+
+        RaggedMmap.from_generator(
+            out_dir=str(out_dir / "wakeword_mmap"),
+            sample_generator=gen,
+            batch_size=100,
+            verbose=False,
+        )
+
+        print(f"      {split} augmentation complete ({label})")
+
+    print(f"✅ Features ready: {out_root_dir}/*/wakeword_mmap")
+    return True
+
+# Wake word generated/TTS features (existing behavior)
+generate_feature_set(args.input_dir, args.output_dir, "generated")
+
+# Personal features (NEW)
+generate_feature_set(args.personal_dir, args.personal_output_dir, "personal")
 
 END_TIME = datetime.now(timezone.utc).replace(microsecond=0)
 et = END_TIME - START_TIME
 print(f"\n{'=' * 80}")
-msg = f"Augmented {max_samples} wake word samples."
+print(f"{'Augmentation completed.':>50s} Elapsed time: {et!s}")
-print(f"{msg:>50s} Elapsed time: {et!s}")
 print(f"{'=' * 80}\n")

cli/wake_word_sample_trainer

@@ -60,43 +60,57 @@ check_directories() {
 check_directories ${WORK_DIR}/wake_word_samples_augmented \
     ${TRAINING_DS}/negative_datasets/{speech,dinner_party,no_speech,dinner_party_eval}
 
+# Personal features are optional, but if present they MUST have /training
+PERSONAL_FEATURES_DIR="${WORK_DIR}/personal_augmented_features"
+HAS_PERSONAL="false"
+if [ -d "${PERSONAL_FEATURES_DIR}/training" ] ; then
+  HAS_PERSONAL="true"
+  echo "✅ Found personal features: ${PERSONAL_FEATURES_DIR}/training (will weight sampling_weight=3.0)"
+else
+  echo "ℹ️  No personal features found at ${PERSONAL_FEATURES_DIR}/training (continuing without personal weighting)"
+fi
+
 cd "${WORK_DIR}"
 
 echo "===== Starting ${TRAINING_STEPS} training steps ====="
-
 START_TS=$EPOCHSECONDS
 
 mkdir -p "${WORK_DIR}/trained_models" || :
-cat <<EOF >"${WORK_DIR}/trained_models/training_parameters.yaml"
+
+# We write a YAML with a marker, then splice personal feature block in if it exists.
+YAML_PATH="${WORK_DIR}/trained_models/training_parameters.yaml"
+
+cat <<'EOF' > "${YAML_PATH}"
 batch_size: 16
 clip_duration_ms: 1500
 eval_step_interval: 500
 features:
-- features_dir: ${WORK_DIR}/wake_word_samples_augmented
+- features_dir: __WAKEWORD_FEATURES__
   penalty_weight: 1.0
   sampling_weight: 2.0
   truncation_strategy: truncate_start
   truth: true
   type: mmap
-- features_dir: ${TRAINING_DS}/negative_datasets/speech
+__PERSONAL_FEATURE_MARKER__
+- features_dir: __NEG_SPEECH__
   penalty_weight: 1.0
   sampling_weight: 12.0
   truncation_strategy: random
   truth: false
   type: mmap
-- features_dir: ${TRAINING_DS}/negative_datasets/dinner_party
+- features_dir: __NEG_DINNER__
   penalty_weight: 1.0
   sampling_weight: 12.0
   truncation_strategy: random
   truth: false
   type: mmap
-- features_dir: ${TRAINING_DS}/negative_datasets/no_speech
+- features_dir: __NEG_NOSPEECH__
   penalty_weight: 1.0
   sampling_weight: 5.0
   truncation_strategy: random
   truth: false
   type: mmap
-- features_dir: ${TRAINING_DS}/negative_datasets/dinner_party_eval
+- features_dir: __NEG_DINNER_EVAL__
   penalty_weight: 1.0
   sampling_weight: 0.0
   truncation_strategy: split
@@ -119,25 +133,46 @@ time_mask_count:
 - 0
 time_mask_max_size:
 - 0
-train_dir: ${WORK_DIR}/trained_models/wakeword
+train_dir: __TRAIN_DIR__
 training_steps:
-- ${TRAINING_STEPS}
+- __TRAINING_STEPS__
 window_step_ms: 10
-
 EOF
 
+# Replace placeholders (portable)
+sed -i \
+  -e "s|__WAKEWORD_FEATURES__|${WORK_DIR}/wake_word_samples_augmented|g" \
+  -e "s|__NEG_SPEECH__|${TRAINING_DS}/negative_datasets/speech|g" \
+  -e "s|__NEG_DINNER__|${TRAINING_DS}/negative_datasets/dinner_party|g" \
+  -e "s|__NEG_NOSPEECH__|${TRAINING_DS}/negative_datasets/no_speech|g" \
+  -e "s|__NEG_DINNER_EVAL__|${TRAINING_DS}/negative_datasets/dinner_party_eval|g" \
+  -e "s|__TRAIN_DIR__|${WORK_DIR}/trained_models/wakeword|g" \
+  -e "s|__TRAINING_STEPS__|${TRAINING_STEPS}|g" \
+  "${YAML_PATH}"
+
+# Insert/remove personal block
+if [ "${HAS_PERSONAL}" = "true" ]; then
+  # Insert directly after the wakeword feature block (matches your notebook: insert(1, ...))
+  perl -0777 -i -pe 's/__PERSONAL_FEATURE_MARKER__/\n- features_dir: '"${PERSONAL_FEATURES_DIR}"'\n  penalty_weight: 1.0\n  sampling_weight: 3.0\n  truncation_strategy: truncate_start\n  truth: true\n  type: mmap\n/g' "${YAML_PATH}"
+else
+  # Remove marker line entirely
+  sed -i -e "/__PERSONAL_FEATURE_MARKER__/d" "${YAML_PATH}"
+fi
+
 echo "   Wrote training_parameters.yaml"
 rm -rf "${WORK_DIR}/trained_models/wakeword"
 
-wake_word_filename="${WAKE_WORD//[ \`~\!\$&*$begin:math:text$$end:math:text$\{\}$begin:math:display$$end:math:display$\|\;\'\"<>.?\/]/_}"
+wake_word_filename="$(
+  echo "${WAKE_WORD}" \
+    | tr '[:upper:]' '[:lower:]' \
+    | sed -E 's/[^a-z0-9]+/_/g; s/^_+//; s/_+$//'
+)"
+[ -n "${wake_word_filename}" ] || wake_word_filename="wakeword"
+
 OUTPUT_DIR="${DATA_DIR}/output/$(date +'%Y-%m-%d-%H-%M-%S')-${wake_word_filename}-${SAMPLES}-${TRAINING_STEPS}"
 mkdir -p "${OUTPUT_DIR}/logs" || :
-
 TRAIN_LOG="${OUTPUT_DIR}/logs/training.log"
 
-# ------------------------------------------------------------------
-# Training args (same as before)
-# ------------------------------------------------------------------
 TRAIN_ARGS=(
   -m microwakeword.model_train_eval
   --training_config "${WORK_DIR}/trained_models/training_parameters.yaml"
@@ -159,10 +194,6 @@ TRAIN_ARGS=(
   --stride 2
 )
 
-# ------------------------------------------------------------------
-# GPU failure markers that should trigger CPU fallback
-# (OOM + known GPU runtime/copy/init failures)
-# ------------------------------------------------------------------
 GPU_FALLBACK_MARKERS=(
   "resourceexhaustederror"
   "resource exhausted"
@@ -189,7 +220,6 @@ run_attempt() {
   echo "→ ${PYTHON_BIN:-python} ${TRAIN_ARGS[*]}"
   echo
 
-  # stream everything except validation minibatch spam
   "${PYTHON_BIN:-python}" "${TRAIN_ARGS[@]}" 2>&1 \
     | tr '\r' '\n' \
     | stdbuf -i0 -o0 sed -r -e "/^Validation Batch/d" \
@@ -199,20 +229,17 @@ run_attempt() {
   return ${PIPESTATUS[0]}
 }
 
-# ---- Common TF env (mirrors your notebook) ----
 export TF_CPP_MIN_LOG_LEVEL="${TF_CPP_MIN_LOG_LEVEL:-2}"
 export TF_XLA_FLAGS="${TF_XLA_FLAGS:---tf_xla_auto_jit=0}"
 export NVIDIA_TF32_OVERRIDE="${NVIDIA_TF32_OVERRIDE:-1}"
 export TF_FORCE_GPU_ALLOW_GROWTH="${TF_FORCE_GPU_ALLOW_GROWTH:-true}"
 export TF_GPU_ALLOCATOR="${TF_GPU_ALLOCATOR:-cuda_malloc_async}"
 
-# Attempt 1: GPU
 if run_attempt "Attempt 1/2: GPU training (allow_growth + cuda_malloc_async)" ; then
   echo "✅ Training complete (GPU path)."
 else
   echo "⚠️  GPU attempt failed. Checking whether this looks like a GPU/OOM/runtime failure…"
 
-  # Check log for GPU/OOM/runtime markers
   log_lc="$(tr '[:upper:]' '[:lower:]' < "${TRAIN_LOG}" || true)"
   looks_like_gpu_fail="false"
   for m in "${GPU_FALLBACK_MARKERS[@]}"; do
@@ -225,7 +252,6 @@ else
   if [ "${looks_like_gpu_fail}" = "true" ]; then
     echo "↪️  Detected GPU/OOM/runtime failure markers. Falling back to CPU."
 
-    # Attempt 2: CPU (hide GPU completely)
     export CUDA_VISIBLE_DEVICES=""
     unset TF_GPU_ALLOCATOR
     if run_attempt "Attempt 2/2: CPU fallback (CUDA_VISIBLE_DEVICES='')" ; then
@@ -256,7 +282,6 @@ echo "   Full log: ${TRAIN_LOG}"
 
 tflite_filename="${wake_word_filename}.tflite"
 tflite_path="${OUTPUT_DIR}/${tflite_filename}"
-
 cp "${source_path}" "${tflite_path}"
 
 json_path="${OUTPUT_DIR}/${wake_word_filename}.json"