cli + web recorder ui

cli/wake_word_sample_augmenter

@@ -71,17 +71,16 @@ if not files:
 max_samples = len(files)
 
 print(f"\n===== Augmenting {max_samples} wake word samples =====")
-
 print("   Initializing libraries")
 
-os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"
-os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]="true"
-os.environ["TF_GPU_ALLOCATOR"]="cuda_malloc_async"
-os.environ["TF_XLA_FLAGS"]="--tf_xla_auto_jit=0"
-os.environ["NVIDIA_TF32_OVERRIDE"]="1"
-os.environ["TF_CUDNN_WORKSPACE_LIMIT_IN_MB"]="512"
-os.environ["GLOG_minloglevel"]="9"
-os.environ["GRPC_VERBOSITY"]="ERROR"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
+os.environ["TF_GPU_ALLOCATOR"] = "cuda_malloc_async"
+os.environ["TF_XLA_FLAGS"] = "--tf_xla_auto_jit=0"
+os.environ["NVIDIA_TF32_OVERRIDE"] = "1"
+os.environ["TF_CUDNN_WORKSPACE_LIMIT_IN_MB"] = "512"
+os.environ["GLOG_minloglevel"] = "9"
+os.environ["GRPC_VERBOSITY"] = "ERROR"
 
 print("   Loading Tensorflow")
 import tensorflow as tf
@@ -98,6 +97,7 @@ 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
@@ -108,7 +108,7 @@ START_TIME = datetime.now(timezone.utc).replace(microsecond=0)
 
 # Paths to augmented data
 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,
@@ -139,8 +139,6 @@ augmenter = Augmentation(
     max_jitter_s=0.3,
 )
 
-# Augment samples and save the training, validation, and testing sets.
-
 def audio_generator_from_wavs(self, split="train", repeat=1):
     """
     Yield 1-D float32 arrays loaded via librosa from input_dir/*.wav.
@@ -175,7 +173,7 @@ def audio_generator_from_wavs(self, split="train", repeat=1):
 # Bind the patched generator to your existing `clips` instance
 clips.audio_generator = types.MethodType(audio_generator_from_wavs, clips)
 
-# ---- Split config (same as before) ----
+# ---- Split config ----
 split_cfg = {
     "training":   {"name": "train",      "repetition": 2, "slide_frames": 10},
     "validation": {"name": "validation", "repetition": 1, "slide_frames": 10},
@@ -188,28 +186,34 @@ for split, cfg in split_cfg.items():
     out_dir.mkdir(parents=True, exist_ok=True)
     print(f"   Augmenting {split}")
 
-    print(f"      Generating spectrograms")
+    print("      Generating spectrograms")
     spectros = SpectrogramGeneration(
-        clips=clips,                 # now backed by our WAV loader
-        augmenter=augmenter,         # your existing augmenter
+        clips=clips,
+        augmenter=augmenter,
         slide_frames=cfg["slide_frames"],
         step_ms=10,
     )
 
-    print(f"      Generating files")
+    print("      Generating files")
+    print("      Sit tight — this step can take a while.")
+
+    gen = spectros.spectrogram_generator(
+        split=cfg["name"],
+        repeat=cfg["repetition"],
+    )
+
     RaggedMmap.from_generator(
         out_dir=str(out_dir / "wakeword_mmap"),
-        sample_generator=spectros.spectrogram_generator(
-            split=cfg["name"], repeat=cfg["repetition"]
-        ),
+        sample_generator=gen,
         batch_size=100,
-        verbose=False,
+        verbose=False,   # keep mmap quiet
     )
+
     print(f"      {split} augmentation complete")
 
 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."
+msg = f"Augmented {max_samples} wake word samples."
 print(f"{msg:>50s} Elapsed time: {et!s}")
-print(f"{'=' * 80}\n")
+print(f"{'=' * 80}\n")