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Tater Totterson
2025-09-27 11:10:43 -05:00
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parent 3eea499f90
commit e9d6b8b87e
3 changed files with 262 additions and 203 deletions
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14
dockerfile
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@@ -1,17 +1,21 @@
# CUDA + cuDNN userspace from NVIDIA (no manual repo installs needed)
FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04
# CUDA + cuDNN userspace from NVIDIA (Ubuntu 22.04)
FROM nvidia/cuda:12.6.2-cudnn-runtime-ubuntu22.04
ENV DEBIAN_FRONTEND=noninteractive \
PYTHONUNBUFFERED=1 \
PIP_NO_CACHE_DIR=1
PIP_NO_CACHE_DIR=1 \
PIP_ROOT_USER_ACTION=ignore \
HF_HUB_DISABLE_SYMLINKS_WARNING=1
# System deps (+dev headers for building C/C++ extensions)
RUN apt-get update && apt-get install -y --no-install-recommends \
python3.10 python3.10-venv python3.10-distutils python3.10-dev python3-pip \
git wget curl unzip ca-certificates \
git wget curl unzip ca-certificates git-lfs \
build-essential g++ cmake \
libsndfile1 libsndfile1-dev libffi-dev \
&& rm -rf /var/lib/apt/lists/*
ffmpeg \
&& rm -rf /var/lib/apt/lists/*
# Use python3.10 everywhere
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1 \

440
microWakeWord_training_notebook.ipynb
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@@ -160,10 +160,12 @@
"print(\"Linux/NVIDIA detected — using main piper-sample-generator repo.\")\n",
"safe_clone(REPO_URL)\n",
"\n",
"# 2) Install deps (GPU ONNX)\n",
"# - piper-phonemize-cross provides phonemization\n",
"# - onnxruntime-gpu enables CUDA (container must have CUDA + drivers)\n",
"# 2) Install deps\n",
"# - piper-tts provides the `piper` module (required by generate_samples.py)\n",
"# - piper-phonemize-cross does the phonemization\n",
"# - onnxruntime-gpu enables CUDA (container must have NVIDIA runtime)\n",
"deps = [\n",
" \"piper-tts>=1.2.0\",\n",
" \"piper-phonemize-cross==1.2.1\",\n",
" \"soundfile\",\n",
" \"numpy\",\n",
@@ -195,6 +197,7 @@
"cmd = [\n",
" sys.executable, str(gen_script),\n",
" TARGET_WORD,\n",
" \"--model\", str(MODELS_DIR / MODEL_NAME), # ← pass the generator .pt explicitly\n",
" \"--max-samples\", \"1\",\n",
" \"--batch-size\", \"1\",\n",
" \"--output-dir\", str(AUDIO_OUT_DIR),\n",
@@ -217,17 +220,27 @@
},
"outputs": [],
"source": [
"# Generates a larger amount of wake word samples.\n",
"# Start here when trying to improve your model.\n",
"# See https://github.com/rhasspy/-m piper-sample-generator for the full set of\n",
"# parameters. In particular, experiment with noise-scales and noise-scale-ws,\n",
"# generating negative samples similar to the wake word, and generating many more\n",
"# wake word samples, possibly with different phonetic pronunciations.\n",
"# Generate a large number of wake word samples for training\n",
"import sys, subprocess\n",
"from pathlib import Path\n",
"\n",
"!\"{sys.executable}\" piper-sample-generator/generate_samples.py \"{target_word}\" \\\n",
"--max-samples 50000 \\\n",
"--batch-size 100 \\\n",
"--output-dir generated_samples"
"target_word = \"hey_tater\"\n",
"REPO_DIR = Path.cwd() / \"piper-sample-generator\"\n",
"MODELS_DIR = REPO_DIR / \"models\"\n",
"MODEL_NAME = \"en_US-libritts_r-medium.pt\"\n",
"\n",
"cmd = [\n",
" sys.executable,\n",
" str(REPO_DIR / \"generate_samples.py\"),\n",
" target_word,\n",
" \"--model\", str(MODELS_DIR / MODEL_NAME), # important: specify generator .pt\n",
" \"--max-samples\", \"50000\",\n",
" \"--batch-size\", \"100\",\n",
" \"--output-dir\", \"generated_samples\",\n",
"]\n",
"\n",
"print(\"→\", \" \".join(cmd))\n",
"subprocess.run(cmd, check=True)"
]
},
{
@@ -238,150 +251,132 @@
},
"outputs": [],
"source": [
"# Downloads audio data for augmentation. This can be slow!\n",
"# Borrowed from openWakeWord's automatic_model_training.ipynb, accessed March 4, 2024\n",
"#\n",
"# **Important note!** The data downloaded here has a mixture of difference\n",
"# licenses and usage restrictions. As such, any custom models trained with this\n",
"# data should be considered as appropriate for **non-commercial** personal use only.\n",
"# NVIDIA/Linux dataset prep to match the Apple behavior, but without datasets.Audio (no TorchCodec)\n",
"# MIT RIR -> resample to 16 kHz\n",
"# AudioSet -> NO resample\n",
"# FMA -> resample to 16 kHz mono\n",
"\n",
"import os\n",
"import scipy.io.wavfile\n",
"import numpy as np\n",
"from datasets import Dataset, Audio, load_dataset\n",
"import os, sys, scipy.io.wavfile, numpy as np\n",
"from pathlib import Path\n",
"from tqdm import tqdm\n",
"import soundfile as sf\n",
"import librosa\n",
"from datasets import load_dataset\n",
"\n",
"def write_wav(dst: Path, data: np.ndarray, sr: int):\n",
" x = np.clip(data, -1.0, 1.0)\n",
" scipy.io.wavfile.write(dst, sr, (x * 32767).astype(np.int16))\n",
"\n",
"# -----------------------------\n",
"# Download and Process MIT RIR\n",
"# MIT RIR (resample to 16 kHz)\n",
"# -----------------------------\n",
"output_dir = \"./mit_rirs\"\n",
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
" rir_dataset = load_dataset(\"davidscripka/MIT_environmental_impulse_responses\", split=\"train\", streaming=True)\n",
" print(f\"Downloading MIT RIR dataset to {output_dir}...\")\n",
" for row in tqdm(rir_dataset):\n",
" name = row[\"audio\"][\"path\"].split(\"/\")[-1]\n",
" scipy.io.wavfile.write(\n",
" os.path.join(output_dir, name), \n",
" 16000, \n",
" (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
" )\n",
" print(f\"Finished downloading MIT RIR dataset to {output_dir}.\\n\")\n",
"else:\n",
" print(f\"{output_dir} already exists. Skipping download.\")\n",
"\n",
"# -----------------------------\n",
"# Download and Process Audioset\n",
"# -----------------------------\n",
"\n",
"# Directory setup\n",
"audioset_dir = \"./audioset\"\n",
"output_dir = \"./audioset_16k\"\n",
"os.makedirs(audioset_dir, exist_ok=True)\n",
"os.makedirs(output_dir, exist_ok=True)\n",
"\n",
"# Full-scale dataset download links\n",
"dataset_links = [\n",
" f\"https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main/data/bal_train0{i}.tar\"\n",
" for i in range(10)\n",
"]\n",
"\n",
"# Download and extract each dataset part\n",
"for link in dataset_links:\n",
" file_name = link.split(\"/\")[-1]\n",
" out_path = os.path.join(audioset_dir, file_name)\n",
" if not os.path.exists(out_path):\n",
" print(f\"Downloading {file_name}...\")\n",
" os.system(f\"wget --quiet -O {out_path} {link}\")\n",
" print(f\"Extracting {file_name}...\")\n",
" os.system(f\"tar -xf {out_path} -C {audioset_dir}\")\n",
"\n",
"# Collect all FLAC files for processing\n",
"audioset_files = list(Path(audioset_dir).glob(\"**/*.flac\"))\n",
"print(f\"Number of FLAC files found: {len(audioset_files)}\")\n",
"\n",
"if audioset_files:\n",
" corrupted_files = []\n",
"\n",
" print(\"Converting Audioset files to 16kHz WAV...\")\n",
" for file_path in tqdm(audioset_files, desc=\"Processing Audioset files\"):\n",
"print(\"=== MIT RIR ===\")\n",
"rir_out = Path(\"mit_rirs\")\n",
"rir_out.mkdir(exist_ok=True)\n",
"if not any(rir_out.rglob(\"*.wav\")):\n",
" ok = 0\n",
" try:\n",
" # Avoid datasets.Audio to keep TorchCodec out:\n",
" # Use streaming=True + Audio(decode=False)-equivalent: access raw file path and decode with librosa\n",
" print(\"⬇️ MIT RIR (streaming + manual decode)…\")\n",
" ds = load_dataset(\"davidscripka/MIT_environmental_impulse_responses\",\n",
" split=\"train\", streaming=True)\n",
" for i, row in enumerate(tqdm(ds)):\n",
" try:\n",
" audio_path = row[\"audio\"][\"path\"]\n",
" y, sr = librosa.load(audio_path, sr=16000, mono=True)\n",
" write_wav(rir_out / f\"rir_{i:04d}.wav\", y, 16000)\n",
" ok += 1\n",
" except Exception:\n",
" pass\n",
" print(f\"✅ MIT RIR saved: {ok} files\")\n",
" except Exception as e:\n",
" print(f\"⚠️ MIT RIR download failed: {e}\")\n",
" # Fallback to official ZIP if needed (rare)\n",
" try:\n",
" # Attempt to load the file and handle any errors\n",
" audio, sampling_rate = sf.read(file_path)\n",
" \n",
" if audio is None or len(audio) == 0:\n",
" raise ValueError(f\"Empty or invalid audio data in file: {file_path}\")\n",
"\n",
" # Resample audio to 16kHz\n",
" output_path = Path(output_dir) / (file_path.stem + \".wav\")\n",
" scipy.io.wavfile.write(\n",
" output_path,\n",
" 16000,\n",
" (audio * 32767).astype(np.int16),\n",
" )\n",
" except (sf.LibsndfileError, ValueError, Exception) as e:\n",
" # Log the error and skip the file\n",
" print(f\"Error converting {file_path}: {e}\")\n",
" corrupted_files.append(str(file_path))\n",
"\n",
" # Log corrupted files\n",
" if corrupted_files:\n",
" log_path = Path(output_dir) / \"audioset_corrupted_files.log\"\n",
" with open(log_path, \"w\") as log_file:\n",
" log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
" print(f\"Logged corrupted files to {log_path}\")\n",
" print(\"⬇️ MIT RIR (fallback ZIP)…\")\n",
" zip_url = \"https://mcdermottlab.mit.edu/Reverb/IRMAudio/Audio.zip\"\n",
" zip_path = rir_out.parent / \"MIT_RIR_Audio.zip\"\n",
" if not zip_path.exists():\n",
" os.system(f\"wget -q -O '{zip_path}' '{zip_url}'\")\n",
" os.system(f'unzip -q -o \"{zip_path}\" -d \"{rir_out}\"')\n",
" # Normalize to 16k mono\n",
" for p in tqdm(list(rir_out.rglob(\"*.wav\")), desc=\"Normalize MIT RIR\"):\n",
" a, sr = sf.read(p, always_2d=False)\n",
" if a.ndim > 1: a = a[:,0]\n",
" if sr != 16000:\n",
" a, _ = librosa.load(p, sr=16000, mono=True)\n",
" write_wav(p, a, 16000)\n",
" print(\"✅ MIT RIR fallback complete\")\n",
" except Exception as e2:\n",
" print(f\"❌ MIT RIR fallback failed: {e2}\")\n",
"else:\n",
" print(\"No FLAC files found in Audioset.\")\n",
"\n",
"print(\"Audioset processing complete!\")\n",
"\n",
" print(\"✅ mit_rirs exists; skipping.\")\n",
"\n",
"# -----------------------------\n",
"# Download and Process FMA\n",
"# AudioSet (NO resample — fast)\n",
"# -----------------------------\n",
"output_dir = \"./fma\"\n",
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
" fname = \"fma_xs.zip\"\n",
" link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n",
" out_dir = os.path.join(output_dir, fname)\n",
" os.system(f\"wget -q -O {out_dir} {link}\")\n",
" os.system(f\"cd {output_dir} && unzip -q {fname}\")\n",
"print(\"\\n=== AudioSet subset ===\")\n",
"audioset_dir = Path(\"audioset\"); audioset_dir.mkdir(exist_ok=True)\n",
"audioset_out = Path(\"audioset_16k\"); audioset_out.mkdir(exist_ok=True)\n",
"\n",
"output_dir = \"./fma_16k\"\n",
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
"links = [f\"https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main/data/bal_train0{i}.tar\"\n",
" for i in range(10)]\n",
"for link in links:\n",
" fname = link.split(\"/\")[-1]\n",
" out_tar = audioset_dir / fname\n",
" if not out_tar.exists():\n",
" print(f\"⬇️ {fname}\")\n",
" os.system(f\"wget -q -O '{out_tar}' '{link}'\")\n",
" print(f\"📦 Extract {fname}\")\n",
" os.system(f\"tar -xf '{out_tar}' -C '{audioset_dir}'\")\n",
"\n",
"# Save clips to 16-bit PCM wav files\n",
"fma_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n",
"print(f\"Number of MP3 files found: {len(fma_files)}\")\n",
"if fma_files:\n",
" fma_dataset = Dataset.from_dict({\"audio\": [str(file) for file in fma_files]})\n",
" fma_dataset = fma_dataset.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
"flacs = list(audioset_dir.rglob(\"*.flac\"))\n",
"print(f\"🔎 FLAC files: {len(flacs)}\")\n",
"corrupt = []\n",
"for p in tqdm(flacs, desc=\"AudioSet→WAV (no resample)\"):\n",
" try:\n",
" a, sr = sf.read(p, always_2d=False)\n",
" if a is None or len(a) == 0:\n",
" raise ValueError(\"empty audio\")\n",
" if a.ndim > 1:\n",
" a = a[:,0]\n",
" # Apple behavior: write as 16-bit and label 16 kHz (no resample)\n",
" write_wav(audioset_out / (p.stem + \".wav\"), a, 16000)\n",
" except Exception as e:\n",
" corrupt.append(f\"{p}:{e}\")\n",
"if corrupt:\n",
" (audioset_out / \"audioset_corrupted_files.log\").write_text(\"\\n\".join(corrupt))\n",
"print(\"✅ AudioSet processing complete!\")\n",
"\n",
" corrupted_files = []\n",
" print(\"Converting FMA files to 16kHz WAV...\")\n",
" for row in tqdm(fma_dataset):\n",
" try:\n",
" name = row[\"audio\"][\"path\"].split(\"/\")[-1].replace(\".mp3\", \".wav\")\n",
" scipy.io.wavfile.write(\n",
" os.path.join(output_dir, name), \n",
" 16000, \n",
" (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
" )\n",
" except Exception as e:\n",
" print(f\"Error converting {row['audio']['path']}: {e}\")\n",
" corrupted_files.append(row[\"audio\"][\"path\"])\n",
"# -----------------------------\n",
"# FMA xsmall (resample to 16 kHz mono)\n",
"# -----------------------------\n",
"print(\"\\n=== FMA xsmall ===\")\n",
"fma_zip_dir = Path(\"fma\"); fma_zip_dir.mkdir(exist_ok=True)\n",
"fma_out = Path(\"fma_16k\"); fma_out.mkdir(exist_ok=True)\n",
"\n",
" if corrupted_files:\n",
" with open(\"fma_corrupted_files.log\", \"w\") as log_file:\n",
" log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
"else:\n",
" print(\"No MP3 files found in FMA.\")\n",
"zipname = \"fma_xs.zip\"\n",
"zipurl = f\"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/{zipname}\"\n",
"zipout = fma_zip_dir / zipname\n",
"if not zipout.exists():\n",
" os.system(f\"wget -q -O '{zipout}' '{zipurl}'\")\n",
" os.system(f\"cd fma && unzip -q '{zipname}'\")\n",
"\n",
"print(\"Dataset preparation complete!\")"
"mp3s = list(Path(\"fma/fma_small\").rglob(\"*.mp3\"))\n",
"print(f\"🎵 FMA mp3 count: {len(mp3s)}\")\n",
"corrupt = []\n",
"for p in tqdm(mp3s, desc=\"FMA→16k WAV\"):\n",
" try:\n",
" y, sr = librosa.load(p, sr=16000, mono=True) # proper decode+resample\n",
" if y.size == 0:\n",
" raise ValueError(\"empty audio\")\n",
" write_wav(fma_out / (p.stem + \".wav\"), y, 16000)\n",
" except Exception as e:\n",
" corrupt.append(f\"{p}:{e}\")\n",
"if corrupt:\n",
" Path(\"fma_corrupted_files.log\").write_text(\"\\n\".join(corrupt))\n",
"print(\"\\n✅ Dataset prep complete!\")"
]
},
{
@@ -453,29 +448,41 @@
},
"outputs": [],
"source": [
"# Augment a random clip and play it back to verify it works well\n",
"# Augment a random generated-sample WAV and play it back (pass ndarray to augmenter)\n",
"from pathlib import Path\n",
"from IPython.display import Audio\n",
"from microwakeword.audio.audio_utils import save_clip\n",
"from IPython.display import Audio, display\n",
"import numpy as np\n",
"import soundfile as sf\n",
"import librosa, random, glob\n",
"\n",
"# Ensure output directory exists\n",
"output_dir = Path('./augmented_clips')\n",
"output_dir = Path(\"./augmented_clips\")\n",
"output_dir.mkdir(exist_ok=True)\n",
"\n",
"# 1) Pick a random WAV from the Piper outputs\n",
"candidates = glob.glob(\"generated_samples/*.wav\")\n",
"if not candidates:\n",
" raise SystemExit(\"No files in generated_samples/. Run the TTS sample cell first.\")\n",
"src_path = random.choice(candidates)\n",
"\n",
"# 2) Load as 16 kHz mono float32\n",
"y, sr = librosa.load(src_path, sr=16000, mono=True)\n",
"y = y.astype(np.float32, copy=False)\n",
"\n",
"# 3) Augment — microwakeword Augmentation expects a 1-D numpy array\n",
"try:\n",
" # Get a random clip and apply augmentation\n",
" random_clip = clips.get_random_clip()\n",
" augmented_clip = augmenter.augment_clip(random_clip)\n",
" \n",
" # Save augmented clip to file\n",
" output_file = output_dir / 'augmented_clip.wav'\n",
" save_clip(augmented_clip, output_file)\n",
" print(f\"Augmented clip saved to {output_file}\")\n",
" \n",
" # Playback augmented clip\n",
" display(Audio(str(output_file), autoplay=True))\n",
" y_aug = augmenter.augment_clip(y)\n",
"except Exception as e:\n",
" print(f\"Error during augmentation or playback: {e}\")"
" # some versions accept (samples, sr) — try that as a fallback\n",
" try:\n",
" y_aug = augmenter.augment_clip((y, sr))\n",
" except Exception:\n",
" raise\n",
"\n",
"# 4) Save and play\n",
"out_path = output_dir / \"augmented_clip.wav\"\n",
"sf.write(str(out_path), y_aug.astype(np.float32, copy=False), sr, subtype=\"PCM_16\")\n",
"print(f\"Augmented clip saved to {out_path}\")\n",
"display(Audio(str(out_path), autoplay=True))"
]
},
{
@@ -487,53 +494,96 @@
"outputs": [],
"source": [
"# Augment samples and save the training, validation, and testing sets.\n",
"# Validating and testing samples generated the same way can make the model\n",
"# benchmark better than it performs in real-word use. Use real samples or TTS\n",
"# samples generated with a different TTS engine to potentially get more accurate\n",
"# benchmarks.\n",
"# This version avoids datasets.Audio entirely by driving Clips from local WAVs.\n",
"\n",
"import os\n",
"import os, glob, random\n",
"from pathlib import Path\n",
"import types\n",
"import numpy as np\n",
"import librosa\n",
"from mmap_ninja.ragged import RaggedMmap\n",
"from microwakeword.audio.spectrograms import SpectrogramGeneration\n",
"\n",
"# Output directory for augmented features\n",
"output_dir = 'generated_augmented_features'\n",
"os.makedirs(output_dir, exist_ok=True)\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",
" \"\"\"\n",
" Yield 1-D float32 arrays loaded via librosa from generated_samples/*.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",
" if not files:\n",
" raise SystemExit(\"❌ No WAVs in generated_samples/. Generate TTS samples first.\")\n",
"\n",
"# Configuration for each split\n",
"split_config = {\n",
" \"training\": {\"name\": \"train\", \"repetition\": 2, \"slide_frames\": 10},\n",
" rng = random.Random(10) # deterministic shuffling like Clips(random_split_seed=10)\n",
" files_shuf = files[:]\n",
" rng.shuffle(files_shuf)\n",
"\n",
" n = len(files_shuf)\n",
" n_val = max(1, int(0.10 * n))\n",
" n_test = max(1, int(0.10 * n))\n",
" n_train = max(0, n - n_val - n_test)\n",
" splits = {\n",
" \"train\": files_shuf[:n_train],\n",
" \"validation\": files_shuf[n_train:n_train + n_val],\n",
" \"test\": files_shuf[n_train + n_val:],\n",
" }\n",
" file_list = splits.get(split, [])\n",
" if not file_list:\n",
" return # nothing to yield\n",
"\n",
" for _ in range(max(1, int(repeat))):\n",
" for p in file_list:\n",
" 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",
"\n",
"# ---- Validate augmentation asset folders exist ----\n",
"def validate(paths):\n",
" for p in paths:\n",
" if not Path(p).exists():\n",
" raise SystemExit(f\"❌ Missing directory: {p}. Run dataset prep first.\")\n",
"\n",
"impulse_paths = [\"mit_rirs\"]\n",
"background_paths = [\"fma_16k\", \"audioset_16k\"]\n",
"validate(impulse_paths + background_paths)\n",
"\n",
"# ---- Output root ----\n",
"out_root = Path(\"generated_augmented_features\")\n",
"out_root.mkdir(exist_ok=True)\n",
"\n",
"# ---- Split config (same as before) ----\n",
"split_cfg = {\n",
" \"training\": {\"name\": \"train\", \"repetition\": 2, \"slide_frames\": 10},\n",
" \"validation\": {\"name\": \"validation\", \"repetition\": 1, \"slide_frames\": 10},\n",
" \"testing\": {\"name\": \"test\", \"repetition\": 1, \"slide_frames\": 1},\n",
" \"testing\": {\"name\": \"test\", \"repetition\": 1, \"slide_frames\": 1},\n",
"}\n",
"\n",
"# Generate augmented features for each split\n",
"for split, config in split_config.items():\n",
" out_dir = os.path.join(output_dir, split)\n",
" os.makedirs(out_dir, exist_ok=True)\n",
" print(f\"Processing {split} set...\")\n",
"# ---- Generate features ----\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",
"\n",
" try:\n",
" # Spectrogram generation configuration\n",
" spectrograms = SpectrogramGeneration(\n",
" clips=clips,\n",
" augmenter=augmenter,\n",
" slide_frames=config[\"slide_frames\"],\n",
" step_ms=10, # Can parameterize this if needed\n",
" )\n",
" spectros = SpectrogramGeneration(\n",
" clips=clips, # now backed by our WAV loader\n",
" augmenter=augmenter, # your existing augmenter\n",
" slide_frames=cfg[\"slide_frames\"],\n",
" step_ms=10,\n",
" )\n",
"\n",
" # Generate and save spectrogram features\n",
" RaggedMmap.from_generator(\n",
" out_dir=os.path.join(out_dir, 'wakeword_mmap'),\n",
" sample_generator=spectrograms.spectrogram_generator(\n",
" split=config[\"name\"], repeat=config[\"repetition\"]\n",
" ),\n",
" batch_size=100, # Can parameterize this if needed\n",
" verbose=True,\n",
" )\n",
" print(f\"Completed processing {split} set. Output saved to {out_dir}\")\n",
" except Exception as e:\n",
" print(f\"Error processing {split} set: {e}\")"
" RaggedMmap.from_generator(\n",
" out_dir=str(out_dir / \"wakeword_mmap\"),\n",
" sample_generator=spectros.spectrogram_generator(\n",
" split=cfg[\"name\"], repeat=cfg[\"repetition\"]\n",
" ),\n",
" batch_size=100,\n",
" verbose=True,\n",
" )\n",
"\n",
"print(\"✅ Features ready (generated_augmented_features/*/wakeword_mmap)\")"
]
},
{
@@ -808,7 +858,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
"version": "3.10.12"
}
},
"nbformat": 4,

9
requirements.txt
View File

@@ -1,6 +1,11 @@
# --- Core training (Microwakeword) ---
torch==2.5.1
torchaudio==2.5.1
tensorboard==2.18.0
tensorboard-data-server==0.7.2
tensorflow==2.18.0
tensorflow-estimator==2.13.0
tensorflow-io-gcs-filesystem==0.37.1
numpy==1.26.4
scipy==1.12.0
librosa==0.10.2.post1
@@ -13,16 +18,16 @@ scikit-learn==1.6.0
numba==0.60.0
joblib==1.4.2
pandas==2.2.3
# feature extractors + metadata helpers your repo uses
pymicro_features @ git+https://github.com/puddly/pymicro-features@e1d3f88183e12bb8af2df9e399ea157af7393762
audio-metadata @ git+https://github.com/whatsnowplaying/audio-metadata@d4ebb238e6a401bb1a5aaaac60c9e2b3cb30929f
bitstruct==8.19.0
# --- Piper sample generation ---
piper-tts>=1.2.0
onnxruntime-gpu>=1.16.0
piper-phonemize-cross==1.2.1
# --- Notebook / tooling (keep light) ---
# --- Notebook / tooling ---
ipykernel==6.29.5
jupyterlab==4.3.4
ipywidgets==8.1.5