taco/microWakeWord-Trainer-Nvidia-Docker
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piper speaking speeds

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MasterPhooey
2025-12-22 19:44:09 -06:00
parent 99eaaa39cd
commit 1c954eb253
1 changed files with 74 additions and 55 deletions
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microWakeWord_training_notebook.ipynb
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@@ -232,7 +232,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# Generate a large number of wake word samples for training\n", "# Generate a large number of wake word samples for training (with length-scale sweep)\n",
"import sys, subprocess\n", "import sys, subprocess\n",
"from pathlib import Path\n", "from pathlib import Path\n",
"\n", "\n",
@@ -240,14 +240,21 @@
"MODELS_DIR = REPO_DIR / \"models\"\n", "MODELS_DIR = REPO_DIR / \"models\"\n",
"MODEL_NAME = \"en_US-libritts_r-medium.pt\"\n", "MODEL_NAME = \"en_US-libritts_r-medium.pt\"\n",
"\n", "\n",
"MAX_SAMPLES = 50000\n",
"BATCH_SIZE = 100\n",
"\n",
"# Piper \"speed\" control via piper-sample-generator is length_scale(s)\n",
"LENGTH_SCALES = [\"0.85\", \"0.95\", \"1.00\", \"1.05\", \"1.15\"]\n",
"\n",
"cmd = [\n", "cmd = [\n",
" sys.executable,\n", " sys.executable,\n",
" str(REPO_DIR / \"generate_samples.py\"),\n", " str(REPO_DIR / \"generate_samples.py\"),\n",
" TARGET_WORD,\n", " TARGET_WORD,\n",
" \"--model\", str(MODELS_DIR / MODEL_NAME),\n", " \"--model\", str(MODELS_DIR / MODEL_NAME),\n",
" \"--max-samples\", \"50000\",\n", " \"--max-samples\", str(MAX_SAMPLES),\n",
" \"--batch-size\", \"100\",\n", " \"--batch-size\", str(BATCH_SIZE),\n",
" \"--output-dir\", \"generated_samples\",\n", " \"--output-dir\", \"generated_samples\",\n",
" \"--length-scales\", *LENGTH_SCALES,\n",
"]\n", "]\n",
"\n", "\n",
"print(\"→\", \" \".join(cmd))\n", "print(\"→\", \" \".join(cmd))\n",
@@ -742,9 +749,36 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# GPU memory config (set env BEFORE importing TF)\n",
"import os, sys, gc\n",
"\n",
"if \"tensorflow\" not in sys.modules:\n",
" os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\" # grow as needed\n",
" os.environ[\"TF_GPU_ALLOCATOR\"] = \"cuda_malloc_async\" # modern CUDA allocator\n",
" os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_cuda_data_dir=/usr/local/cuda\"\n",
" os.environ[\"TF_XLA_FLAGS\"] = \"--tf_xla_auto_jit=0\" # disable XLA JIT (more stable mem)\n",
"import tensorflow as tf\n",
"\n",
"# Per-device memory growth (belt + suspenders)\n",
"for g in tf.config.list_physical_devices(\"GPU\"):\n",
" try:\n",
" tf.config.experimental.set_memory_growth(g, True)\n",
" except Exception:\n",
" pass\n",
"print(\"GPUs:\", tf.config.list_physical_devices(\"GPU\"))\n",
"gc.collect()\n",
"\n",
"# Optional but recommended: mixed precision halves activation memory\n",
"try:\n",
" from tensorflow.keras import mixed_precision\n",
" mixed_precision.set_global_policy(\"mixed_float16\")\n",
" print(\"Mixed precision policy:\", mixed_precision.global_policy())\n",
"except Exception as e:\n",
" print(\"Mixed precision not enabled:\", e)\n",
"\n",
"# --- Save a yaml config that controls the training process ---\n", "# --- Save a yaml config that controls the training process ---\n",
"\n", "\n",
"import os, sys, yaml\n", "import yaml\n",
"\n", "\n",
"config = {}\n", "config = {}\n",
"\n", "\n",
@@ -782,7 +816,7 @@
"with open(\"training_parameters.yaml\", \"w\") as f:\n", "with open(\"training_parameters.yaml\", \"w\") as f:\n",
" yaml.dump(config, f)\n", " yaml.dump(config, f)\n",
"\n", "\n",
"print(\"✅ Wrote training_parameters.yaml (batch_size=16)\")" "print(\"✅ Wrote training_parameters.yaml (batch_size=16) with allow_growth, cuda_malloc_async, XLA JIT OFF, mixed precision ON.\")"
] ]
}, },
{ {
@@ -795,59 +829,44 @@
"source": [ "source": [
"# Train + export (GPU-friendly env + stable flags)\n", "# Train + export (GPU-friendly env + stable flags)\n",
"\n", "\n",
"import os, sys, gc, runpy\n", "import os, sys\n",
"\n",
"# --- Runtime env (inherited by the subprocess we're about to launch) ---\n",
"os.environ.setdefault(\"LD_LIBRARY_PATH\",\n",
" \"/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/lib/x86_64-linux-gnu:\" +\n",
" os.environ.get(\"LD_LIBRARY_PATH\",\"\")\n",
")\n",
"os.environ.setdefault(\"TF_CPP_MIN_LOG_LEVEL\", \"2\") # quieter logs\n",
"os.environ.setdefault(\"TF_FORCE_GPU_ALLOW_GROWTH\", \"true\") # grow VRAM as needed\n",
"os.environ.setdefault(\"TF_GPU_ALLOCATOR\", \"cuda_malloc_async\")# modern allocator\n",
"os.environ.setdefault(\"XLA_FLAGS\", \"--xla_gpu_cuda_data_dir=/usr/local/cuda\")\n",
"os.environ.setdefault(\"TF_XLA_FLAGS\", \"--tf_xla_auto_jit=0\") # disable XLA JIT (more stable)\n",
"os.environ.setdefault(\"NVIDIA_TF32_OVERRIDE\", \"1\") # allow TF32 (perf/VRAM win on Ampere+)\n",
"\n", "\n",
"if \"tensorflow\" not in sys.modules:\n",
" os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\" # grow as needed\n",
" os.environ[\"TF_GPU_ALLOCATOR\"] = \"cuda_malloc_async\" # modern CUDA allocator\n",
" os.environ[\"TF_XLA_FLAGS\"] = \"--tf_xla_auto_jit=0\" # disable XLA JIT (more stable mem)\n",
" os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\" # quieter logs\n",
" os.environ[\"NVIDIA_TF32_OVERRIDE\"] = \"1\" # allow TF32 (perf/VRAM win on Ampere+)\n",
"# If you still hit GPU memory errors, uncomment to force a smaller workspace:\n", "# If you still hit GPU memory errors, uncomment to force a smaller workspace:\n",
"# os.environ[\"TF_CUDNN_WORKSPACE_LIMIT_IN_MB\"] = \"256\"\n", "# os.environ[\"TF_CUDNN_WORKSPACE_LIMIT_IN_MB\"] = \"256\"\n",
"\n", "\n",
"import tensorflow as tf\n", "# --- Kick off training ---\n",
"\n", "cmd = f'''\"{sys.executable}\" -m microwakeword.model_train_eval \\\n",
"allow_growth = \"\"\n", " --training_config=\"training_parameters.yaml\" \\\n",
"# Per-device memory growth (belt + suspenders)\n", " --train 1 \\\n",
"for g in tf.config.list_physical_devices(\"GPU\"):\n", " --restore_checkpoint 1 \\\n",
" try:\n", " --test_tf_nonstreaming 0 \\\n",
" tf.config.experimental.set_memory_growth(g, True)\n", " --test_tflite_nonstreaming 0 \\\n",
" allow_growth = \"gpu_allow_growth, \"\n", " --test_tflite_nonstreaming_quantized 0 \\\n",
" except Exception:\n", " --test_tflite_streaming 0 \\\n",
" pass\n", " --test_tflite_streaming_quantized 1 \\\n",
"print(\"GPUs:\", tf.config.list_physical_devices(\"GPU\"))\n", " --use_weights \"best_weights\" \\\n",
"gc.collect()\n", " mixednet \\\n",
"\n", " --pointwise_filters \"64,64,64,64\" \\\n",
"print(f\"✅ Set environment with {allow_growth}cuda_malloc_async, xla_auto_jit=0, min_log_level=2, nvidia_tf2_override\")\n", " --repeat_in_block \"1,1,1,1\" \\\n",
"print(\" Starting training...\")\n", " --mixconv_kernel_sizes \"[5], [7,11], [9,15], [23]\" \\\n",
"\n", " --residual_connection \"0,0,0,0\" \\\n",
"original_argv = list(sys.argv)\n", " --first_conv_filters 32 \\\n",
"try:\n", " --first_conv_kernel_size 5 \\\n",
" sys.argv = [\n", " --stride 2'''\n",
" 'model_train_eval.py',\n", "print(\"Running:\\n\", cmd)\n",
" '--training_config', 'training_parameters.yaml',\n", "!$cmd"
" '--train', '1',\n",
" '--restore_checkpoint', '1',\n",
" '--test_tf_nonstreaming', '0',\n",
" '--test_tflite_nonstreaming', '0',\n",
" '--test_tflite_nonstreaming_quantized', '0',\n",
" '--test_tflite_streaming', '0',\n",
" '--test_tflite_streaming_quantized', '1',\n",
" '--use_weights', 'best_weights',\n",
" 'mixednet',\n",
" '--pointwise_filters', '64,64,64,64',\n",
" '--repeat_in_block', '1,1,1,1',\n",
" '--mixconv_kernel_sizes', '[5], [7,11], [9,15], [23]',\n",
" '--residual_connection', '0,0,0,0',\n",
" '--first_conv_filters', '32',\n",
" '--first_conv_kernel_size', '5',\n",
" '--stride', '2'\n",
" ]\n",
" runpy.run_module(\"microwakeword.model_train_eval\", run_name=\"__main__\", alter_sys=True)\n",
"finally:\n",
" sys.argv = original_argv\n",
"print(\"✅ Training and testing complete.\")\n"
] ]
}, },
{ {