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Update advanced_training_notebook.ipynb

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MasterPhooey
2025-01-19 15:45:38 -06:00
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parent f2368903d2
commit 4d25fc3051
1 changed files with 34 additions and 42 deletions
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advanced_training_notebook.ipynb
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@@ -124,7 +124,7 @@
"# wake word samples, possibly with different phonetic pronunciations.\n", "# wake word samples, possibly with different phonetic pronunciations.\n",
"\n", "\n",
"!\"{sys.executable}\" piper-sample-generator/generate_samples.py \"{target_word}\" \\\n", "!\"{sys.executable}\" piper-sample-generator/generate_samples.py \"{target_word}\" \\\n",
"--max-samples 10000 \\\n", "--max-samples 50000 \\\n",
"--batch-size 100 \\\n", "--batch-size 100 \\\n",
"--output-dir generated_samples" "--output-dir generated_samples"
] ]
@@ -246,13 +246,12 @@
" fname = \"fma_xs.zip\"\n", " fname = \"fma_xs.zip\"\n",
" link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n", " link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n",
" out_dir = os.path.join(output_dir, fname)\n", " out_dir = os.path.join(output_dir, fname)\n",
" print(f\"Downloading {fname}...\")\n", " os.system(f\"wget -O {out_dir} {link}\")\n",
" os.system(f\"wget -q -O {out_dir} {link}\")\n", " os.system(f\"cd {output_dir} && unzip -q {fname}\")\n",
" print(f\"Extracting {fname}...\")\n",
" os.system(f\"unzip -q -o {out_dir} -d {output_dir}\")\n",
"\n", "\n",
"output_dir = \"./fma_16k\"\n", "output_dir = \"./fma_16k\"\n",
"os.makedirs(output_dir, exist_ok=True)\n", "if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
"\n", "\n",
"# Save clips to 16-bit PCM wav files\n", "# Save clips to 16-bit PCM wav files\n",
"fma_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n", "fma_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n",
@@ -263,32 +262,25 @@
"\n", "\n",
" corrupted_files = []\n", " corrupted_files = []\n",
" print(\"Converting FMA files to 16kHz WAV...\")\n", " print(\"Converting FMA files to 16kHz WAV...\")\n",
" for row in tqdm(fma_dataset, desc=\"Processing FMA files\", unit=\"file\"):\n", " for row in tqdm(fma_dataset):\n",
" try:\n", " try:\n",
" name = Path(row[\"audio\"][\"path\"]).stem + \".wav\"\n", " name = row[\"audio\"][\"path\"].split(\"/\")[-1].replace(\".mp3\", \".wav\")\n",
" output_path = Path(output_dir) / name\n",
"\n",
" # Check if audio data is valid before writing\n",
" if row[\"audio\"][\"array\"] is None or len(row[\"audio\"][\"array\"]) == 0:\n",
" raise ValueError(\"Empty or invalid audio data\")\n",
"\n",
" scipy.io.wavfile.write(\n", " scipy.io.wavfile.write(\n",
" output_path,\n", " os.path.join(output_dir, name), \n",
" 16000, \n", " 16000, \n",
" (row[\"audio\"][\"array\"] * 32767).astype(np.int16),\n", " (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
" )\n", " )\n",
" except Exception as e:\n", " except Exception as e:\n",
" print(f\"Error converting {row['audio']['path']}: {e}\")\n",
" corrupted_files.append(row[\"audio\"][\"path\"])\n", " corrupted_files.append(row[\"audio\"][\"path\"])\n",
"\n", "\n",
" if corrupted_files:\n", " if corrupted_files:\n",
" log_path = Path(output_dir) / \"fma_corrupted_files.log\"\n", " with open(\"fma_corrupted_files.log\", \"w\") as log_file:\n",
" with open(log_path, \"w\") as log_file:\n",
" log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n", " log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
" print(f\"Logged {len(corrupted_files)} corrupted files to {log_path}\")\n",
"else:\n", "else:\n",
" print(\"No MP3 files found in FMA.\")\n", " print(\"No MP3 files found in FMA.\")\n",
"\n", "\n",
"print(\"FMA dataset preparation complete!\")" "print(\"Dataset preparation complete!\")"
] ]
}, },
{ {
@@ -339,16 +331,16 @@
" \"PitchShift\": 0.15,\n", " \"PitchShift\": 0.15,\n",
" \"BandStopFilter\": 0.1,\n", " \"BandStopFilter\": 0.1,\n",
" \"AddColorNoise\": 0.1,\n", " \"AddColorNoise\": 0.1,\n",
" \"AddBackgroundNoise\": 0.9,\n", " \"AddBackgroundNoise\": 0.7,\n",
" \"Gain\": 0.8,\n", " \"Gain\": 0.8,\n",
" \"RIR\": 0.7,\n", " \"RIR\": 0.7,\n",
" },\n", " },\n",
" impulse_paths=impulse_paths,\n", " impulse_paths=impulse_paths,\n",
" background_paths=background_paths,\n", " background_paths=background_paths,\n",
" background_min_snr_db=-10,\n", " background_min_snr_db=5,\n",
" background_max_snr_db=15,\n", " background_max_snr_db=10,\n",
" min_jitter_s=0.15,\n", " min_jitter_s=0.2,\n",
" max_jitter_s=0.25,\n", " max_jitter_s=0.3,\n",
")\n" ")\n"
] ]
}, },
@@ -543,7 +535,7 @@
" },\n", " },\n",
" {\n", " {\n",
" \"features_dir\": \"negative_datasets/speech\",\n", " \"features_dir\": \"negative_datasets/speech\",\n",
" \"sampling_weight\": 10.0, # Adjusted\n", " \"sampling_weight\": 12.0, # Adjusted\n",
" \"penalty_weight\": 1.0,\n", " \"penalty_weight\": 1.0,\n",
" \"truth\": False,\n", " \"truth\": False,\n",
" \"truncation_strategy\": \"random\",\n", " \"truncation_strategy\": \"random\",\n",
@@ -551,7 +543,7 @@
" },\n", " },\n",
" {\n", " {\n",
" \"features_dir\": \"negative_datasets/dinner_party\",\n", " \"features_dir\": \"negative_datasets/dinner_party\",\n",
" \"sampling_weight\": 10.0, # Adjusted\n", " \"sampling_weight\": 12.0, # Adjusted\n",
" \"penalty_weight\": 1.0,\n", " \"penalty_weight\": 1.0,\n",
" \"truth\": False,\n", " \"truth\": False,\n",
" \"truncation_strategy\": \"random\",\n", " \"truncation_strategy\": \"random\",\n",
@@ -559,7 +551,7 @@
" },\n", " },\n",
" {\n", " {\n",
" \"features_dir\": \"negative_datasets/no_speech\",\n", " \"features_dir\": \"negative_datasets/no_speech\",\n",
" \"sampling_weight\": 7.0, # Balanced\n", " \"sampling_weight\": 5.0, # Balanced\n",
" \"penalty_weight\": 1.0,\n", " \"penalty_weight\": 1.0,\n",
" \"truth\": False,\n", " \"truth\": False,\n",
" \"truncation_strategy\": \"random\",\n", " \"truncation_strategy\": \"random\",\n",
@@ -567,7 +559,7 @@
" },\n", " },\n",
" {\n", " {\n",
" \"features_dir\": \"negative_datasets/dinner_party_eval\",\n", " \"features_dir\": \"negative_datasets/dinner_party_eval\",\n",
" \"sampling_weight\": 8.0,\n", " \"sampling_weight\": 0.0,\n",
" \"penalty_weight\": 1.0,\n", " \"penalty_weight\": 1.0,\n",
" \"truth\": False,\n", " \"truth\": False,\n",
" \"truncation_strategy\": \"split\",\n", " \"truncation_strategy\": \"split\",\n",
@@ -575,18 +567,18 @@
" },\n", " },\n",
"]\n", "]\n",
"\n", "\n",
"config[\"training_steps\"] = [30000] # Increased\n", "config[\"training_steps\"] = [40000] # Increased\n",
"config[\"positive_class_weight\"] = [2]\n", "config[\"positive_class_weight\"] = [1]\n",
"config[\"negative_class_weight\"] = [15] # Adjusted\n", "config[\"negative_class_weight\"] = [20] # Adjusted\n",
"config[\"learning_rates\"] = [0.001, 0.0001] # Adjusted\n", "config[\"learning_rates\"] = [0.001] # Adjusted\n",
"config[\"batch_size\"] = 256\n", "config[\"batch_size\"] = 128\n",
"\n", "\n",
"config[\"time_mask_max_size\"] = [50] # Enabled SpecAugment\n", "config[\"time_mask_max_size\"] = [0] # Enabled SpecAugment\n",
"config[\"time_mask_count\"] = [2]\n", "config[\"time_mask_count\"] = [0]\n",
"config[\"freq_mask_max_size\"] = [5]\n", "config[\"freq_mask_max_size\"] = [0]\n",
"config[\"freq_mask_count\"] = [2]\n", "config[\"freq_mask_count\"] = [0]\n",
"\n", "\n",
"config[\"eval_step_interval\"] = 250 # Adjusted\n", "config[\"eval_step_interval\"] = 500 # Adjusted\n",
"config[\"clip_duration_ms\"] = 1500 # Increased\n", "config[\"clip_duration_ms\"] = 1500 # Increased\n",
"\n", "\n",
"config[\"target_minimization\"] = 0.9\n", "config[\"target_minimization\"] = 0.9\n",
@@ -632,9 +624,9 @@
"--test_tflite_streaming_quantized 1 \\\n", "--test_tflite_streaming_quantized 1 \\\n",
"--use_weights \"best_weights\" \\\n", "--use_weights \"best_weights\" \\\n",
"mixednet \\\n", "mixednet \\\n",
"--pointwise_filters \"64,64,64,64\" \\\n", "--pointwise_filters \"80,80,80,80\" \\\n",
"--repeat_in_block \"1,1,1,1\" \\\n", "--repeat_in_block \"1,1,1,1\" \\\n",
"--mixconv_kernel_sizes '[5], [7,11], [9,15], [23]' \\\n", "--mixconv_kernel_sizes '[7], [9,13], [11,17], [25]' \\\n",
"--residual_connection \"0,0,0,0\" \\\n", "--residual_connection \"0,0,0,0\" \\\n",
"--first_conv_filters 32 \\\n", "--first_conv_filters 32 \\\n",
"--first_conv_kernel_size 5 \\\n", "--first_conv_kernel_size 5 \\\n",