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https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git
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Update advanced_training_notebook.ipynb
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MasterPhooey
@@ -144,141 +144,129 @@
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"# licenses and usage restrictions. As such, any custom models trained with this\n",
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"# licenses and usage restrictions. As such, any custom models trained with this\n",
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"# data should be considered as appropriate for **non-commercial** personal use only.\n",
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"# data should be considered as appropriate for **non-commercial** personal use only.\n",
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"\n",
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"\n",
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"import datasets\n",
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"import scipy\n",
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"import os\n",
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"import os\n",
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"import requests\n",
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"import scipy.io.wavfile\n",
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"\n",
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"import numpy as np\n",
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"import numpy as np\n",
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"\n",
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"from datasets import Dataset, Audio, load_dataset\n",
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"from pathlib import Path\n",
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"from pathlib import Path\n",
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"from tqdm import tqdm\n",
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"from tqdm import tqdm\n",
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"\n",
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"\n",
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"## Download MIR RIR data\n",
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"# -----------------------------\n",
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"\n",
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"# Download and Process MIT RIR\n",
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"# -----------------------------\n",
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"output_dir = \"./mit_rirs\"\n",
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"output_dir = \"./mit_rirs\"\n",
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"if not os.path.exists(output_dir):\n",
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"if not os.path.exists(output_dir):\n",
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" os.mkdir(output_dir)\n",
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" os.mkdir(output_dir)\n",
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" rir_dataset = datasets.load_dataset(\"davidscripka/MIT_environmental_impulse_responses\", split=\"train\", streaming=True)\n",
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" rir_dataset = load_dataset(\"davidscripka/MIT_environmental_impulse_responses\", split=\"train\", streaming=True)\n",
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" # Save clips to 16-bit PCM wav files\n",
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" print(f\"Downloading MIT RIR dataset to {output_dir}...\")\n",
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" for row in tqdm(rir_dataset):\n",
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" for row in tqdm(rir_dataset):\n",
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" name = row['audio']['path'].split('/')[-1]\n",
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" name = row[\"audio\"][\"path\"].split(\"/\")[-1]\n",
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" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, (row['audio']['array'] * 32767).astype(np.int16))\n",
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" scipy.io.wavfile.write(\n",
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" os.path.join(output_dir, name), \n",
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" 16000, \n",
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" (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
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" )\n",
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" print(f\"Finished downloading MIT RIR dataset to {output_dir}.\\n\")\n",
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"else:\n",
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" print(f\"{output_dir} already exists. Skipping download.\")\n",
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"\n",
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"\n",
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"## Download noise and background audio\n",
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"# -----------------------------\n",
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"# Download and Process Audioset\n",
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"# -----------------------------\n",
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"audioset_dir = \"./audioset\"\n",
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"if not os.path.exists(audioset_dir):\n",
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" os.mkdir(audioset_dir)\n",
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"\n",
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"\n",
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"# Full-scale dataset download links\n",
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"# Full-scale dataset download links\n",
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"dataset_links = [\n",
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"dataset_links = [\n",
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" f\"https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main/data/bal_train0{i}.tar\"\n",
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" f\"https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main/data/bal_train0{i}.tar\"\n",
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" for i in range(10) # Adjust for additional parts\n",
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" for i in range(10)\n",
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"]\n",
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"]\n",
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"\n",
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"\n",
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"if not os.path.exists(\"audioset\"):\n",
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" os.mkdir(\"audioset\")\n",
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"\n",
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"for link in dataset_links:\n",
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"for link in dataset_links:\n",
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" fname = link.split(\"/\")[-1]\n",
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" file_name = link.split(\"/\")[-1]\n",
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" out_dir = f\"audioset/{fname}\"\n",
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" out_dir = os.path.join(audioset_dir, file_name)\n",
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" if not os.path.exists(out_dir):\n",
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" if not os.path.exists(out_dir):\n",
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" print(f\"Downloading {fname}...\")\n",
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" print(f\"Downloading {file_name}...\")\n",
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" response = requests.get(link, stream=True)\n",
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" os.system(f\"wget --quiet -O {out_dir} {link}\")\n",
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" with open(out_dir, \"wb\") as f:\n",
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" print(f\"Extracting {file_name}...\")\n",
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" for chunk in response.iter_content(chunk_size=1024):\n",
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" os.system(f\"cd {audioset_dir} && tar -xf {file_name}\")\n",
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" if chunk:\n",
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" f.write(chunk)\n",
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" print(f\"Downloaded {fname}\")\n",
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"\n",
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" # Extract the tar file\n",
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" print(f\"Extracting {fname}...\")\n",
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" os.system(f\"cd audioset && tar -xf {fname}\")\n",
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"\n",
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"\n",
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"output_dir = \"./audioset_16k\"\n",
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"output_dir = \"./audioset_16k\"\n",
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"if not os.path.exists(output_dir):\n",
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"if not os.path.exists(output_dir):\n",
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" os.mkdir(output_dir)\n",
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" os.mkdir(output_dir)\n",
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"\n",
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"\n",
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"# Save clips to 16-bit PCM wav files\n",
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"# Save clips to 16-bit PCM wav files\n",
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" audio_files = list(Path(\"audioset/audio\").glob(\"**/*.flac\"))\n",
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"audioset_files = list(Path(\"audioset/audio\").glob(\"**/*.flac\"))\n",
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" print(f\"Number of FLAC files found: {len(audio_files)}\")\n",
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"print(f\"Number of FLAC files found: {len(audioset_files)}\")\n",
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" if not audio_files:\n",
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"if audioset_files:\n",
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" raise FileNotFoundError(\"No .flac files found in the audioset directory. Check your dataset extraction.\")\n",
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" audioset_dataset = Dataset.from_dict({\"audio\": [str(file) for file in audioset_files]})\n",
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" audioset_dataset = audioset_dataset.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
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"\n",
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"\n",
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" print(\"Converting audioset files to 16kHz WAV...\")\n",
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" corrupted_files = []\n",
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" for file_path in tqdm(audio_files):\n",
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" print(\"Converting Audioset files to 16kHz WAV...\")\n",
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" for row in tqdm(audioset_dataset):\n",
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" try:\n",
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" try:\n",
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" # Read and convert the .flac file\n",
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" name = row[\"audio\"][\"path\"].split(\"/\")[-1].replace(\".flac\", \".wav\")\n",
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" data, samplerate = scipy.io.wavfile.read(file_path)\n",
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" if samplerate != 16000:\n",
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" data = np.interp(\n",
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" np.linspace(0, len(data), int(len(data) * 16000 / samplerate)),\n",
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" np.arange(len(data)),\n",
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" data,\n",
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" )\n",
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"\n",
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" # Save as WAV\n",
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" output_path = Path(output_dir) / file_path.name.replace(\".flac\", \".wav\")\n",
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" scipy.io.wavfile.write(\n",
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" scipy.io.wavfile.write(\n",
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" output_path,\n",
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" os.path.join(output_dir, name), \n",
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" 16000, \n",
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" 16000, \n",
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" (data * 32767).astype(np.int16),\n",
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" (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
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" )\n",
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" )\n",
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" except Exception as e:\n",
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" except Exception as e:\n",
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" print(f\"Error converting {file_path}: {e}\")\n",
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" print(f\"Error converting {row['audio']['path']}: {e}\")\n",
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" continue # Skip and proceed with the next file\n",
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" corrupted_files.append(row[\"audio\"][\"path\"])\n",
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"\n",
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"\n",
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"# Free Music Archive dataset\n",
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" if corrupted_files:\n",
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"# https://github.com/mdeff/fma\n",
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" with open(\"audioset_corrupted_files.log\", \"w\") as log_file:\n",
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"# (Third-party mchl914 extra small set)\n",
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" log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
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"else:\n",
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" print(\"No FLAC files found in Audioset.\")\n",
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"\n",
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"\n",
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"# -----------------------------\n",
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"# Download and Process FMA\n",
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"# -----------------------------\n",
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"output_dir = \"./fma\"\n",
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"output_dir = \"./fma\"\n",
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"if not os.path.exists(output_dir):\n",
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"if not os.path.exists(output_dir):\n",
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" os.mkdir(output_dir)\n",
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" os.mkdir(output_dir)\n",
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" fname = \"fma_xs.zip\"\n",
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" fname = \"fma_xs.zip\"\n",
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" link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n",
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" link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n",
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" out_dir = f\"fma/{fname}\"\n",
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" out_dir = os.path.join(output_dir, fname)\n",
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" print(\"Downloading fma_xs dataset...\")\n",
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" os.system(f\"wget -O {out_dir} {link}\")\n",
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" response = requests.get(link, stream=True)\n",
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" os.system(f\"cd {output_dir} && unzip -q {fname}\")\n",
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" with open(out_dir, \"wb\") as f:\n",
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" for chunk in response.iter_content(chunk_size=1024):\n",
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" if chunk:\n",
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" f.write(chunk)\n",
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" print(\"Downloaded fma_xs dataset.\")\n",
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"\n",
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" os.system(f\"cd fma && unzip -q {fname}\")\n",
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"\n",
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"\n",
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"output_dir = \"./fma_16k\"\n",
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"output_dir = \"./fma_16k\"\n",
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"if not os.path.exists(output_dir):\n",
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"if not os.path.exists(output_dir):\n",
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" os.mkdir(output_dir)\n",
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" os.mkdir(output_dir)\n",
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"\n",
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"\n",
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"# Save clips to 16-bit PCM wav files\n",
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"# Save clips to 16-bit PCM wav files\n",
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" audio_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n",
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"fma_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n",
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" print(f\"Number of MP3 files found: {len(audio_files)}\")\n",
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"print(f\"Number of MP3 files found: {len(fma_files)}\")\n",
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" if not audio_files:\n",
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"if fma_files:\n",
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" raise FileNotFoundError(\"No .mp3 files found in the fma directory. Check your dataset extraction.\")\n",
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" fma_dataset = Dataset.from_dict({\"audio\": [str(file) for file in fma_files]})\n",
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" fma_dataset = fma_dataset.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
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"\n",
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"\n",
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" print(\"Converting fma_xs files to 16kHz WAV...\")\n",
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" corrupted_files = []\n",
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" for file_path in tqdm(audio_files):\n",
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" print(\"Converting FMA files to 16kHz WAV...\")\n",
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" for row in tqdm(fma_dataset):\n",
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" try:\n",
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" try:\n",
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" # Read and convert the .mp3 file\n",
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" name = row[\"audio\"][\"path\"].split(\"/\")[-1].replace(\".mp3\", \".wav\")\n",
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" data, samplerate = scipy.io.wavfile.read(file_path)\n",
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" if samplerate != 16000:\n",
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" data = np.interp(\n",
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" np.linspace(0, len(data), int(len(data) * 16000 / samplerate)),\n",
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" np.arange(len(data)),\n",
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" data,\n",
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" )\n",
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"\n",
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" # Save as WAV\n",
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" output_path = Path(output_dir) / file_path.name.replace(\".mp3\", \".wav\")\n",
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" scipy.io.wavfile.write(\n",
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" scipy.io.wavfile.write(\n",
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" output_path,\n",
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" os.path.join(output_dir, name), \n",
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" 16000, \n",
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" 16000, \n",
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" (data * 32767).astype(np.int16),\n",
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" (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
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" )\n",
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" )\n",
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" except Exception as e:\n",
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" except Exception as e:\n",
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" print(f\"Error converting {file_path}: {e}\")\n",
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" print(f\"Error converting {row['audio']['path']}: {e}\")\n",
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" continue # Skip and proceed with the next file\n",
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" corrupted_files.append(row[\"audio\"][\"path\"])\n",
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"\n",
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"\n",
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"print(\"All datasets processed successfully!\")"
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" if corrupted_files:\n",
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" with open(\"fma_corrupted_files.log\", \"w\") as log_file:\n",
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" log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
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"else:\n",
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" print(\"No MP3 files found in FMA.\")\n",
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"\n",
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"print(\"Dataset preparation complete!\")"
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]
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]
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},
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},
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{
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{
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