Update advanced_training_notebook.ipynb

advanced_training_notebook.ipynb

@@ -144,79 +144,28 @@
     "# 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",
     "\n",
+    "import datasets\n",
+    "import scipy\n",
     "import os\n",
-    "import scipy.io.wavfile\n",
+    "import requests\n",
+    "\n",
     "import numpy as np\n",
-    "import soundfile as sf\n",
+    "\n",
     "from pathlib import Path\n",
     "from tqdm import tqdm\n",
-    "import requests\n",
-    "import tarfile\n",
-    "import zipfile\n",
-    "from datasets import load_dataset\n",
     "\n",
-    "# Function to download and process RIR dataset\n",
+    "## Download MIR RIR data\n",
-    "# Function to download and process RIR dataset\n",
+    "\n",
-    "def download_rir_dataset(dataset_name, output_dir, split=\"train\"):\n",
+    "output_dir = \"./mit_rirs\"\n",
-    "    output_dir = Path(output_dir)\n",
+    "if not os.path.exists(output_dir):\n",
-    "    if not output_dir.exists():\n",
+    "    os.mkdir(output_dir)\n",
-    "        output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    rir_dataset = datasets.load_dataset(\"davidscripka/MIT_environmental_impulse_responses\", split=\"train\", streaming=True)\n",
-    "        try:\n",
+    "    # Save clips to 16-bit PCM wav files\n",
-    "            rir_dataset = load_dataset(dataset_name, split=split, streaming=True)\n",
-    "            print(f\"Downloading {dataset_name} to {output_dir}...\")\n",
     "    for row in tqdm(rir_dataset):\n",
-    "                name = Path(row['audio']['path']).name\n",
+    "        name = row['audio']['path'].split('/')[-1]\n",
-    "                # Save the original audio file\n",
+    "        scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, (row['audio']['array'] * 32767).astype(np.int16))\n",
-    "                with open(output_dir / name, \"wb\") as audio_file:\n",
-    "                    audio_file.write(row[\"audio\"][\"bytes\"])\n",
-    "            print(f\"Finished downloading {dataset_name} to {output_dir}.\\n\")\n",
-    "        except Exception as e:\n",
-    "            print(f\"Error downloading {dataset_name}: {e}\")\n",
-    "    else:\n",
-    "        print(f\"{output_dir} already exists. Skipping download.\\n\")\n",
     "\n",
-    "# Download MIT RIRs\n",
+    "## Download noise and background audio\n",
-    "download_rir_dataset(\n",
-    "    \"davidscripka/MIT_environmental_impulse_responses\",\n",
-    "    \"./mit_rirs\"\n",
-    ")\n",
-    "\n",
-    "# Function to download files\n",
-    "def download_file(url, output_path):\n",
-    "    response = requests.get(url, stream=True)\n",
-    "    total_size = int(response.headers.get('content-length', 0))\n",
-    "    with open(output_path, \"wb\") as f, tqdm(\n",
-    "        desc=f\"Downloading {output_path.name}\",\n",
-    "        total=total_size,\n",
-    "        unit=\"B\",\n",
-    "        unit_scale=True,\n",
-    "        unit_divisor=1024,\n",
-    "    ) as bar:\n",
-    "        for chunk in response.iter_content(chunk_size=1024):\n",
-    "            f.write(chunk)\n",
-    "            bar.update(len(chunk))\n",
-    "    print(f\"Downloaded {output_path}\")\n",
-    "\n",
-    "# Function to extract .tar files\n",
-    "def extract_tar(file_path, extract_dir):\n",
-    "    with tarfile.open(file_path, \"r\") as tar:\n",
-    "        tar.extractall(path=extract_dir)\n",
-    "    print(f\"Extracted {file_path} to {extract_dir}\")\n",
-    "\n",
-    "# Function to download and extract ZIP files\n",
-    "def download_and_extract_zip(url, extract_to):\n",
-    "    file_name = url.split(\"/\")[-1]\n",
-    "    local_path = Path(extract_to) / file_name\n",
-    "    download_file(url, local_path)\n",
-    "    with zipfile.ZipFile(local_path, 'r') as zip_ref:\n",
-    "        zip_ref.extractall(extract_to)\n",
-    "    print(f\"Extracted {file_name} to {extract_to}\")\n",
-    "\n",
-    "# Directories\n",
-    "raw_dir = Path(\"./audioset_raw\")\n",
-    "processed_dir = Path(\"./audioset_16k\")\n",
-    "raw_dir.mkdir(exist_ok=True)\n",
-    "processed_dir.mkdir(exist_ok=True)\n",
     "\n",
     "# Full-scale dataset download links\n",
     "dataset_links = [\n",
@@ -224,96 +173,93 @@
     "    for i in range(10)  # Adjust for additional parts\n",
     "]\n",
     "\n",
-    "# Step 1: Download all parts of the dataset\n",
+    "if not os.path.exists(\"audioset\"):\n",
-    "print(\"Downloading datasets...\")\n",
+    "    os.mkdir(\"audioset\")\n",
+    "\n",
     "    for link in dataset_links:\n",
-    "    file_name = link.split(\"/\")[-1]\n",
+    "        fname = link.split(\"/\")[-1]\n",
-    "    output_path = raw_dir / file_name\n",
+    "        out_dir = f\"audioset/{fname}\"\n",
-    "    if not output_path.exists():\n",
+    "        if not os.path.exists(out_dir):\n",
-    "        download_file(link, output_path)\n",
+    "            print(f\"Downloading {fname}...\")\n",
+    "            response = requests.get(link, stream=True)\n",
+    "            with open(out_dir, \"wb\") as f:\n",
+    "                for chunk in response.iter_content(chunk_size=1024):\n",
+    "                    if chunk:\n",
+    "                        f.write(chunk)\n",
+    "            print(f\"Downloaded {fname}\")\n",
     "\n",
-    "# Step 2: Extract all .tar files\n",
+    "        # Extract the tar file\n",
-    "print(\"Extracting datasets...\")\n",
+    "        print(f\"Extracting {fname}...\")\n",
-    "for file_path in raw_dir.glob(\"*.tar\"):\n",
+    "        os.system(f\"cd audioset && tar -xf {fname}\")\n",
-    "    extract_dir = raw_dir / file_path.stem\n",
-    "    if not extract_dir.exists():\n",
-    "        extract_tar(file_path, extract_dir)\n",
     "\n",
-    "# Step 3: Convert audio files to 16kHz WAV\n",
+    "    output_dir = \"./audioset_16k\"\n",
-    "audio_files = list(Path(raw_dir).glob(\"**/*.flac\"))\n",
+    "    if not os.path.exists(output_dir):\n",
+    "        os.mkdir(output_dir)\n",
+    "\n",
+    "    # Save clips to 16-bit PCM wav files\n",
+    "    audio_files = list(Path(\"audioset/audio\").glob(\"**/*.flac\"))\n",
     "    print(f\"Number of FLAC files found: {len(audio_files)}\")\n",
     "    if not audio_files:\n",
-    "    raise FileNotFoundError(\"No .flac files found in the raw directories. Check your dataset extraction.\")\n",
+    "        raise FileNotFoundError(\"No .flac files found in the audioset directory. Check your dataset extraction.\")\n",
     "\n",
-    "print(\"Converting audio files to 16kHz WAV...\")\n",
+    "    print(\"Converting audioset files to 16kHz WAV...\")\n",
-    "corrupted_files = []\n",
+    "    for file_path in tqdm(audio_files):\n",
-    "resampled_files = []\n",
-    "\n",
-    "for file_path in tqdm(audio_files, desc=\"Processing audio files\"):\n",
     "        try:\n",
-    "        # Read the .flac file\n",
+    "            # Read and convert the .flac file\n",
-    "        data, samplerate = sf.read(file_path)\n",
+    "            data, samplerate = scipy.io.wavfile.read(file_path)\n",
-    "\n",
-    "        # Check and resample if needed\n",
     "            if samplerate != 16000:\n",
-    "            resampled_files.append(str(file_path))\n",
     "                data = np.interp(\n",
     "                    np.linspace(0, len(data), int(len(data) * 16000 / samplerate)),\n",
     "                    np.arange(len(data)),\n",
     "                    data,\n",
     "                )\n",
     "\n",
-    "        # Convert and save as WAV\n",
+    "            # Save as WAV\n",
-    "        output_path = processed_dir / file_path.name.replace(\".flac\", \".wav\")\n",
+    "            output_path = Path(output_dir) / file_path.name.replace(\".flac\", \".wav\")\n",
     "            scipy.io.wavfile.write(\n",
     "                output_path,\n",
     "                16000,\n",
     "                (data * 32767).astype(np.int16),\n",
     "            )\n",
     "        except Exception as e:\n",
-    "        corrupted_files.append(str(file_path))\n",
+    "            print(f\"Error converting {file_path}: {e}\")\n",
+    "            continue  # Skip and proceed with the next file\n",
     "\n",
-    "# Log corrupted files\n",
+    "# Free Music Archive dataset\n",
-    "if corrupted_files:\n",
+    "# https://github.com/mdeff/fma\n",
-    "    with open(\"corrupted_files.log\", \"w\") as log_file:\n",
+    "# (Third-party mchl914 extra small set)\n",
-    "        log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
     "\n",
-    "# Log resampled files\n",
+    "output_dir = \"./fma\"\n",
-    "if resampled_files:\n",
+    "if not os.path.exists(output_dir):\n",
-    "    with open(\"resampled_files.log\", \"w\") as log_file:\n",
+    "    os.mkdir(output_dir)\n",
-    "        log_file.writelines(f\"{file}\\n\" for file in resampled_files)\n",
+    "    fname = \"fma_xs.zip\"\n",
-    "\n",
+    "    link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n",
-    "print(f\"Audio conversion complete! {len(corrupted_files)} files corrupted and logged.\")\n",
+    "    out_dir = f\"fma/{fname}\"\n",
-    "print(f\"{len(resampled_files)} files resampled and logged.\")\n",
-    "\n",
-    "# Process fma_xs dataset\n",
-    "fma_raw_dir = Path(\"./fma\")\n",
-    "fma_processed_dir = Path(\"./fma_16k\")  # Separate directory for fma_xs processed files\n",
-    "fma_raw_dir.mkdir(exist_ok=True)\n",
-    "fma_processed_dir.mkdir(exist_ok=True)\n",
-    "\n",
-    "fma_link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/fma_xs.zip\"\n",
-    "fma_zip_path = fma_raw_dir / \"fma_xs.zip\"\n",
-    "\n",
-    "if not fma_zip_path.exists():\n",
     "    print(\"Downloading fma_xs dataset...\")\n",
-    "    download_file(fma_link, fma_zip_path)\n",
+    "    response = requests.get(link, stream=True)\n",
+    "    with open(out_dir, \"wb\") as f:\n",
+    "        for chunk in response.iter_content(chunk_size=1024):\n",
+    "            if chunk:\n",
+    "                f.write(chunk)\n",
+    "    print(\"Downloaded fma_xs dataset.\")\n",
     "\n",
-    "print(\"Extracting fma_xs dataset...\")\n",
+    "    os.system(f\"cd fma && unzip -q {fname}\")\n",
-    "download_and_extract_zip(fma_link, fma_raw_dir)\n",
     "\n",
-    "fma_audio_files = list(fma_raw_dir.glob(\"**/*.mp3\"))\n",
+    "    output_dir = \"./fma_16k\"\n",
-    "print(f\"Number of MP3 files found: {len(fma_audio_files)}\")\n",
+    "    if not os.path.exists(output_dir):\n",
-    "if not fma_audio_files:\n",
+    "        os.mkdir(output_dir)\n",
+    "\n",
+    "    # Save clips to 16-bit PCM wav files\n",
+    "    audio_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n",
+    "    print(f\"Number of MP3 files found: {len(audio_files)}\")\n",
+    "    if not audio_files:\n",
     "        raise FileNotFoundError(\"No .mp3 files found in the fma directory. Check your dataset extraction.\")\n",
     "\n",
     "    print(\"Converting fma_xs files to 16kHz WAV...\")\n",
-    "for file_path in tqdm(fma_audio_files, desc=\"Processing fma_xs files\"):\n",
+    "    for file_path in tqdm(audio_files):\n",
     "        try:\n",
-    "        # Read the .mp3 file\n",
+    "            # Read and convert the .mp3 file\n",
-    "        data, samplerate = sf.read(file_path)\n",
+    "            data, samplerate = scipy.io.wavfile.read(file_path)\n",
-    "\n",
-    "        # Check and resample if needed\n",
     "            if samplerate != 16000:\n",
     "                data = np.interp(\n",
     "                    np.linspace(0, len(data), int(len(data) * 16000 / samplerate)),\n",
@@ -321,23 +267,18 @@
     "                    data,\n",
     "                )\n",
     "\n",
-    "        # Convert and save as WAV\n",
+    "            # Save as WAV\n",
-    "        output_path = fma_processed_dir / file_path.name.replace(\".mp3\", \".wav\")\n",
+    "            output_path = Path(output_dir) / file_path.name.replace(\".mp3\", \".wav\")\n",
     "            scipy.io.wavfile.write(\n",
     "                output_path,\n",
     "                16000,\n",
     "                (data * 32767).astype(np.int16),\n",
     "            )\n",
     "        except Exception as e:\n",
-    "        corrupted_files.append(str(file_path))\n",
+    "            print(f\"Error converting {file_path}: {e}\")\n",
+    "            continue  # Skip and proceed with the next file\n",
     "\n",
-    "# Log corrupted files from fma_xs\n",
+    "print(\"All datasets processed successfully!\")"
-    "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",
-    "\n",
-    "print(\"fma_xs processing complete!\")\n",
-    "print(\"Full-scale dataset preparation complete!\")"
    ]
   },
   {
@@ -712,7 +653,7 @@
     "# Define the JSON metadata for the model\n",
     "json_data = {\n",
     "    \"type\": \"micro\",\n",
-    "    \"wake_word\": \"hey_norman\",  # Adjust based on your target wake word\n",
+    "    \"wake_word\": \"khum_puter\",  # Adjust based on your target wake word\n",
     "    \"author\": \"master phooey\",\n",
     "    \"website\": \"https://github.com/MasterPhooey/MicroWakeWord-Trainer-Docker\",\n",
     "    \"model\": \"stream_state_internal_quant.tflite\",\n",