ObjectDetectorUsingAudio.ipynb

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            "\u001b[?25hInstalling collected packages: pydub, websockets, tomlkit, semantic-version, ruff, python-multipart, orjson, h11, ffmpy, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, timm, gradio-client, gradio\n",
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          ]
        }
      ],
      "source": [
        "!pip install timm transformers torch gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scipy phonemizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5azHXjWtW8do",
        "outputId": "b66ee778-7ea2-473e-e0ef-09fd8f4d68e2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hBuilding wheels for collected packages: pylatexenc\n",
            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pylatexenc: filename=pylatexenc-2.10-py3-none-any.whl size=136817 sha256=0990f1af89dac16ab8f58de1c9318e4146741f0500a8d5f2ec4f6c34d39b4d35\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/31/8b/e09b0386afd80cfc556c00408c9aeea5c35c4d484a9c762fd5\n",
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            "Successfully installed bibtexparser-2.0.0b7 clldutils-3.22.2 colorama-0.4.6 colorlog-6.8.2 csvw-3.3.1 dlinfo-1.2.1 isodate-0.6.1 language-tags-1.2.0 phonemizer-3.3.0 pylatexenc-2.10 rdflib-7.0.0 rfc3986-1.5.0 segments-2.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import scipy.io.wavfile as wavfile\n",
        "\n",
        "\n",
        "# Use a pipeline as a high-level helper\n",
        "from transformers import pipeline\n",
        "\n",
        "# model_path = (\"../Models/models--facebook--detr-resnet-50/snapshots\"\n",
        "#               \"/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b\")\n",
        "#\n",
        "# tts_model_path = (\"../Models/models--kakao-enterprise--vits-ljs/snapshots\"\n",
        "#                   \"/3bcb8321394f671bd948ebf0d086d694dda95464\")\n",
        "\n",
        "\n",
        "narrator = pipeline(\"text-to-speech\",\n",
        "                    model=\"kakao-enterprise/vits-ljs\")\n",
        "\n",
        "object_detector = pipeline(\"object-detection\",\n",
        "                model=\"facebook/detr-resnet-50\")\n",
        "\n",
        "# object_detector = pipeline(\"object-detection\",\n",
        "#                 model=model_path)\n",
        "#\n",
        "# narrator = pipeline(\"text-to-speech\",\n",
        "#                     model=tts_model_path)\n",
        "\n",
        "# [{'score': 0.9996405839920044, 'label': 'person', 'box': {'xmin': 435, 'ymin': 282, 'xmax': 636, 'ymax': 927}}, {'score': 0.9995879530906677, 'label': 'dog', 'box': {'xmin': 570, 'ymin': 694, 'xmax': 833, 'ymax': 946}}]\n",
        "\n",
        "# Define the function to generate audio from text\n",
        "def generate_audio(text):\n",
        "    # Generate the narrated text\n",
        "    narrated_text = narrator(text)\n",
        "\n",
        "    # Save the audio to a WAV file\n",
        "    wavfile.write(\"output.wav\", rate=narrated_text[\"sampling_rate\"],\n",
        "                  data=narrated_text[\"audio\"][0])\n",
        "\n",
        "    # Return the path to the saved audio file\n",
        "    return \"output.wav\"\n",
        "\n",
        "# Could you please write me a python code that will take list of detection object as an input and it will give the response that will include all the objects (labels) provided in the input. For example if the input is like this: [{'score': 0.9996405839920044, 'label': 'person', 'box': {'xmin': 435, 'ymin': 282, 'xmax': 636, 'ymax': 927}}, {'score': 0.9995879530906677, 'label': 'dog', 'box': {'xmin': 570, 'ymin': 694, 'xmax': 833, 'ymax': 946}}]\n",
        "# The output should be, This pictuture contains 1 person and 1 dog. If there are multiple objects, do not add 'and' between every objects but 'and' should be at the end only\n",
        "\n",
        "\n",
        "def read_objects(detection_objects):\n",
        "    # Initialize counters for each object label\n",
        "    object_counts = {}\n",
        "\n",
        "    # Count the occurrences of each label\n",
        "    for detection in detection_objects:\n",
        "        label = detection['label']\n",
        "        if label in object_counts:\n",
        "            object_counts[label] += 1\n",
        "        else:\n",
        "            object_counts[label] = 1\n",
        "\n",
        "    # Generate the response string\n",
        "    response = \"This picture contains\"\n",
        "    labels = list(object_counts.keys())\n",
        "    for i, label in enumerate(labels):\n",
        "        response += f\" {object_counts[label]} {label}\"\n",
        "        if object_counts[label] > 1:\n",
        "            response += \"s\"\n",
        "        if i < len(labels) - 2:\n",
        "            response += \",\"\n",
        "        elif i == len(labels) - 2:\n",
        "            response += \" and\"\n",
        "\n",
        "    response += \".\"\n",
        "\n",
        "    return response\n",
        "\n",
        "\n",
        "\n",
        "def draw_bounding_boxes(image, detections, font_path=None, font_size=20):\n",
        "    \"\"\"\n",
        "    Draws bounding boxes on the given image based on the detections.\n",
        "    :param image: PIL.Image object\n",
        "    :param detections: List of detection results, where each result is a dictionary containing\n",
        "                       'score', 'label', and 'box' keys. 'box' itself is a dictionary with 'xmin',\n",
        "                       'ymin', 'xmax', 'ymax'.\n",
        "    :param font_path: Path to the TrueType font file to use for text.\n",
        "    :param font_size: Size of the font to use for text.\n",
        "    :return: PIL.Image object with bounding boxes drawn.\n",
        "    \"\"\"\n",
        "    # Make a copy of the image to draw on\n",
        "    draw_image = image.copy()\n",
        "    draw = ImageDraw.Draw(draw_image)\n",
        "\n",
        "    # Load custom font or default font if path not provided\n",
        "    if font_path:\n",
        "        font = ImageFont.truetype(font_path, font_size)\n",
        "    else:\n",
        "        # When font_path is not provided, load default font but it's size is fixed\n",
        "        font = ImageFont.load_default()\n",
        "        # Increase font size workaround by using a TTF font file, if needed, can download and specify the path\n",
        "\n",
        "    for detection in detections:\n",
        "        box = detection['box']\n",
        "        xmin = box['xmin']\n",
        "        ymin = box['ymin']\n",
        "        xmax = box['xmax']\n",
        "        ymax = box['ymax']\n",
        "\n",
        "        # Draw the bounding box\n",
        "        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline=\"red\", width=3)\n",
        "\n",
        "        # Optionally, you can also draw the label and score\n",
        "        label = detection['label']\n",
        "        score = detection['score']\n",
        "        text = f\"{label} {score:.2f}\"\n",
        "\n",
        "        # Draw text with background rectangle for visibility\n",
        "        if font_path:  # Use the custom font with increased size\n",
        "            text_size = draw.textbbox((xmin, ymin), text, font=font)\n",
        "        else:\n",
        "            # Calculate text size using the default font\n",
        "            text_size = draw.textbbox((xmin, ymin), text)\n",
        "\n",
        "        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill=\"red\")\n",
        "        draw.text((xmin, ymin), text, fill=\"white\", font=font)\n",
        "\n",
        "    return draw_image\n",
        "\n",
        "\n",
        "def detect_object(image):\n",
        "    raw_image = image\n",
        "    output = object_detector(raw_image)\n",
        "    processed_image = draw_bounding_boxes(raw_image, output)\n",
        "    natural_text = read_objects(output)\n",
        "    processed_audio = generate_audio(natural_text)\n",
        "    return processed_image, processed_audio\n",
        "\n",
        "\n",
        "demo = gr.Interface(fn=detect_object,\n",
        "                    inputs=[gr.Image(label=\"Select Image\",type=\"pil\")],\n",
        "                    outputs=[gr.Image(label=\"Processed Image\", type=\"pil\"), gr.Audio(label=\"Generated Audio\")],\n",
        "                    title=\"Object Detector with Audio\",\n",
        "                    description=\"THIS APPLICATION WILL BE USED TO HIGHLIGHT OBJECTS AND GIVES AUDIO DESCRIPTION FOR THE PROVIDED INPUT IMAGE.\")\n",
        "demo.launch()\n",
        "\n",
        "# print(output)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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        "outputId": "8606ecd9-f020-403c-a61c-6fccebda1565"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.64k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/145M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at kakao-enterprise/vits-ljs were not used when initializing VitsModel: ['flow.flows.0.wavenet.in_layers.0.weight_g', 'flow.flows.0.wavenet.in_layers.0.weight_v', 'flow.flows.0.wavenet.in_layers.1.weight_g', 'flow.flows.0.wavenet.in_layers.1.weight_v', 'flow.flows.0.wavenet.in_layers.2.weight_g', 'flow.flows.0.wavenet.in_layers.2.weight_v', 'flow.flows.0.wavenet.in_layers.3.weight_g', 'flow.flows.0.wavenet.in_layers.3.weight_v', 'flow.flows.0.wavenet.res_skip_layers.0.weight_g', 'flow.flows.0.wavenet.res_skip_layers.0.weight_v', 'flow.flows.0.wavenet.res_skip_layers.1.weight_g', 'flow.flows.0.wavenet.res_skip_layers.1.weight_v', 'flow.flows.0.wavenet.res_skip_layers.2.weight_g', 'flow.flows.0.wavenet.res_skip_layers.2.weight_v', 'flow.flows.0.wavenet.res_skip_layers.3.weight_g', 'flow.flows.0.wavenet.res_skip_layers.3.weight_v', 'flow.flows.1.wavenet.in_layers.0.weight_g', 'flow.flows.1.wavenet.in_layers.0.weight_v', 'flow.flows.1.wavenet.in_layers.1.weight_g', 'flow.flows.1.wavenet.in_layers.1.weight_v', 'flow.flows.1.wavenet.in_layers.2.weight_g', 'flow.flows.1.wavenet.in_layers.2.weight_v', 'flow.flows.1.wavenet.in_layers.3.weight_g', 'flow.flows.1.wavenet.in_layers.3.weight_v', 'flow.flows.1.wavenet.res_skip_layers.0.weight_g', 'flow.flows.1.wavenet.res_skip_layers.0.weight_v', 'flow.flows.1.wavenet.res_skip_layers.1.weight_g', 'flow.flows.1.wavenet.res_skip_layers.1.weight_v', 'flow.flows.1.wavenet.res_skip_layers.2.weight_g', 'flow.flows.1.wavenet.res_skip_layers.2.weight_v', 'flow.flows.1.wavenet.res_skip_layers.3.weight_g', 'flow.flows.1.wavenet.res_skip_layers.3.weight_v', 'flow.flows.2.wavenet.in_layers.0.weight_g', 'flow.flows.2.wavenet.in_layers.0.weight_v', 'flow.flows.2.wavenet.in_layers.1.weight_g', 'flow.flows.2.wavenet.in_layers.1.weight_v', 'flow.flows.2.wavenet.in_layers.2.weight_g', 'flow.flows.2.wavenet.in_layers.2.weight_v', 'flow.flows.2.wavenet.in_layers.3.weight_g', 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'posterior_encoder.wavenet.in_layers.15.weight_g', 'posterior_encoder.wavenet.in_layers.15.weight_v', 'posterior_encoder.wavenet.in_layers.2.weight_g', 'posterior_encoder.wavenet.in_layers.2.weight_v', 'posterior_encoder.wavenet.in_layers.3.weight_g', 'posterior_encoder.wavenet.in_layers.3.weight_v', 'posterior_encoder.wavenet.in_layers.4.weight_g', 'posterior_encoder.wavenet.in_layers.4.weight_v', 'posterior_encoder.wavenet.in_layers.5.weight_g', 'posterior_encoder.wavenet.in_layers.5.weight_v', 'posterior_encoder.wavenet.in_layers.6.weight_g', 'posterior_encoder.wavenet.in_layers.6.weight_v', 'posterior_encoder.wavenet.in_layers.7.weight_g', 'posterior_encoder.wavenet.in_layers.7.weight_v', 'posterior_encoder.wavenet.in_layers.8.weight_g', 'posterior_encoder.wavenet.in_layers.8.weight_v', 'posterior_encoder.wavenet.in_layers.9.weight_g', 'posterior_encoder.wavenet.in_layers.9.weight_v', 'posterior_encoder.wavenet.res_skip_layers.0.weight_g', 'posterior_encoder.wavenet.res_skip_layers.0.weight_v', 'posterior_encoder.wavenet.res_skip_layers.1.weight_g', 'posterior_encoder.wavenet.res_skip_layers.1.weight_v', 'posterior_encoder.wavenet.res_skip_layers.10.weight_g', 'posterior_encoder.wavenet.res_skip_layers.10.weight_v', 'posterior_encoder.wavenet.res_skip_layers.11.weight_g', 'posterior_encoder.wavenet.res_skip_layers.11.weight_v', 'posterior_encoder.wavenet.res_skip_layers.12.weight_g', 'posterior_encoder.wavenet.res_skip_layers.12.weight_v', 'posterior_encoder.wavenet.res_skip_layers.13.weight_g', 'posterior_encoder.wavenet.res_skip_layers.13.weight_v', 'posterior_encoder.wavenet.res_skip_layers.14.weight_g', 'posterior_encoder.wavenet.res_skip_layers.14.weight_v', 'posterior_encoder.wavenet.res_skip_layers.15.weight_g', 'posterior_encoder.wavenet.res_skip_layers.15.weight_v', 'posterior_encoder.wavenet.res_skip_layers.2.weight_g', 'posterior_encoder.wavenet.res_skip_layers.2.weight_v', 'posterior_encoder.wavenet.res_skip_layers.3.weight_g', 'posterior_encoder.wavenet.res_skip_layers.3.weight_v', 'posterior_encoder.wavenet.res_skip_layers.4.weight_g', 'posterior_encoder.wavenet.res_skip_layers.4.weight_v', 'posterior_encoder.wavenet.res_skip_layers.5.weight_g', 'posterior_encoder.wavenet.res_skip_layers.5.weight_v', 'posterior_encoder.wavenet.res_skip_layers.6.weight_g', 'posterior_encoder.wavenet.res_skip_layers.6.weight_v', 'posterior_encoder.wavenet.res_skip_layers.7.weight_g', 'posterior_encoder.wavenet.res_skip_layers.7.weight_v', 'posterior_encoder.wavenet.res_skip_layers.8.weight_g', 'posterior_encoder.wavenet.res_skip_layers.8.weight_v', 'posterior_encoder.wavenet.res_skip_layers.9.weight_g', 'posterior_encoder.wavenet.res_skip_layers.9.weight_v']\n",
            "- This IS expected if you are initializing VitsModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing VitsModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of VitsModel were not initialized from the model checkpoint at kakao-enterprise/vits-ljs and are newly initialized: ['flow.flows.0.wavenet.in_layers.0.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.0.parametrizations.weight.original1', 'flow.flows.0.wavenet.in_layers.1.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.1.parametrizations.weight.original1', 'flow.flows.0.wavenet.in_layers.2.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.2.parametrizations.weight.original1', 'flow.flows.0.wavenet.in_layers.3.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.3.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.0.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.0.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.1.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.1.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.2.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.2.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.3.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.0.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.0.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.1.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.1.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.2.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.2.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.3.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.3.parametrizations.weight.original1', 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'flow.flows.2.wavenet.in_layers.2.parametrizations.weight.original1', 'flow.flows.2.wavenet.in_layers.3.parametrizations.weight.original0', 'flow.flows.2.wavenet.in_layers.3.parametrizations.weight.original1', 'flow.flows.2.wavenet.res_skip_layers.0.parametrizations.weight.original0', 'flow.flows.2.wavenet.res_skip_layers.0.parametrizations.weight.original1', 'flow.flows.2.wavenet.res_skip_layers.1.parametrizations.weight.original0', 'flow.flows.2.wavenet.res_skip_layers.1.parametrizations.weight.original1', 'flow.flows.2.wavenet.res_skip_layers.2.parametrizations.weight.original0', 'flow.flows.2.wavenet.res_skip_layers.2.parametrizations.weight.original1', 'flow.flows.2.wavenet.res_skip_layers.3.parametrizations.weight.original0', 'flow.flows.2.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'flow.flows.3.wavenet.in_layers.0.parametrizations.weight.original0', 'flow.flows.3.wavenet.in_layers.0.parametrizations.weight.original1', 'flow.flows.3.wavenet.in_layers.1.parametrizations.weight.original0', 'flow.flows.3.wavenet.in_layers.1.parametrizations.weight.original1', 'flow.flows.3.wavenet.in_layers.2.parametrizations.weight.original0', 'flow.flows.3.wavenet.in_layers.2.parametrizations.weight.original1', 'flow.flows.3.wavenet.in_layers.3.parametrizations.weight.original0', 'flow.flows.3.wavenet.in_layers.3.parametrizations.weight.original1', 'flow.flows.3.wavenet.res_skip_layers.0.parametrizations.weight.original0', 'flow.flows.3.wavenet.res_skip_layers.0.parametrizations.weight.original1', 'flow.flows.3.wavenet.res_skip_layers.1.parametrizations.weight.original0', 'flow.flows.3.wavenet.res_skip_layers.1.parametrizations.weight.original1', 'flow.flows.3.wavenet.res_skip_layers.2.parametrizations.weight.original0', 'flow.flows.3.wavenet.res_skip_layers.2.parametrizations.weight.original1', 'flow.flows.3.wavenet.res_skip_layers.3.parametrizations.weight.original0', 'flow.flows.3.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.0.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.0.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.1.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.1.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.10.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.10.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.11.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.11.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.12.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.12.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.13.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.13.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.14.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.14.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.15.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.15.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.2.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.2.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.3.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.3.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.4.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.4.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.5.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.5.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.6.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.6.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.7.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.7.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.8.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.8.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.9.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.9.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.0.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.0.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.1.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.1.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.10.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.10.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.11.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.11.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.12.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.12.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.13.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.13.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.14.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.14.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.15.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.15.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.2.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.2.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.3.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.4.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.4.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.5.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.5.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.6.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.6.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.7.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.7.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.8.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.8.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.9.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.9.parametrizations.weight.original1']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/285 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3a3e80f985a430ea6bdadc39e67713d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.14k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4182d53de4274cfd80c8de4a9fd75cb8"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/47.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3418ad396d94e9f8cb590533eae11f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/4.59k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77b17c835e27447fb061c9de7dc96ef4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/167M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a4aec396e0645b19b47daa52d3c7cbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/102M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc0b02182ea9428d9c994519a4dcdf18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at facebook/detr-resnet-50 were not used when initializing DetrForObjectDetection: ['model.backbone.conv_encoder.model.layer1.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer2.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer3.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer4.0.downsample.1.num_batches_tracked']\n",
            "- This IS expected if you are initializing DetrForObjectDetection from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DetrForObjectDetection from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/290 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a21eaf15db44d77808b5aedc14bff8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://5e8145d2e451a56813.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://5e8145d2e451a56813.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gradio deploy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHNzdr_ceQB8",
        "outputId": "22ec0a56-191e-41ae-fdf1-10d0d6d4a1ba"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rmnist_test.csv:   0% 0.00/18.3M [00:00<?, ?B/s]\n",
            "\rUpload 2 LFS files:   0% 0/2 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "\rmnist_train_small.csv:   0% 0.00/36.5M [00:00<?, ?B/s]\u001b[A\u001b[A\n",
            "\n",
            "mnist_test.csv:  27% 5.01M/18.3M [00:00<00:00, 35.2MB/s]\n",
            "\n",
            "mnist_test.csv:  47% 8.54M/18.3M [00:00<00:00, 22.6MB/s]\n",
            "\n",
            "mnist_test.csv:  88% 16.0M/18.3M [00:00<00:00, 18.4MB/s]\n",
            "\n",
            "mnist_train_small.csv:  46% 17.0M/36.5M [00:01<00:01, 11.5MB/s]\u001b[A\u001b[A\n",
            "\n",
            "mnist_test.csv: 100% 18.3M/18.3M [00:01<00:00, 14.2MB/s]\n",
            "\n",
            "Upload 2 LFS files:  50% 1/2 [00:01<00:01,  1.47s/it]\u001b[A\n",
            "\n",
            "mnist_train_small.csv:  75% 27.6M/36.5M [00:01<00:00, 15.7MB/s]\u001b[A\u001b[A\n",
            "\n",
            "mnist_train_small.csv: 100% 36.5M/36.5M [00:02<00:00, 16.1MB/s]\n",
            "\n",
            "Upload 2 LFS files: 100% 2/2 [00:02<00:00,  1.26s/it]\n",
            "Space available at \u001b[4;94mhttps://huggingface.co/spaces/arssite/Object_Detectio_with_Audio_Output\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aJzdO86bgwgC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}