diff --git a/colab/demo.ipynb b/colab/demo.ipynb
index b034ce9..cc55d35 100644
--- a/colab/demo.ipynb
+++ b/colab/demo.ipynb
@@ -1,33 +1,471 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd70762d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/eloimoliner/denoising-historical-recordings/blob/colab/colab/demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# New Section"
+      ],
+      "metadata": {
+        "id": "jbe_aWYkjWRH"
+      },
+      "id": "jbe_aWYkjWRH"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "dd70762d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dd70762d",
+        "outputId": "499681b1-46d6-4f21-b3b8-3a3348f6765b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'denoising-historical-recordings'...\n",
+            "remote: Enumerating objects: 177, done.\u001b[K\n",
+            "remote: Counting objects: 100% (177/177), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (153/153), done.\u001b[K\n",
+            "remote: Total 177 (delta 63), reused 99 (delta 16), pack-reused 0\u001b[K\n",
+            "Receiving objects: 100% (177/177), 97.29 KiB | 4.86 MiB/s, done.\n",
+            "Resolving deltas: 100% (63/63), done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "#download the files\n",
+        "! git clone https://github.com/eloimoliner/denoising-historical-recordings.git\n",
+        "! wget https://github.com/eloimoliner/denoising-historical-recordings/releases/download/v0.0/checkpoint.zip\n",
+        "! unzip checkpoint.zip -d denoising-historical-recordings/experiments/trained_model/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "%cd denoising-historical-recordings"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HmHRpa5eloy2",
+        "outputId": "34af882d-8fb7-4b2a-dccd-50e328e0ab90"
+      },
+      "id": "HmHRpa5eloy2",
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/denoising-historical-recordings\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "gvd6KZkTlyhR"
+      },
+      "id": "gvd6KZkTlyhR",
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#install dependencies\n",
+        "! pip install hydra-core==0.11.3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nmic9hVzmSj6",
+        "outputId": "c549f0d1-9e5c-4445-ecd4-702697535011"
+      },
+      "id": "nmic9hVzmSj6",
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting hydra-core==0.11.3\n",
+            "  Downloading hydra_core-0.11.3-py3-none-any.whl (72 kB)\n",
+            "\u001b[?25l\r\u001b[K     |████▌                           | 10 kB 19.6 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 20 kB 12.0 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 30 kB 8.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 40 kB 7.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 51 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▎    | 61 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 71 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 72 kB 372 kB/s \n",
+            "\u001b[?25hCollecting omegaconf<1.5,>=1.4\n",
+            "  Downloading omegaconf-1.4.1-py3-none-any.whl (14 kB)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from omegaconf<1.5,>=1.4->hydra-core==0.11.3) (1.15.0)\n",
+            "Collecting PyYAML\n",
+            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
+            "\u001b[K     |████████████████████████████████| 596 kB 39.4 MB/s \n",
+            "\u001b[?25hInstalling collected packages: PyYAML, omegaconf, hydra-core\n",
+            "Successfully installed PyYAML-6.0 hydra-core-0.11.3 omegaconf-1.4.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import unet\n",
+        "import tensorflow as tf\n",
+        "import soundfile as sf\n",
+        "import numpy as np\n",
+        "from tqdm import tqdm\n",
+        "import scipy.signal\n",
+        "import hydra\n",
+        "import os"
+      ],
+      "metadata": {
+        "id": "TQBDTmO4mUBx"
+      },
+      "id": "TQBDTmO4mUBx",
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#workaround to load hydra conf file\n",
+        "import yaml\n",
+        "from pathlib import Path\n",
+        "args = yaml.safe_load(Path('conf/conf.yaml').read_text())\n",
+        "class dotdict(dict):\n",
+        "    \"\"\"dot.notation access to dictionary attributes\"\"\"\n",
+        "    __getattr__ = dict.get\n",
+        "    __setattr__ = dict.__setitem__\n",
+        "    __delattr__ = dict.__delitem__\n",
+        "args=dotdict(args)\n",
+        "unet_args=dotdict(args.unet)"
+      ],
+      "metadata": {
+        "id": "yMI9sIUYo9Lp"
+      },
+      "id": "yMI9sIUYo9Lp",
+      "execution_count": 62,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "path_experiment=str(args.path_experiment)\n",
+        "\n",
+        "unet_model = unet.build_model_denoise(unet_args=unet_args)\n",
+        "\n",
+        "ckpt=os.path.join(\"/content/denoising-historical-recordings\",path_experiment, 'checkpoint')\n",
+        "unet_model.load_weights(ckpt)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cbdPyEpAp7s0",
+        "outputId": "63df20d2-1c00-41a5-c8fc-a6cdcfb57d08"
+      },
+      "id": "cbdPyEpAp7s0",
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f1d0d679dd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def do_stft(noisy):\n",
+        "        \n",
+        "    window_fn = tf.signal.hamming_window\n",
+        "\n",
+        "    win_size=args.stft[\"win_size\"]\n",
+        "    hop_size=args.stft[\"hop_size\"]\n",
+        "\n",
+        "    \n",
+        "    stft_signal_noisy=tf.signal.stft(noisy,frame_length=win_size, window_fn=window_fn, frame_step=hop_size, pad_end=True)\n",
+        "    stft_noisy_stacked=tf.stack( values=[tf.math.real(stft_signal_noisy), tf.math.imag(stft_signal_noisy)], axis=-1)\n",
+        "\n",
+        "    return stft_noisy_stacked\n",
+        "\n",
+        "def do_istft(data):\n",
+        "    \n",
+        "    window_fn = tf.signal.hamming_window\n",
+        "\n",
+        "    win_size=args.stft[\"win_size\"]\n",
+        "    hop_size=args.stft[\"hop_size\"]\n",
+        "\n",
+        "    inv_window_fn=tf.signal.inverse_stft_window_fn(hop_size, forward_window_fn=window_fn)\n",
+        "\n",
+        "    pred_cpx=data[...,0] + 1j * data[...,1]\n",
+        "    pred_time=tf.signal.inverse_stft(pred_cpx, win_size, hop_size, window_fn=inv_window_fn)\n",
+        "    return pred_time\n"
+      ],
+      "metadata": {
+        "id": "iDJGN-1_taXR"
+      },
+      "id": "iDJGN-1_taXR",
+      "execution_count": 79,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def denoise_audio(audio):\n",
+        "\n",
+        "    data, samplerate = sf.read(audio)\n",
+        "    print(data.dtype)\n",
+        "    #Stereo to mono\n",
+        "    if len(data.shape)>1:\n",
+        "        data=np.mean(data,axis=1)\n",
+        "    \n",
+        "    if samplerate!=44100: \n",
+        "        print(\"Resampling\")\n",
+        "   \n",
+        "        data=scipy.signal.resample(data, int((44100  / samplerate )*len(data))+1)  \n",
+        " \n",
+        "    \n",
+        "    \n",
+        "    segment_size=44100*5  #20s segments\n",
+        "\n",
+        "    length_data=len(data)\n",
+        "    overlapsize=2048 #samples (46 ms)\n",
+        "    window=np.hanning(2*overlapsize)\n",
+        "    window_right=window[overlapsize::]\n",
+        "    window_left=window[0:overlapsize]\n",
+        "    audio_finished=False\n",
+        "    pointer=0\n",
+        "    denoised_data=np.zeros(shape=(len(data),))\n",
+        "    residual_noise=np.zeros(shape=(len(data),))\n",
+        "    numchunks=int(np.ceil(length_data/segment_size))\n",
+        "     \n",
+        "    for i in tqdm(range(numchunks)):\n",
+        "        if pointer+segment_size<length_data:\n",
+        "            segment=data[pointer:pointer+segment_size]\n",
+        "            #dostft\n",
+        "            segment_TF=do_stft(segment)\n",
+        "            segment_TF_ds=tf.data.Dataset.from_tensors(segment_TF)\n",
+        "            pred = unet_model.predict(segment_TF_ds.batch(1))\n",
+        "            pred=pred[0]\n",
+        "            residual=segment_TF-pred[0]\n",
+        "            residual=np.array(residual)\n",
+        "            pred_time=do_istft(pred[0])\n",
+        "            residual_time=do_istft(residual)\n",
+        "            residual_time=np.array(residual_time)\n",
+        "\n",
+        "            if pointer==0:\n",
+        "                pred_time=np.concatenate((pred_time[0:int(segment_size-overlapsize)], np.multiply(pred_time[int(segment_size-overlapsize):segment_size],window_right)), axis=0)\n",
+        "                residual_time=np.concatenate((residual_time[0:int(segment_size-overlapsize)], np.multiply(residual_time[int(segment_size-overlapsize):segment_size],window_right)), axis=0)\n",
+        "            else:\n",
+        "                pred_time=np.concatenate((np.multiply(pred_time[0:int(overlapsize)], window_left), pred_time[int(overlapsize):int(segment_size-overlapsize)], np.multiply(pred_time[int(segment_size-overlapsize):int(segment_size)],window_right)), axis=0)\n",
+        "                residual_time=np.concatenate((np.multiply(residual_time[0:int(overlapsize)], window_left), residual_time[int(overlapsize):int(segment_size-overlapsize)], np.multiply(residual_time[int(segment_size-overlapsize):int(segment_size)],window_right)), axis=0)\n",
+        "                \n",
+        "            denoised_data[pointer:pointer+segment_size]=denoised_data[pointer:pointer+segment_size]+pred_time\n",
+        "            residual_noise[pointer:pointer+segment_size]=residual_noise[pointer:pointer+segment_size]+residual_time\n",
+        "\n",
+        "            pointer=pointer+segment_size-overlapsize\n",
+        "        else: \n",
+        "            segment=data[pointer::]\n",
+        "            lensegment=len(segment)\n",
+        "            segment=np.concatenate((segment, np.zeros(shape=(int(segment_size-len(segment)),))), axis=0)\n",
+        "            audio_finished=True\n",
+        "            #dostft\n",
+        "            segment_TF=do_stft(segment)\n",
+        "\n",
+        "            segment_TF_ds=tf.data.Dataset.from_tensors(segment_TF)\n",
+        "\n",
+        "            pred = unet_model.predict(segment_TF_ds.batch(1))\n",
+        "            pred=pred[0]\n",
+        "            residual=segment_TF-pred[0]\n",
+        "            residual=np.array(residual)\n",
+        "            pred_time=do_istft(pred[0])\n",
+        "            pred_time=np.array(pred_time)\n",
+        "            pred_time=pred_time[0:segment_size]\n",
+        "            residual_time=do_istft(residual)\n",
+        "            residual_time=np.array(residual_time)\n",
+        "            residual_time=residual_time[0:segment_size]\n",
+        "            if pointer==0:\n",
+        "                pred_time=pred_time\n",
+        "                residual_time=residual_time\n",
+        "            else:\n",
+        "                pred_time=np.concatenate((np.multiply(pred_time[0:int(overlapsize)], window_left), pred_time[int(overlapsize):int(segment_size)]),axis=0)\n",
+        "                residual_time=np.concatenate((np.multiply(residual_time[0:int(overlapsize)], window_left), residual_time[int(overlapsize):int(segment_size)]),axis=0)\n",
+        "\n",
+        "            denoised_data[pointer::]=denoised_data[pointer::]+pred_time[0:lensegment]\n",
+        "            residual_noise[pointer::]=residual_noise[pointer::]+residual_time[0:lensegment]\n",
+        "    return denoised_data"
+      ],
+      "metadata": {
+        "id": "LcSmbzm_z8w9"
+      },
+      "id": "LcSmbzm_z8w9",
+      "execution_count": 72,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded=files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "resources": {
+            "http://localhost:8080/nbextensions/google.colab/files.js": {
+              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgZG8gewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwoKICAgICAgbGV0IHBlcmNlbnREb25lID0gZmlsZURhdGEuYnl0ZUxlbmd0aCA9PT0gMCA/CiAgICAgICAgICAxMDAgOgogICAgICAgICAgTWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCk7CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPSBgJHtwZXJjZW50RG9uZX0lIGRvbmVgOwoKICAgIH0gd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCk7CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            }
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "50Kmdy6AtbhW",
+        "outputId": "31d357e7-5adb-45f4-a3fe-3bb11d05a5af"
+      },
+      "id": "50Kmdy6AtbhW",
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-d94b6f39-a36a-4c09-b590-17eb2e0fd976\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-d94b6f39-a36a-4c09-b590-17eb2e0fd976\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving Carmen-Habanera_(Love_is_Like_a_Woo_-_Marguerite_D'Alvarez_noisy_input.wav to Carmen-Habanera_(Love_is_Like_a_Woo_-_Marguerite_D'Alvarez_noisy_input.wav\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for fn in uploaded.keys():\n",
+        "  print('Denoising uploaded file \"{name}\"'.format(\n",
+        "      name=fn))\n",
+        "  denoise_data=denoise_audio(fn)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0po6zpvrylc2",
+        "outputId": "0d2b228e-dad8-40f8-f10a-118ea115180d"
+      },
+      "id": "0po6zpvrylc2",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\r  0%|          | 0/41 [00:00<?, ?it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Denoising uploaded file \"Carmen-Habanera_(Love_is_Like_a_Woo_-_Marguerite_D'Alvarez_noisy_input.wav\"\n",
+            "float64\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            " 71%|███████   | 29/41 [09:59<04:09, 20.77s/it]"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "3tEshWBezYvf"
+      },
+      "id": "3tEshWBezYvf",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.5"
+    },
+    "colab": {
+      "name": "demo.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file