FakeAndCatch.ipynb

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    "colab": {
      "name": "FakeAndCatch.ipynb",
      "provenance": [],
      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yk3PMfBuZhS",
        "colab_type": "text"
      },
      "source": [
        "Make sure GPU is enabled\n",
        "Runtime -> Change Runtime Type -> Hardware Accelerator -> GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhunyJSod_UT",
        "colab_type": "code",
        "outputId": "3390a8d6-0ba2-4c2b-e4e0-7164fe494237",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# Clone git repo\n",
        "!git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Real-Time-Voice-Cloning'...\n",
            "remote: Enumerating objects: 2363, done.\u001b[K\n",
            "remote: Total 2363 (delta 0), reused 0 (delta 0), pack-reused 2363\u001b[K\n",
            "Receiving objects: 100% (2363/2363), 360.72 MiB | 41.76 MiB/s, done.\n",
            "Resolving deltas: 100% (1296/1296), done.\n"
          ],
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        }
      ]
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        "id": "IEAy7tlAHDI6",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install rev-ai\n",
        "!pip install firebase_admin\n",
        "!pip install wget\n",
        "!pip install pydub"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Successfully installed enum34-1.1.6 mock-3.0.5 rev-ai-2.7.0 websocket-client-0.56.0\n"
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            "\u001b[31mERROR: google-cloud-storage 1.26.0 has requirement google-auth<2.0dev,>=1.11.0, but you'll have google-auth 1.7.2 which is incompatible.\u001b[0m\n",
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            "\u001b[31mERROR: google-cloud-bigquery 1.21.0 has requirement google-resumable-media!=0.4.0,<0.5.0dev,>=0.3.1, but you'll have google-resumable-media 0.5.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: google-cloud-firestore, google-resumable-media, google-cloud-storage, cachecontrol, firebase-admin\n",
            "  Found existing installation: google-resumable-media 0.4.1\n",
            "    Uninstalling google-resumable-media-0.4.1:\n",
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            "  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9682 sha256=59985577af54a84a2e0a337a371a5da89d405f8d6d60e6dcdf3a2ae5d24a296a\n",
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            "Successfully built wget\n",
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            "Collecting pydub\n",
            "  Downloading https://files.pythonhosted.org/packages/79/db/eaf620b73a1eec3c8c6f8f5b0b236a50f9da88ad57802154b7ba7664d0b8/pydub-0.23.1-py2.py3-none-any.whl\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "GneTTDCIs8TM",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd Real-Time-Voice-Cloning/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Real-Time-Voice-Cloning\n"
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          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Install dependencies\n",
        "!pip install -q -r requirements.txt\n",
        "!apt-get install -qq libportaudio2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "\u001b[K     |████████████████████████████████| 3.2MB 56.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 256kB 57.3MB/s \n",
            "\u001b[?25h  Building wheel for visdom (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for webrtcvad (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for torchfile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 1.14.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 1.14.0 which is incompatible.\u001b[0m\n",
            "Selecting previously unselected package libportaudio2:amd64.\n",
            "(Reading database ... 145113 files and directories currently installed.)\n",
            "Preparing to unpack .../libportaudio2_19.6.0-1_amd64.deb ...\n",
            "Unpacking libportaudio2:amd64 (19.6.0-1) ...\n",
            "Setting up libportaudio2:amd64 (19.6.0-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuwgOQlPeN8a",
        "colab_type": "code",
        "outputId": "28ea9e44-4375-4edc-d863-cac8affcc073",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "# Download dataset\n",
        "!gdown https://drive.google.com/uc?id=1n1sPXvT34yXFLT47QZA6FIRGrwMeSsZc\n",
        "!unzip pretrained.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1n1sPXvT34yXFLT47QZA6FIRGrwMeSsZc\n",
            "To: /content/Real-Time-Voice-Cloning/pretrained.zip\n",
            "384MB [00:01, 207MB/s]\n",
            "Archive:  pretrained.zip\n",
            "   creating: encoder/saved_models/\n",
            "  inflating: encoder/saved_models/pretrained.pt  \n",
            "   creating: synthesizer/saved_models/\n",
            "   creating: synthesizer/saved_models/logs-pretrained/\n",
            "   creating: synthesizer/saved_models/logs-pretrained/taco_pretrained/\n",
            " extracting: synthesizer/saved_models/logs-pretrained/taco_pretrained/checkpoint  \n",
            "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.data-00000-of-00001  \n",
            "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.index  \n",
            "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.meta  \n",
            "   creating: vocoder/saved_models/\n",
            "   creating: vocoder/saved_models/pretrained/\n",
            "  inflating: vocoder/saved_models/pretrained/pretrained.pt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6ufTksHH8CQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import firebase_admin\n",
        "from firebase_admin import credentials\n",
        "from firebase_admin import storage\n",
        "cred = credentials.Certificate('/content/greetup-6a2a3-firebase-adminsdk-ppjba-4905632f7e.json')\n",
        "app = firebase_admin.initialize_app(cred, {\n",
        "  'storageBucket': 'greetup-6a2a3.appspot.com',\n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53CWMIQ-eZ-L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code for recording audio from the browser\n",
        "from IPython.display import Javascript\n",
        "from google.colab import output\n",
        "from base64 import b64decode\n",
        "import IPython\n",
        "import uuid\n",
        "from google.colab import output\n",
        "\n",
        "\n",
        "class InvokeButton(object):\n",
        "  def __init__(self, title, callback):\n",
        "    self._title = title\n",
        "    self._callback = callback\n",
        "\n",
        "  def _repr_html_(self):\n",
        "    from google.colab import output\n",
        "    callback_id = 'button-' + str(uuid.uuid4())\n",
        "    output.register_callback(callback_id, self._callback)\n",
        "\n",
        "    template = \"\"\"<button id=\"{callback_id}\" style=\"cursor:pointer;background-color:#EEEEEE;border-color:#E0E0E0;padding:5px 15px;font-size:14px\">{title}</button>\n",
        "        <script>\n",
        "          document.querySelector(\"#{callback_id}\").onclick = (e) => {{\n",
        "            google.colab.kernel.invokeFunction('{callback_id}', [], {{}})\n",
        "            e.preventDefault();\n",
        "          }};\n",
        "        </script>\"\"\"\n",
        "    html = template.format(title=self._title, callback_id=callback_id)\n",
        "    return html\n",
        "\n",
        "RECORD = \"\"\"\n",
        "const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\n",
        "const b2text = blob => new Promise(resolve => {\n",
        "  const reader = new FileReader()\n",
        "  reader.onloadend = e => resolve(e.srcElement.result)\n",
        "  reader.readAsDataURL(blob)\n",
        "})\n",
        "var record = time => new Promise(async resolve => {\n",
        "  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n",
        "  recorder = new MediaRecorder(stream)\n",
        "  chunks = []\n",
        "  recorder.ondataavailable = e => chunks.push(e.data)\n",
        "  recorder.start()\n",
        "  await sleep(time)\n",
        "  recorder.onstop = async ()=>{\n",
        "    blob = new Blob(chunks)\n",
        "    text = await b2text(blob)\n",
        "    resolve(text)\n",
        "  }\n",
        "  recorder.stop()\n",
        "})\n",
        "\"\"\"\n",
        "\n",
        "def record(sec=3):\n",
        "  display(Javascript(RECORD))\n",
        "  s = output.eval_js('record(%d)' % (sec*1000))\n",
        "  b = b64decode(s.split(',')[1])\n",
        "  with open('audio.wav','wb+') as f:\n",
        "    f.write(b)\n",
        "  return 'audio.wav'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDvZn-k9t3Eu",
        "colab_type": "code",
        "outputId": "cba2e036-648e-4c63-bff6-14f8155522d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "from IPython.display import Audio\n",
        "from IPython.utils import io\n",
        "from synthesizer.inference import Synthesizer\n",
        "from encoder import inference as encoder\n",
        "from vocoder import inference as vocoder\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import librosa\n",
        "encoder_weights = Path(\"encoder/saved_models/pretrained.pt\")\n",
        "vocoder_weights = Path(\"vocoder/saved_models/pretrained/pretrained.pt\")\n",
        "syn_dir = Path(\"synthesizer/saved_models/logs-pretrained/taco_pretrained\")\n",
        "encoder.load_model(encoder_weights)\n",
        "synthesizer = Synthesizer(syn_dir)\n",
        "vocoder.load_model(vocoder_weights)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded encoder \"pretrained.pt\" trained to step 1564501\n",
            "Found synthesizer \"pretrained\" trained to step 278000\n",
            "Building Wave-RNN\n",
            "Trainable Parameters: 4.481M\n",
            "Loading model weights at vocoder/saved_models/pretrained/pretrained.pt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyLdbUfks2lv",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "#@title Deep vocoder\n",
        "def synth(filename,text=\"Donald Trump\"):\n",
        "  #text = \"Donald Trump\" #@param {type:\"string\"}\n",
        "  print(\"Now recording for 10 seconds, say what you will...\")\n",
        "  #record(10)\n",
        "  print(\"Audio recording complete\")\n",
        "  in_fpath = Path(\"/content/\"+filename)\n",
        "  reprocessed_wav = encoder.preprocess_wav(in_fpath)\n",
        "  original_wav, sampling_rate = librosa.load(in_fpath)\n",
        "  preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)\n",
        "  embed = encoder.embed_utterance(preprocessed_wav)\n",
        "  print(\"Synthesizing new audio...\")\n",
        "  with io.capture_output() as captured:\n",
        "    specs = synthesizer.synthesize_spectrograms([text], [embed])\n",
        "  generated_wav = vocoder.infer_waveform(specs[0])\n",
        "  generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode=\"constant\")\n",
        "  audio = Audio(generated_wav, rate=synthesizer.sample_rate)\n",
        "  with open(\"/content/clone_audio.wav\", \"wb\") as fp:\n",
        "        fp.write(audio.data)  \n",
        "  return generated_wav,synthesizer.sample_rate\n",
        "#InvokeButton('Start recording', synth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCYB7qw5n2bZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F75qMhYmeb81",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!/usr/bin/env python\n",
        "\n",
        "import datetime\n",
        "import os\n",
        "import urllib\n",
        "import wget\n",
        "import requests\n",
        "import google.cloud\n",
        "import firebase_admin\n",
        "\n",
        "#proxy = 'http://edcguest:edcguest@172.31.100.14:3128'\n",
        "#os.environ['http_proxy'] = proxy\n",
        "#os.environ['HTTP_PROXY'] = proxy\n",
        "#os.environ['https_proxy'] = proxy\n",
        "#os.environ['HTTPS_PROXY'] = proxy\n",
        "\n",
        "def GetFromCloud(filename):\n",
        "    bucket = storage.bucket(app=app)\n",
        "    blob = bucket.blob(filename)\n",
        "    file_url = blob.generate_signed_url(datetime.timedelta(seconds=300), method='GET')\n",
        "    print(file_url,filename)\n",
        "    r = requests.get(file_url)\n",
        "    print(r.status_code)\n",
        "    if r.status_code != 200:\n",
        "      return False\n",
        "    open(\"/content/\"+filename , 'wb').write(r.content)\n",
        "    return True\n",
        "\n",
        "def SendToCloud(filename):\n",
        "    \n",
        "    bucket = storage.bucket()\n",
        "    imagePath = filename\n",
        "    imageBlob = bucket.blob(\"output.wav\")\n",
        "    imageBlob.upload_from_filename(filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj3zqf_-fEgL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from rev_ai import apiclient, JobStatus\n",
        "from pydub import AudioSegment\n",
        "import wave\n",
        "\n",
        "#proxy = 'http://edcguest:edcguest@172.31.100.14:3128'\n",
        "\n",
        "#os.environ['http_proxy'] = proxy\n",
        "#os.environ['HTTP_PROXY'] = proxy\n",
        "#os.environ['https_proxy'] = proxy\n",
        "#os.environ['HTTPS_PROXY'] = proxy\n",
        "\n",
        "def mergetwoaudio(mylist,end,training_audio):\n",
        "\n",
        "    sound1 = AudioSegment.from_wav(\"/content/\"+training_audio)\n",
        "    \n",
        "    sound2 = AudioSegment.from_wav(\"/content/clone_audio.wav\")\n",
        "\n",
        "    fro = 0.0\n",
        "    to = 0.0\n",
        "    mixed_sound = sound1[:0]\n",
        "    for ts in mylist:\n",
        "        to = ts[0] * 1000\n",
        "        mixed_sound = mixed_sound + sound1[fro:to] + sound2\n",
        "        fro = ts[1] * 1000\n",
        "\n",
        "    end = end * 1000\n",
        "\n",
        "    mixed_sound = mixed_sound + sound1[fro:end]\n",
        "    mixed_sound.export(\"/content/output.wav\", format='wav')\n",
        "\n",
        "\n",
        "\n",
        "def changeAudio(file_path,sample):\n",
        "    access_token = '02UMSGJJclAaCjsV2JhZn2cVfRiFTo4NBlQtKycPT6k5IxaGNumOSU7a33Bc-uaGXjnXKbo8y1IaVjMHOchz6ua68Lb7U'\n",
        "\n",
        "    # Create client with your access token\n",
        "    client = apiclient.RevAiAPIClient(access_token)\n",
        "\n",
        "    file_job = client.submit_job_local_file(filename= file_path, metadata=\"This_is_some_job_metadata\", callback_url=\"\", skip_diarization=False)\n",
        "\n",
        "    job_id = file_job.id\n",
        "\n",
        "    while file_job.status == JobStatus.IN_PROGRESS :\n",
        "        file_job=client.get_job_details(job_id)\n",
        "    mylist = []\n",
        "    file = client.get_transcript_json(job_id)\n",
        "\n",
        "    end_time = 0.0\n",
        "    sample = sample.lower()\n",
        "    for i in file['monologues']:\n",
        "        for j in i['elements']:\n",
        "            if j['type'] == \"text\":\n",
        "                value_string = j[\"value\"].lower()\n",
        "                print(value_string,sample)\n",
        "                if value_string == sample:\n",
        "                    new_tuple = (j[\"ts\"], j[\"end_ts\"])\n",
        "                    mylist.append(new_tuple)\n",
        "                end_time = j[\"end_ts\"]\n",
        "\n",
        "    print(end_time)\n",
        "    return mylist,end_time\n",
        "    \n",
        "def increasesound(speechpath,cloneaudio):\n",
        "    sound=AudioSegment.from_wav(speechpath)\n",
        "    sound1=AudioSegment.from_wav(cloneaudio)\n",
        "    print(sound.dBFS)\n",
        "    print(sound1.dBFS)\n",
        "    print((sound.dBFS-sound1.dBFS))\n",
        "    sound1=sound1+(sound.dBFS-sound1.dBFS)\n",
        "    sound1.export(cloneaudio, format='wav')\n",
        "    print(sound1.dBFS)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aS5D_dAeVoZ",
        "colab_type": "code",
        "outputId": "b3eece3e-4b55-4b06-9bf2-a1f23ff1b602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# This is server running continously\n",
        "import os\n",
        "import time\n",
        "from os import path\n",
        "import shutil\n",
        "from scipy.io.wavfile import write\n",
        "from pydub import AudioSegment\n",
        "# file to be breaked speech.wav\n",
        "#!rm -rf Interface \n",
        "#!git clone https://BhaiChalJa:passwordbhulgaye@github.com/BhaiChalJa/Interface.git\n",
        "while 1 == 1:\n",
        "  if GetFromCloud('input.txt'):      \n",
        "      f= open(\"/content/input.txt\",\"r\")\n",
        "      time.sleep(5)\n",
        "      training_audio = f.readline()\n",
        "      training_audio=training_audio[:-1]\n",
        "      temp=training_audio\n",
        "      training_audio=training_audio+\".mp3\"\n",
        "      GetFromCloud(training_audio)\n",
        "      sound = AudioSegment.from_mp3(\"/content/\"+training_audio)\n",
        "      training_audio=temp+\".wav\"\n",
        "      sound.export(\"/content/\"+training_audio, format=\"wav\")\n",
        "      #training_audio=training_audio[:-3]\n",
        "      #training_audio=training_audio+\"wav\"\n",
        "      training_text = f.readline()\n",
        "      training_text=training_text[:-1]\n",
        "      word_to_be_changed = f.readline();\n",
        "      generated_wav,sample_rate = synth(training_audio,training_text)\n",
        "      print(training_audio)\n",
        "      print(word_to_be_changed)\n",
        "      if word_to_be_changed==\"NULL\":\n",
        "        shutil.move('/content/clone_audio.wav', '/content/output.wav')\n",
        "      else:    \n",
        "        word_to_be_changed = word_to_be_changed[:-1]\n",
        "        increasesound(\"/content/\"+training_audio,\"/content/clone_audio.wav\")\n",
        "        # write('/content/clone_audio.wav', sample_rate, generated_wav)\n",
        "        mylist,end_time = changeAudio(\"/content/\"+training_audio,word_to_be_changed)\n",
        "        mergetwoaudio(mylist,end_time,training_audio)\n",
        "      SendToCloud('/content/output.wav')\n",
        "      break\n",
        "  else:\n",
        "    print(\"File Not Found In System\")\n",
        "    time.sleep(5)\n",
        "\n",
        "\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://storage.googleapis.com/greetup-6a2a3.appspot.com/input.txt?Expires=1581817332&GoogleAccessId=firebase-adminsdk-ppjba%40greetup-6a2a3.iam.gserviceaccount.com&Signature=oRLR1ucEPLXOYGLKvMwE6kmao1CBH5cHbnS7Au6Krb9GtFHRujfr%2BxV6OSlOW0D4cgly2VZYv3xQZJKzBx0%2BKry5Jk8p6dlKZ2OTBcfrxm3lkGQ2miGGKDrDJpRGSrx7xLipIOtO8nqGW1G0WkpCEjB4hqX%2FCA52kOfWbK2NAKEdd4IoDrYAAI0Ij%2BAJhlL80KmQsCAuKuMJIXNCvLCxu5qRO17a9B8HeNORq9%2F5xRtXOKWG0rJqudt%2BiOKO8VVSzDCZnrzNX%2F%2BYJOrEAgWEBxxy1yuO4W2PdG75F8BbvAG3upSQj3Ar8PD9hvHzQttAuUG%2BS9gWsHmlBeXC5%2BUJZg%3D%3D input.txt\n",
            "200\n",
            "https://storage.googleapis.com/greetup-6a2a3.appspot.com/audio_sample.mp3?Expires=1581817338&GoogleAccessId=firebase-adminsdk-ppjba%40greetup-6a2a3.iam.gserviceaccount.com&Signature=PDCsS%2Bvd5syqBlQYmtwXixM9lo4GjGFkCNQVmzs2RTI4aE8xX1uutr3vUDWczzZSTV0gUVibO%2BlH9xIZ3Ge8vrfY73DqbR92FwqP3WX62OuAuqcgDRqQqFr6Ys1gngr5r7nnaKpNZt%2Bxc1qaxu8VRPOMtkqQ6%2FtpCs5fZwguPNgJoFUjCr2Zy0%2FStJqvLhoQqcaoq2SZvUKK3DFcd7HiXhtKGxpxk7FUqbqwFvGUPMhpv1tRaXft5ed5J9NiE2%2B4y2gHsR0KeHjKV%2Fp2IeIVQyC22iBDtEHK4stjfzbKA9oR2nIfkbmrPLSi%2B%2FpOUnzZeVuhYOIOsWf57o%2BmajiKHw%3D%3D audio_sample.mp3\n",
            "200\n",
            "Now recording for 10 seconds, say what you will...\n",
            "Audio recording complete\n",
            "Synthesizing new audio...\n",
            "{| ████████████████ 76000/76800 | Batch Size: 8 | Gen Rate: 9.8kHz | }audio_sample.wav\n",
            "wondering\n",
            "\n",
            "-28.703686152397463\n",
            "-23.269209590481715\n",
            "-5.434476561915748\n",
            "-28.703686152397463\n",
            "no wondering\n",
            "listen wondering\n",
            "this wondering\n",
            "is wondering\n",
            "jason bourne wondering\n",
            "i wondering\n",
            "was wondering\n",
            "wondering wondering\n",
            "when wondering\n",
            "you wondering\n",
            "were wondering\n",
            "going wondering\n",
            "to wondering\n",
            "make wondering\n",
            "this wondering\n",
            "call wondering\n",
            "how wondering\n",
            "did wondering\n",
            "you wondering\n",
            "get wondering\n",
            "this wondering\n",
            "number wondering\n",
            "you wondering\n",
            "didn't wondering\n",
            "actually wondering\n",
            "think wondering\n",
            "i wondering\n",
            "was wondering\n",
            "coming wondering\n",
            "to wondering\n",
            "tutor wondering\n",
            "city wondering\n",
            "did wondering\n",
            "you wondering\n",
            "know wondering\n",
            "i wondering\n",
            "guess wondering\n",
            "not wondering\n",
            "but wondering\n",
            "if wondering\n",
            "it's wondering\n",
            "me wondering\n",
            "you wondering\n",
            "want wondering\n",
            "to wondering\n",
            "talk wondering\n",
            "to wondering\n",
            "perhaps wondering\n",
            "we wondering\n",
            "can wondering\n",
            "arrange wondering\n",
            "a wondering\n",
            "meet wondering\n",
            "where wondering\n",
            "are wondering\n",
            "you wondering\n",
            "now wondering\n",
            "i'm wondering\n",
            "sitting wondering\n",
            "in wondering\n",
            "my wondering\n",
            "office wondering\n",
            "i wondering\n",
            "doubt wondering\n",
            "that wondering\n",
            "okay wondering\n",
            "why wondering\n",
            "would wondering\n",
            "you wondering\n",
            "doubt wondering\n",
            "that wondering\n",
            "if wondering\n",
            "you wondering\n",
            "were wondering\n",
            "in wondering\n",
            "your wondering\n",
            "office wondering\n",
            "right wondering\n",
            "33.01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1T2WQYzV5EvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(\"Done 1 Job\")"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}