deepspeech_api_notebook.ipynb

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  "metadata": {
    "colab": {
      "name": "deepspeech_api_notebook.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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        },
        "id": "lVELylqKehtR",
        "outputId": "6d28b134-f169-478d-dda4-f94bf5ce7225"
      },
      "source": [
        "!pip install ipython-autotime\n",
        "\n",
        "%load_ext autotime"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ipython-autotime\n",
            "  Downloading ipython_autotime-0.3.1-py2.py3-none-any.whl (6.8 kB)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from ipython-autotime) (5.5.0)\n",
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            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython->ipython-autotime) (0.7.0)\n",
            "Installing collected packages: ipython-autotime\n",
            "Successfully installed ipython-autotime-0.3.1\n",
            "time: 2.11 ms (started: 2021-08-26 15:45:49 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpmKFBKjWHo-",
        "outputId": "e19101e0-6c18-475e-e0f0-381bc8bf3960"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import shlex\n",
        "import subprocess\n",
        "import sys\n",
        "import wave\n",
        "\n",
        "!pip install deepspeech \n",
        "# from deepspeech import Model, printVersions\n",
        "from deepspeech import *\n",
        "from timeit import default_timer as timer\n",
        "\n",
        "# audio converters\n",
        "!apt update && apt-get install ffmpeg mpg123\n",
        "\n",
        "# sox package for adjusting sample rate.\n",
        "!apt-get install libsox-fmt-all libsox-dev sox\n",
        "\n",
        "# neural network model for acoustic recognition\n",
        "!wget -O - https://github.com/mozilla/DeepSpeech/releases/download/v0.3.0/deepspeech-0.3.0-models.tar.gz | tar xvfz -"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from deepspeech) (1.19.5)\n",
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            "Successfully installed deepspeech-0.9.3\n",
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            "--2021-08-26 15:46:36--  https://github.com/mozilla/DeepSpeech/releases/download/v0.3.0/deepspeech-0.3.0-models.tar.gz\n",
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            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/60273704/0d729e00-cbb3-11e8-951e-bdf28484f70f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210826%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210826T154636Z&X-Amz-Expires=300&X-Amz-Signature=f87b55f2a613c3355056002904eb2818b9c6062f4432ca824f8f39d89cc35c0a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=60273704&response-content-disposition=attachment%3B%20filename%3Ddeepspeech-0.3.0-models.tar.gz&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-08-26 15:46:36--  https://github-releases.githubusercontent.com/60273704/0d729e00-cbb3-11e8-951e-bdf28484f70f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210826%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210826T154636Z&X-Amz-Expires=300&X-Amz-Signature=f87b55f2a613c3355056002904eb2818b9c6062f4432ca824f8f39d89cc35c0a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=60273704&response-content-disposition=attachment%3B%20filename%3Ddeepspeech-0.3.0-models.tar.gz&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1975437979 (1.8G) [application/octet-stream]\n",
            "Saving to: ‘STDOUT’\n",
            "\n",
            "-                     0%[                    ]       0  --.-KB/s               models/\n",
            "models/lm.binary\n",
            "-                    77%[==============>     ]   1.43G  25.8MB/s    eta 20s    models/output_graph.rounded.pb\n",
            "-                    79%[==============>     ]   1.47G  22.2MB/s    eta 18s    models/output_graph.pbmm\n",
            "-                    88%[================>   ]   1.63G  24.6MB/s    eta 10s    models/output_graph.pb\n",
            "-                    97%[==================> ]   1.79G  26.3MB/s    eta 2s     models/trie\n",
            "-                    98%[==================> ]   1.80G  13.1MB/s    eta 2s     models/alphabet.txt\n",
            "models/output_graph.rounded.pbmm\n",
            "-                   100%[===================>]   1.84G  14.0MB/s    in 86s     \n",
            "\n",
            "2021-08-26 15:48:02 (21.9 MB/s) - written to stdout [1975437979/1975437979]\n",
            "\n",
            "time: 1min 59s (started: 2021-08-26 15:46:03 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zex6CckIWRLm",
        "outputId": "ac68cff8-aad3-4ed9-aa20-4d584200d529"
      },
      "source": [
        "def convert_samplerate(audio_path):\n",
        "    sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate 16000 --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(audio_path)\n",
        "    try:\n",
        "        output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE)\n",
        "    except subprocess.CalledProcessError as e:\n",
        "        raise RuntimeError('SoX returned non-zero status: {}'.format(e.stderr))\n",
        "    except OSError as e:\n",
        "        raise OSError(e.errno, 'SoX not found, use 16kHz files or install it: {}'.format(e.strerror))\n",
        "\n",
        "    return 16000, np.frombuffer(output, np.int16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 10.9 ms (started: 2021-08-26 15:49:02 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "id": "FyU7qoaNWTIp",
        "outputId": "e7cb6f43-401a-4000-bc6f-301bddd70682"
      },
      "source": [
        "# upload mp3 audio file.\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "for audio in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "         name=audio, length=len(uploaded[audio])))\n",
        "\n",
        "os.rename(audio, 'speech.mp3')\n",
        "audio = 'speech.wav'\n",
        "\n",
        "# convert to wav file.  \n",
        "!ffmpeg -i speech.mp3 -vn -acodec pcm_s16le -ac 1 -ar 16000 -f wav speech.wav\n",
        "#!mpg123 -w speech.wav speech.mp3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fc73ca52-0f91-4ab2-8b80-7301aff4159f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fc73ca52-0f91-4ab2-8b80-7301aff4159f\">\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",
          "text": [
            "Saving Self-aware Video it knows its own YouTube Video ID.mp3 to Self-aware Video it knows its own YouTube Video ID (1).mp3\n",
            "User uploaded file \"Self-aware Video it knows its own YouTube Video ID.mp3\" with length 4328026 bytes\n",
            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
            "  libavutil      55. 78.100 / 55. 78.100\n",
            "  libavcodec     57.107.100 / 57.107.100\n",
            "  libavformat    57. 83.100 / 57. 83.100\n",
            "  libavdevice    57. 10.100 / 57. 10.100\n",
            "  libavfilter     6.107.100 /  6.107.100\n",
            "  libavresample   3.  7.  0 /  3.  7.  0\n",
            "  libswscale      4.  8.100 /  4.  8.100\n",
            "  libswresample   2.  9.100 /  2.  9.100\n",
            "  libpostproc    54.  7.100 / 54.  7.100\n",
            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'speech.mp3':\n",
            "  Metadata:\n",
            "    major_brand     : mp42\n",
            "    minor_version   : 0\n",
            "    compatible_brands: isommp42\n",
            "    creation_time   : 2019-12-24T00:44:26.000000Z\n",
            "  Duration: 00:02:49.18, start: 0.000000, bitrate: 204 kb/s\n",
            "    Stream #0:0(und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 73 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
            "    Metadata:\n",
            "      creation_time   : 2019-12-24T00:44:26.000000Z\n",
            "      handler_name    : ISO Media file produced by Google Inc. Created on: 12/23/2019.\n",
            "    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 127 kb/s (default)\n",
            "    Metadata:\n",
            "      creation_time   : 2019-12-24T00:44:26.000000Z\n",
            "      handler_name    : ISO Media file produced by Google Inc. Created on: 12/23/2019.\n",
            "Stream mapping:\n",
            "  Stream #0:1 -> #0:0 (aac (native) -> pcm_s16le (native))\n",
            "Press [q] to stop, [?] for help\n",
            "Output #0, wav, to 'speech.wav':\n",
            "  Metadata:\n",
            "    major_brand     : mp42\n",
            "    minor_version   : 0\n",
            "    compatible_brands: isommp42\n",
            "    ISFT            : Lavf57.83.100\n",
            "    Stream #0:0(und): Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s (default)\n",
            "    Metadata:\n",
            "      creation_time   : 2019-12-24T00:44:26.000000Z\n",
            "      handler_name    : ISO Media file produced by Google Inc. Created on: 12/23/2019.\n",
            "      encoder         : Lavc57.107.100 pcm_s16le\n",
            "size=    5287kB time=00:02:49.18 bitrate= 256.0kbits/s speed= 513x    \n",
            "video:0kB audio:5287kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.001441%\n",
            "time: 29.9 s (started: 2021-08-26 15:49:05 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utrU3Ul7KK0G"
      },
      "source": [
        "# Mozilla DeepSpeech API Exploration\n",
        "\n",
        "Mozilla released [DeepSpeech 0.8.2](https://github.com/mozilla/DeepSpeech/releases/tag/v0.8.2) with [APIs in C, Java, .NET, Python, and JavaScript](https://deepspeech.readthedocs.io/en/v0.8.2/Python-API.html).\n",
        "\n",
        "From Colab menu, select: **Runtime** > **Change runtime type**, and verify that it is set to Python3, and select GPU if you want to try out GPU version.\n",
        "\n",
        "You can install DeepSpeech with pip (make it deepspeech-gpu==0.8.2 if you are using GPU in colab runtime):\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iemeuv-jKR3P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1455b196-f54e-40d0-a3f9-73970e50b694"
      },
      "source": [
        "# !python --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2 ms (started: 2021-08-26 15:49:35 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zABV65yhNJ0M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8ae67f4-6ff9-4ea7-face-3147b68a5559"
      },
      "source": [
        "# !pip install deepspeech==0.8.2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.18 ms (started: 2021-08-26 15:49:35 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbWIPOUwNVyI"
      },
      "source": [
        "## Download Models and Audio Files\n",
        "\n",
        "Mozilla has released models for US English, we will use those in this code lab.\n",
        "\n",
        "1. **Download the models:**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Z0dSoLJPKKY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "894d9c2e-9c53-42e9-9562-5ae1195d60cb"
      },
      "source": [
        "!mkdir deepspeech-0.8.2-models"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 126 ms (started: 2021-08-26 15:49:35 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpOO5HeBPZcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf775691-e633-4f1b-ed08-273d7852bbab"
      },
      "source": [
        "!wget https://github.com/mozilla/DeepSpeech/releases/download/v0.8.1/deepspeech-0.8.1-models.pbmm\n",
        "!wget https://github.com/mozilla/DeepSpeech/releases/download/v0.8.1/deepspeech-0.8.1-models.scorer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-26 15:49:35--  https://github.com/mozilla/DeepSpeech/releases/download/v0.8.1/deepspeech-0.8.1-models.pbmm\n",
            "Resolving github.com (github.com)... 192.30.255.113\n",
            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/60273704/ae836480-db4e-11ea-9bf7-2d4ba9ea96f3?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210826%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210826T154935Z&X-Amz-Expires=300&X-Amz-Signature=26497510d24fcd6f7930ced5c0270b1ff47bce919678db2aec41ef5e78f18f6d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=60273704&response-content-disposition=attachment%3B%20filename%3Ddeepspeech-0.8.1-models.pbmm&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-08-26 15:49:35--  https://github-releases.githubusercontent.com/60273704/ae836480-db4e-11ea-9bf7-2d4ba9ea96f3?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210826%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210826T154935Z&X-Amz-Expires=300&X-Amz-Signature=26497510d24fcd6f7930ced5c0270b1ff47bce919678db2aec41ef5e78f18f6d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=60273704&response-content-disposition=attachment%3B%20filename%3Ddeepspeech-0.8.1-models.pbmm&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.111.154, 185.199.109.154, 185.199.108.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.111.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 188915984 (180M) [application/octet-stream]\n",
            "Saving to: ‘deepspeech-0.8.1-models.pbmm’\n",
            "\n",
            "deepspeech-0.8.1-mo 100%[===================>] 180.16M  28.3MB/s    in 6.6s    \n",
            "\n",
            "2021-08-26 15:49:42 (27.1 MB/s) - ‘deepspeech-0.8.1-models.pbmm’ saved [188915984/188915984]\n",
            "\n",
            "--2021-08-26 15:49:42--  https://github.com/mozilla/DeepSpeech/releases/download/v0.8.1/deepspeech-0.8.1-models.scorer\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/60273704/dd99d600-db4e-11ea-9f56-f897b9a8dc2a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210826%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210826T154942Z&X-Amz-Expires=300&X-Amz-Signature=494a1a0348701d860645192a0439a250e191b5487e4d079920e7c4cea4314261&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=60273704&response-content-disposition=attachment%3B%20filename%3Ddeepspeech-0.8.1-models.scorer&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-08-26 15:49:42--  https://github-releases.githubusercontent.com/60273704/dd99d600-db4e-11ea-9f56-f897b9a8dc2a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210826%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210826T154942Z&X-Amz-Expires=300&X-Amz-Signature=494a1a0348701d860645192a0439a250e191b5487e4d079920e7c4cea4314261&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=60273704&response-content-disposition=attachment%3B%20filename%3Ddeepspeech-0.8.1-models.scorer&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.111.154, 185.199.109.154, 185.199.108.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.111.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 953363776 (909M) [application/octet-stream]\n",
            "Saving to: ‘deepspeech-0.8.1-models.scorer’\n",
            "\n",
            "deepspeech-0.8.1-mo 100%[===================>] 909.20M  39.0MB/s    in 21s     \n",
            "\n",
            "2021-08-26 15:50:03 (44.3 MB/s) - ‘deepspeech-0.8.1-models.scorer’ saved [953363776/953363776]\n",
            "\n",
            "time: 28.2 s (started: 2021-08-26 15:49:35 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxa0os-3OA1X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c3ff2fc-ecdb-4567-e279-450726e5ec80"
      },
      "source": [
        "!mv deepspeech-0.8.1-models.pbmm deepspeech-0.8.1-models.scorer deepspeech-0.8.2-models/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 118 ms (started: 2021-08-26 15:50:03 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Fu521L7QAxa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2044b2a-e4b6-4837-c347-d605bc9a9bef"
      },
      "source": [
        "!ls -l deepspeech-0.8.2-models/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1115516\n",
            "-rw-r--r-- 1 root root 188915984 Aug 10  2020 deepspeech-0.8.1-models.pbmm\n",
            "-rw-r--r-- 1 root root 953363776 Aug 10  2020 deepspeech-0.8.1-models.scorer\n",
            "time: 117 ms (started: 2021-08-26 15:50:03 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_g5hZVWXO1wl"
      },
      "source": [
        "2. **Download audio data files**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8r7m0zlO_BJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13f9716c-db54-413e-bbc3-e0fa4060fb1a"
      },
      "source": [
        "# !curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/audio-0.8.2.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.06 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4yZaht2PH_5"
      },
      "source": [
        "4. **Unzip audio files**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82DLg4JpPOVX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3629c725-6b77-4f41-cae5-ad1167a9273c"
      },
      "source": [
        "# !tar -xvzf audio-0.8.2.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.07 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zExydudVPU4r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8ac6b73-11ef-4d5a-ec57-a95a3142db9e"
      },
      "source": [
        "# !ls -l ./audio/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.17 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIiwclaXPzfm"
      },
      "source": [
        "5. **Test that it all works**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlXV9XlpP8k2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfbb0e71-94c4-4182-e230-9bfd87d1b9c2"
      },
      "source": [
        "# !deepspeech --model deepspeech-0.8.2-models/deepspeech-0.8.1-models.pbmm --scorer deepspeech-0.8.2-models/deepspeech-0.8.1-models.scorer --audio ./audio/2830-3980-0043.wav"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.67 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSENx8jhQGIS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfce52d2-4b11-4cc1-f376-db3eb4ccd9e3"
      },
      "source": [
        "# !deepspeech --model deepspeech-0.8.2-models/deepspeech-0.8.1-models.pbmm --scorer deepspeech-0.8.2-models/deepspeech-0.8.1-models.scorer --audio ./audio/4507-16021-0012.wav"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.14 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSAovXUJQLjP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b1d2851-faf5-4b0f-f24d-ffd758905e51"
      },
      "source": [
        "# !deepspeech --model deepspeech-0.8.2-models/deepspeech-0.8.1-models.pbmm --scorer deepspeech-0.8.2-models/deepspeech-0.8.1-models.scorer --audio ./audio/8455-210777-0068.wav"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.63 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZhiMZtSUZBX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62a25bf1-7aa8-4c8a-dd88-a28ce5466f32"
      },
      "source": [
        "!deepspeech --help"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "usage: deepspeech [-h] --model MODEL [--scorer SCORER] --audio AUDIO\n",
            "                  [--beam_width BEAM_WIDTH] [--lm_alpha LM_ALPHA]\n",
            "                  [--lm_beta LM_BETA] [--version] [--extended] [--json]\n",
            "                  [--candidate_transcripts CANDIDATE_TRANSCRIPTS]\n",
            "                  [--hot_words HOT_WORDS]\n",
            "\n",
            "Running DeepSpeech inference.\n",
            "\n",
            "optional arguments:\n",
            "  -h, --help            show this help message and exit\n",
            "  --model MODEL         Path to the model (protocol buffer binary file)\n",
            "  --scorer SCORER       Path to the external scorer file\n",
            "  --audio AUDIO         Path to the audio file to run (WAV format)\n",
            "  --beam_width BEAM_WIDTH\n",
            "                        Beam width for the CTC decoder\n",
            "  --lm_alpha LM_ALPHA   Language model weight (lm_alpha). If not specified,\n",
            "                        use default from the scorer package.\n",
            "  --lm_beta LM_BETA     Word insertion bonus (lm_beta). If not specified, use\n",
            "                        default from the scorer package.\n",
            "  --version             Print version and exits\n",
            "  --extended            Output string from extended metadata\n",
            "  --json                Output json from metadata with timestamp of each word\n",
            "  --candidate_transcripts CANDIDATE_TRANSCRIPTS\n",
            "                        Number of candidate transcripts to include in JSON\n",
            "                        output\n",
            "  --hot_words HOT_WORDS\n",
            "                        Hot-words and their boosts.\n",
            "time: 323 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJT6z1-uQQZ1"
      },
      "source": [
        "Examine the output of the last three commands, and you will see results *“experience proof less”*, *“why should one halt on the way”*, and *“your power is sufficient i said”* respectively. You are all set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p61UrYSvQrOd"
      },
      "source": [
        "# DeepSpeech API\n",
        "\n",
        "1.   **Import deepspeech**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKwSvpvaRFIe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21356c56-1069-43b4-b0e0-b2f8575a5c95"
      },
      "source": [
        "import deepspeech"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 940 µs (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xqd6bQ_gRPOB"
      },
      "source": [
        "2. **Create a model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKDVOmbFRR1A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c7a2243-6571-4cdd-a565-cf4af592c90a"
      },
      "source": [
        "model_file_path = 'deepspeech-0.8.2-models/deepspeech-0.8.1-models.pbmm'\n",
        "model = deepspeech.Model(model_file_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 35.1 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNRxsb2zRgeJ"
      },
      "source": [
        "3. **Add scorer and other parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FRX1EvDRnLH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "863d3aef-8e76-4bfc-f55f-6dba5554256a"
      },
      "source": [
        "scorer_file_path = 'deepspeech-0.8.2-models/deepspeech-0.8.1-models.scorer'\n",
        "model.enableExternalScorer(scorer_file_path)\n",
        "\n",
        "lm_alpha = 0.75\n",
        "lm_beta = 1.85\n",
        "model.setScorerAlphaBeta(lm_alpha, lm_beta)\n",
        "\n",
        "beam_width = 500\n",
        "model.setBeamWidth(beam_width)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "stream",
          "text": [
            "time: 9.77 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWbHnlwCRuDo"
      },
      "source": [
        "## Batch API\n",
        "\n",
        "1.   **Read an input wav file**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRshwTMoSFEL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24f84eb2-683b-4b67-a183-6084717d4b8b"
      },
      "source": [
        "# import wave\n",
        "# filename = 'audio/8455-210777-0068.wav'\n",
        "# w = wave.open(filename, 'r')\n",
        "# rate = w.getframerate()\n",
        "# frames = w.getnframes()\n",
        "# buffer = w.readframes(frames)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.03 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kWWza8WMrys",
        "outputId": "70e59248-7c33-41e9-e434-6922665f32c6"
      },
      "source": [
        "import wave\n",
        "filename = 'speech.wav'\n",
        "w = wave.open(filename, 'r')\n",
        "rate = w.getframerate()\n",
        "frames = w.getnframes()\n",
        "buffer = w.readframes(frames)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 10.1 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cAowLS39SNC_"
      },
      "source": [
        "Checkout sample rate and buffer type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHvatdmxSYGu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5efa7e3f-a14a-4656-fb0f-1521eae06c12"
      },
      "source": [
        "print(rate)\n",
        "print(model.sampleRate())\n",
        "print(str(type(buffer)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16000\n",
            "16000\n",
            "<class 'bytes'>\n",
            "time: 5.37 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOhbO3iTS3ft"
      },
      "source": [
        "As you can see that the speech sample rate of the wav file is 16000hz, same as the model’s sample rate. But the buffer is a byte array, whereas DeepSpeech model expects 16-bit int array.\n",
        "\n",
        "2.  **Convert byte array buffer to int16 array**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYXF6AU2S8m2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88381903-76f1-4920-dffc-d5fbc4c79d4a"
      },
      "source": [
        "import numpy as np\n",
        "data16 = np.frombuffer(buffer, dtype=np.int16)\n",
        "print(str(type(data16)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "time: 4.08 ms (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyIxzx1zTVFp"
      },
      "source": [
        "3.  **Run speech-to-text in batch mode to get the text**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdzZteC7TZDP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "038c5c72-1392-4f83-97a4-b6440d2432f1"
      },
      "source": [
        "text = model.stt(data16)\n",
        "print(text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "so as you know each huge video has an idea in the our l it identifies the video its unique for each video and its determined randomly when you appledale so how do i know that this video has the idea j seven zero a a nine eight r t h c i hope your intrigue now rennes interesting especially for computers because there is not necessarily anything random about them we tend to think about them as being deterministic machines so in terms of security for example in cryptography it's a huge challenge to get a good random source if somebody could predict a random output and that often breaks cryptography now the tube idea is not gripped for graphically important but it's a nice proof of concept so did he predict the idea actually now i have used something else so one day i noticed that the idea is actually known during your applot you see right there and you see it in your videos over for you so the fire is not yet all up loaded but you already know at the idea it is a sign and this allows us to perform a small sevier i basically taking a huge bubble coaxable that uses the google api to unload a vireo and a modified the source code of that in order to switch out the file whenever i want the means i can start out with the introite let that upland very very very slowly and in the mean time i look up the idea got a sign and i quickly record and editors idea into a short clip i render that and i specifically used the impenitent iner because it's not a very complicated fire forman and it allows you to concatenate bilious simply by concatenating the raw bites of the file you don't need to run a special algorithm that merges the video files this way i can just at some point tell the modifier to switch out the file and let it continue a blow the new file that contains the known id pretty neat her i thought that a pretty cool trick president programming and artist i can really recommend you to subscribe and check out some of my playlist for example i think the point bent three series where we harken that was built to be hacked is really fun by by\n",
            "time: 2min 6s (started: 2021-08-26 15:50:04 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qypyXTZ9hurQ",
        "outputId": "eddab8f1-7803-4a07-ffac-0b92eb695b8e"
      },
      "source": [
        "file = open(\"transcribe.txt\", \"w\")\n",
        "file.write(text)\n",
        "\n",
        "file.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.49 ms (started: 2021-08-26 15:52:10 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUCXp-5uTh0L"
      },
      "source": [
        "## Streaming API\n",
        "\n",
        "Now let’s accomplish the same using streaming API. It consists of 3 steps: open session, feed data, close session.\n",
        "\n",
        "1.  **Open a streaming session**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMSQ2VYCTyao",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad3d7960-1e4d-4758-a8a7-bdf37137b4cc"
      },
      "source": [
        "ds_stream = model.createStream()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.22 ms (started: 2021-08-26 15:57:42 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YK4QDAZtT3QZ"
      },
      "source": [
        "2.  **Repeatedly feed chunks of speech buffer, and get interim results if desired**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScS6c2QQT72-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc84c61b-cb0f-4fc4-d688-bca8ca42ed86"
      },
      "source": [
        "buffer_len = len(buffer)\n",
        "offset = 0\n",
        "batch_size = 16384\n",
        "text = ''\n",
        "while offset < buffer_len:\n",
        "    end_offset = offset + batch_size\n",
        "    chunk = buffer[offset:end_offset]\n",
        "    data16 = np.frombuffer(chunk, dtype=np.int16)\n",
        "    ds_stream.feedAudioContent(data16)\n",
        "    text = ds_stream.intermediateDecode()\n",
        "    # print(text)\n",
        "    offset = end_offset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2min (started: 2021-08-26 15:57:43 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeV7x1NgUK-p"
      },
      "source": [
        "3.  **Close stream and get the final result**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aS0WtnF5UM4n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29636fad-ee60-4df4-bd5a-38838d08c20a"
      },
      "source": [
        "text_fin = ds_stream.finishStream()\n",
        "print(text_fin)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "so as you know each huge video has an idea in the our l it identifies the video its unique for each video and its determined randomly when you appledale so how do i know that this video has the idea j seven zero a a nine eight r t h c i hope your intrigue now rennes interesting especially for computers because there is not necessarily anything random about them we tend to think about them as being deterministic machines so in terms of security for example in cryptography it's a huge challenge to get a good random source if somebody could predict a random output and that often breaks cryptography now the tube idea is not gripped for graphically important but it's a nice proof of concept so did he predict the idea actually now i have used something else so one day i noticed that the idea is actually known during your applot you see right there and you see it in your videos over for you so the fire is not yet all up loaded but you already know at the idea it is a sign and this allows us to perform a small sevier i basically taking a huge bubble coaxable that uses the google api to unload a vireo and a modified the source code of that in order to switch out the file whenever i want the means i can start out with the introite let that upland very very very slowly and in the mean time i look up the idea got a sign and i quickly record and editors idea into a short clip i render that and i specifically used the impenitent iner because it's not a very complicated fire forman and it allows you to concatenate bilious simply by concatenating the raw bites of the file you don't need to run a special algorithm that merges the video files this way i can just at some point tell the modifier to switch out the file and let it continue a blow the new file that contains the known id pretty neat her i thought that a pretty cool trick president programming and artist i can really recommend you to subscribe and check out some of my playlist for example i think the point bent three series where we harken that was built to be hacked is really fun by by\n",
            "time: 258 ms (started: 2021-08-26 15:54:12 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-vbd5CmUmsY"
      },
      "source": [
        "Verify that the output is same as as the batch API output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "keEJIfs6nWmd",
        "outputId": "243e35ce-cbef-4a50-d5f8-0bb98f31de39"
      },
      "source": [
        "file = open(\"transcribe_API.txt\", \"w\")\n",
        "file.write(text_fin)\n",
        "\n",
        "file.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 4.47 ms (started: 2021-08-26 15:54:13 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVNGdkq0fV-n"
      },
      "source": [
        "# Recap\n",
        "\n",
        "DeepSpeech has two modes: batch and streaming. First step is to create a model object, and then either call `stt()` or `feedAudioContnet()` to transcribe audio to text.\n",
        "<br/>\n",
        "\n",
        "---\n"
      ]
    }
  ]
}