core-transcription/translate_transcripts.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://www.assemblyai.com/\">\n",
        "<img width=\"300\"\n",
        "src=\"data:image/png;base64,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\">\n",
        "</a>\n",
        "</img>"
      ],
      "metadata": {
        "id": "5Trj8M34fsvr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Cookbook walks through how to translate AssemblyAI transcripts using a variety of commerical and open-source machine translation models.\n",
        "\n",
        "Choosing a model depends on your use-case and preferences. Here are some considerations you may want to make when choosing a model to use for translation:\n",
        "\n",
        "- Accuracy and Quality of Translation: you should compare the translations from each provider to see which translation you prefer\n",
        "- Language Support: check the supported languages for [Google Translate](https://cloud.google.com/translate/docs/languages), [DeepL](https://www.deepl.com/docs-api/translate-text), and [python-translate](https://github.com/terryyin/translate-python) respectively\n",
        "- Cost: while commercial models usually have a free-tier or trials, they will incur costs eventually\n"
      ],
      "metadata": {
        "id": "w-u2aKZKZUD8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "jt6C5IOts3YI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get started, paste your API token into the empty string below. If you don't already have an API token, you can get one for free [here](https://www.assemblyai.com/dashboard/signup)."
      ],
      "metadata": {
        "id": "S3RWVvPyv-OR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EdjoNNwItpyS"
      },
      "outputs": [],
      "source": [
        "AAI_API_TOKEN = \"\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Make sure not to share this token with anyone** - it is a private key associated uniquely to your account."
      ],
      "metadata": {
        "id": "Lag_DbH5jfq7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we'll install the AssemblyAI Python SDK, which will allow us to easily use LeMUR in just a few lines of code."
      ],
      "metadata": {
        "id": "S3L7C3a7x3UI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install \"assemblyai\""
      ],
      "metadata": {
        "id": "uefvOzgSqoP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, import the `assemblyai` package and set your API token in the settings:"
      ],
      "metadata": {
        "id": "wPpPqxXyygdZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import assemblyai as aai\n",
        "\n",
        "# set the API key\n",
        "aai.settings.api_key = f\"{AAI_API_TOKEN}\""
      ],
      "metadata": {
        "id": "VCtQ3zCerZZg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = aai.TranscriptionConfig(language_detection=True)\n",
        "transcriber = aai.Transcriber(config=config)\n",
        "\n",
        "transcript = transcriber.transcribe('./my-audio.mp3')"
      ],
      "metadata": {
        "id": "m-Lv15tEE58V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Specify the target language for the translation\n"
      ],
      "metadata": {
        "id": "Xeb5HAiwOo93"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "to_lang = 'en'"
      ],
      "metadata": {
        "id": "Kn-VqRfjOorE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get detected language code from the AAI JSON response"
      ],
      "metadata": {
        "id": "wICANSVuOrAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from_lang = transcript.json_response['language_code']"
      ],
      "metadata": {
        "id": "rViCeAhK4rGN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Commercial Models"
      ],
      "metadata": {
        "id": "gdiLOFg5PeOZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Google Translate API\n",
        "\n",
        "https://cloud.google.com/translate/docs/reference/libraries/v2/python\n",
        "\n",
        "Note: you will need a GCP account as well as app credentials to make this API request."
      ],
      "metadata": {
        "id": "J8FXZ6za1Nyz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install google-cloud-translate==2.0.1"
      ],
      "metadata": {
        "id": "4GGwmLFW2Dru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Follow Google's docs on how to generate a credentials JSON file: https://cloud.google.com/docs/authentication/application-default-credentials"
      ],
      "metadata": {
        "id": "sRJt_zw4PJzS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = './translate_creds.json'"
      ],
      "metadata": {
        "id": "vJ_p2mB9Enn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud import translate_v2\n",
        "\n",
        "def translate(target: str, text: str) -> dict:\n",
        "    \"\"\"Translates text into the target language.\n",
        "\n",
        "    Target must be an ISO 639-1 language code.\n",
        "    See https://g.co/cloud/translate/v2/translate-reference#supported_languages\n",
        "    \"\"\"\n",
        "    from google.cloud import translate_v2 as translate\n",
        "\n",
        "    translate_client = translate.Client()\n",
        "\n",
        "    if isinstance(text, bytes):\n",
        "        text = text.decode(\"utf-8\")\n",
        "\n",
        "    # Text can also be a sequence of strings, in which case this method\n",
        "    # will return a sequence of results for each text.\n",
        "    result = translate_client.translate(text, target_language=target)\n",
        "\n",
        "    print(\"Text: {}\".format(result[\"input\"]))\n",
        "    print(\"Translation: {}\".format(result[\"translatedText\"]))\n",
        "    print()\n",
        "\n",
        "    return result\n",
        "\n",
        "for sent in transcript.get_sentences():\n",
        "  translate(to_lang, sent.text)"
      ],
      "metadata": {
        "id": "nMyhv-ws2GKe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e787297-321e-4d47-f791-36bc859f3e49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text: Precisamente sobre este caso, el diario estadounidense New York Times reveló este sábado un conjunto de poco más de 23 mil mensajes de texto inéditos, declaraciones de testigos y documentos con nuevas pistas sobre el caso Ayotzinapa.\n",
            "Translation: Precisely about this case, the American newspaper New York Times revealed this Saturday a set of just over 23 thousand unpublished text messages, witness statements and documents with new clues about the Ayotzinapa case.\n",
            "\n",
            "Text: Entre las nuevas pistas destacan las intercepciones telefónicas de policías, militares y otros funcionarios coludidos con el cártel Guerreros Unidos, mismo que secuestró a 43 estudiantes normalistas la noche del 26 de septiembre de 2014.\n",
            "Translation: Among the new clues are the telephone interceptions of police, military and other officials colluding with the Guerreros Unidos cartel, which kidnapped 43 normalista students on the night of September 26, 2014.\n",
            "\n",
            "Text: El diario agregó que militares que monitoreaban de cerca el secuestro nunca acudieron a socorrer a los estudiantes.\n",
            "Translation: The newspaper added that the military who closely monitored the kidnapping never came to help the students.\n",
            "\n",
            "Text: También había recibido sobornos del cártel.\n",
            "Translation: He had also received bribes from the cartel.\n",
            "\n",
            "Text: De acuerdo con el New York Times, un teniente incluso armó a sicarios vinculados al cártel y, según un testigo, ayudó a la policía a intentar ocultar su participación en este crimen.\n",
            "Translation: According to the New York Times, a lieutenant even armed hitmen linked to the cartel and, according to a witness, helped the police try to hide his participation in this crime.\n",
            "\n",
            "Text: luego de que los estudiantes fueran secuestrados y asesinados.\n",
            "Translation: after the students were kidnapped and murdered.\n",
            "\n",
            "Text: Cabe señalar que a nueve años de lo ocurrido con los 43 normalistas, nadie ha sido sentenciado por ese crimen.\n",
            "Translation: It should be noted that nine years after what happened to the 43 normalistas, no one has been sentenced for that crime.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DeepL API\n",
        "\n",
        "https://www.deepl.com/en/docs-api"
      ],
      "metadata": {
        "id": "30b6EgCf1NNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install deepl"
      ],
      "metadata": {
        "id": "CyjMkSdR1NB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You will need a DeepL account and API token, which can be found here: https://www.deepl.com/pro-api"
      ],
      "metadata": {
        "id": "uOjivdAJTtQU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DEEPL_API_TOKEN = ''"
      ],
      "metadata": {
        "id": "FBf2j7HMJdkB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import deepl\n",
        "\n",
        "def translate(text):\n",
        "    translator = deepl.Translator(DEEPL_API_TOKEN)\n",
        "    result = translator.translate_text(text, target_lang=\"EN-US\") # Note: DeepL requires more formal language code\n",
        "    return result.text\n",
        "\n",
        "# Example usage\n",
        "for sent in transcript.get_sentences():\n",
        "  translated_text = translate(sent.text)\n",
        "  print(\"Text: {}\".format(sent.text))\n",
        "  print(\"Translation: {}\".format(translated_text))\n",
        "  print()"
      ],
      "metadata": {
        "id": "sX-wSNSy1V5F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d24f116a-95e2-4d0e-9570-484a0d3ca276"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text: Precisamente sobre este caso, el diario estadounidense New York Times reveló este sábado un conjunto de poco más de 23 mil mensajes de texto inéditos, declaraciones de testigos y documentos con nuevas pistas sobre el caso Ayotzinapa.\n",
            "Translation: Precisely on this case, the U.S. newspaper New York Times revealed this Saturday a set of just over 23 thousand unpublished text messages, witness statements and documents with new clues about the Ayotzinapa case.\n",
            "\n",
            "Text: Entre las nuevas pistas destacan las intercepciones telefónicas de policías, militares y otros funcionarios coludidos con el cártel Guerreros Unidos, mismo que secuestró a 43 estudiantes normalistas la noche del 26 de septiembre de 2014.\n",
            "Translation: Among the new leads are telephone intercepts of police, military and other officials in collusion with the Guerreros Unidos cartel, the same cartel that kidnapped 43 normalista students on the night of September 26, 2014.\n",
            "\n",
            "Text: El diario agregó que militares que monitoreaban de cerca el secuestro nunca acudieron a socorrer a los estudiantes.\n",
            "Translation: The newspaper added that military personnel closely monitoring the kidnapping never came to the students' aid.\n",
            "\n",
            "Text: También había recibido sobornos del cártel.\n",
            "Translation: He had also received bribes from the cartel.\n",
            "\n",
            "Text: De acuerdo con el New York Times, un teniente incluso armó a sicarios vinculados al cártel y, según un testigo, ayudó a la policía a intentar ocultar su participación en este crimen.\n",
            "Translation: According to the New York Times, a lieutenant even armed hitmen linked to the cartel and, according to a witness, helped the police try to hide their involvement in this crime.\n",
            "\n",
            "Text: luego de que los estudiantes fueran secuestrados y asesinados.\n",
            "Translation: after the students were kidnapped and killed.\n",
            "\n",
            "Text: Cabe señalar que a nueve años de lo ocurrido con los 43 normalistas, nadie ha sido sentenciado por ese crimen.\n",
            "Translation: It should be noted that nine years after what happened to the 43 normalistas, no one has been sentenced for this crime.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Open-Source Models"
      ],
      "metadata": {
        "id": "5vqXoutzP0PF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## `translate-python` Library\n",
        "\n",
        "https://github.com/terryyin/translate-python\n",
        "\n"
      ],
      "metadata": {
        "id": "BY1rL0-N1XE9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install translate"
      ],
      "metadata": {
        "id": "3pGh7cvn1bHi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from translate import Translator\n",
        "\n",
        "def translate(text):\n",
        "    translator = Translator(to_lang=to_lang, from_lang=from_lang)\n",
        "    translation = translator.translate(text)\n",
        "    return translation\n",
        "\n",
        "for sent in transcript.get_sentences():\n",
        "  translated_text = translate(sent.text)\n",
        "  print(\"Text: {}\".format(sent.text))\n",
        "  print(\"Translation: {}\".format(translated_text))\n",
        "  print()"
      ],
      "metadata": {
        "id": "ln5sBteB1bj3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9089d191-5f8b-42d5-8e8e-645293e09cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text: Precisamente sobre este caso, el diario estadounidense New York Times reveló este sábado un conjunto de poco más de 23 mil mensajes de texto inéditos, declaraciones de testigos y documentos con nuevas pistas sobre el caso Ayotzinapa.\n",
            "Translation: On Saturday, the New York Times revealed a set of just over 23,000 unpublished text messages, witness statements and documents with new clues about the Ayotzinapa case.\n",
            "\n",
            "Text: Entre las nuevas pistas destacan las intercepciones telefónicas de policías, militares y otros funcionarios coludidos con el cártel Guerreros Unidos, mismo que secuestró a 43 estudiantes normalistas la noche del 26 de septiembre de 2014.\n",
            "Translation: Among the new clues are the telephone interceptions of police, military and other officials colluding with the Guerreros Unidos cartel, which kidnapped 43 normal students on the night of September 26, 2014.\n",
            "\n",
            "Text: El diario agregó que militares que monitoreaban de cerca el secuestro nunca acudieron a socorrer a los estudiantes.\n",
            "Translation: The newspaper added that military personnel who closely monitored the kidnapping never came to the students' aid.\n",
            "\n",
            "Text: También había recibido sobornos del cártel.\n",
            "Translation: He had also received bribes from the cartel.\n",
            "\n",
            "Text: De acuerdo con el New York Times, un teniente incluso armó a sicarios vinculados al cártel y, según un testigo, ayudó a la policía a intentar ocultar su participación en este crimen.\n",
            "Translation: According to the New York Times, a lieutenant even armed hitmen linked to the cartel and, according to a witness, helped the police try to hide his involvement in this crime.\n",
            "\n",
            "Text: luego de que los estudiantes fueran secuestrados y asesinados.\n",
            "Translation: after the students were kidnapped and killed.\n",
            "\n",
            "Text: Cabe señalar que a nueve años de lo ocurrido con los 43 normalistas, nadie ha sido sentenciado por ese crimen.\n",
            "Translation: It should be noted that nine years after what happened to the 43 normalistas, no one has been sentenced for that crime.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Further Documentation\n",
        "Cookbook: [Translate subtitles](https://github.com/AssemblyAI/cookbook/blob/master/core-transcription/translate_subtitles.ipynb)"
      ],
      "metadata": {
        "id": "b3mdov-N0Pnx"
      }
    }
  ]
}