notebook/agentchat_video_transcript_translate_with_whisper.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "a5b4540e-4987-4774-9305-764c3133e953",
   "metadata": {},
   "source": [
    "# Translating Video audio using Whisper and GPT-3.5-turbo\n",
    "\n",
    "In this notebook, we demonstrate how to use whisper and GPT-3.5-turbo with `AssistantAgent` and `UserProxyAgent` to recognize and translate\n",
    "the speech sound from a video file and add the timestamp like a subtitle file based on [agentchat_function_call.ipynb](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_function_call.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd644cc-2b14-4700-8b1d-959fb2e9acb0",
   "metadata": {},
   "source": [
    "## Requirements\n",
    "\n",
    "````{=mdx}\n",
    ":::info Requirements\n",
    "Some extra dependencies are needed for this notebook, which can be installed via pip:\n",
    "\n",
    "```bash\n",
    "pip install autogen[openai] openai-whisper\n",
    "```\n",
    "\n",
    "For more information, please refer to the [installation guide](https://docs.ag2.ai/docs/user-guide/basic-concepts/installing-ag2).\n",
    ":::\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bdeb0b-c4b6-4dec-97d2-d84f09cffa00",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "It is recommended to store your OpenAI API key in the environment variable. For example, store it in `OPENAI_API_KEY`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26d1ae87-f007-4286-a56a-dcf68abf9393",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "config_list = [\n",
    "    {\n",
    "        \"model\": \"gpt-4\",\n",
    "        \"api_key\": os.getenv(\"OPENAI_API_KEY\"),\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324fec65-ab23-45db-a7a8-0aaf753fe19c",
   "metadata": {},
   "source": [
    "````{=mdx}\n",
    ":::tip\n",
    "Learn more about configuring LLMs for agents [here](https://docs.ag2.ai/docs/user-guide/basic-concepts/llm-configuration/llm-configuration).\n",
    ":::\n",
    "````\n",
    "\n",
    "## Example and Output\n",
    "Below is an example of speech recognition from a [Peppa Pig cartoon video clip](https://drive.google.com/file/d/1QY0naa2acHw2FuH7sY3c-g2sBLtC2Sv4/view?usp=drive_link) originally in English and translated into Chinese.\n",
    "'FFmpeg' does not support online files. To run the code on the example video, you need to download the example video locally. You can change `your_file_path` to your local video file path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3e691b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Annotated, Any, List\n",
    "\n",
    "import whisper\n",
    "from openai import OpenAI\n",
    "\n",
    "import autogen\n",
    "\n",
    "source_language = \"English\"\n",
    "target_language = \"Chinese\"\n",
    "key = os.getenv(\"OPENAI_API_KEY\")\n",
    "target_video = \"your_file_path\"\n",
    "\n",
    "assistant = autogen.AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    system_message=\"For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.\",\n",
    "    llm_config={\"config_list\": config_list, \"timeout\": 120},\n",
    ")\n",
    "\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"user_proxy\",\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\") and x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
    "    human_input_mode=\"NEVER\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    code_execution_config={},\n",
    ")\n",
    "\n",
    "\n",
    "def translate_text(input_text, source_language, target_language):\n",
    "    client = OpenAI(api_key=key)\n",
    "\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": f\"Directly translate the following {source_language} text to a pure {target_language} \"\n",
    "                f\"video subtitle text without additional explanation.: '{input_text}'\",\n",
    "            },\n",
    "        ],\n",
    "        max_tokens=1500,\n",
    "    )\n",
    "\n",
    "    # Correctly accessing the response content\n",
    "    translated_text = response.choices[0].message.content if response.choices else None\n",
    "    return translated_text\n",
    "\n",
    "\n",
    "@user_proxy.register_for_execution()\n",
    "@assistant.register_for_llm(description=\"using translate_text function to translate the script\")\n",
    "def translate_transcript(\n",
    "    source_language: Annotated[str, \"Source language\"], target_language: Annotated[str, \"Target language\"]\n",
    ") -> str:\n",
    "    with open(\"transcription.txt\") as f:\n",
    "        lines = f.readlines()\n",
    "\n",
    "    translated_transcript = []\n",
    "\n",
    "    for line in lines:\n",
    "        # Split each line into timestamp and text parts\n",
    "        parts = line.strip().split(\": \")\n",
    "        if len(parts) == 2:\n",
    "            timestamp, text = parts[0], parts[1]\n",
    "            # Translate only the text part\n",
    "            translated_text = translate_text(text, source_language, target_language)\n",
    "            # Reconstruct the line with the translated text and the preserved timestamp\n",
    "            translated_line = f\"{timestamp}: {translated_text}\"\n",
    "            translated_transcript.append(translated_line)\n",
    "        else:\n",
    "            # If the line doesn't contain a timestamp, add it as is\n",
    "            translated_transcript.append(line.strip())\n",
    "\n",
    "    return \"\\n\".join(translated_transcript)\n",
    "\n",
    "\n",
    "@user_proxy.register_for_execution()\n",
    "@assistant.register_for_llm(description=\"recognize the speech from video and transfer into a txt file\")\n",
    "def recognize_transcript_from_video(filepath: Annotated[str, \"path of the video file\"]) -> List[dict[str, Any]]:\n",
    "    try:\n",
    "        # Load model\n",
    "        model = whisper.load_model(\"small\")\n",
    "\n",
    "        # Transcribe audio with detailed timestamps\n",
    "        result = model.transcribe(filepath, verbose=True)\n",
    "\n",
    "        # Initialize variables for transcript\n",
    "        transcript = []\n",
    "        sentence = \"\"\n",
    "        start_time = 0\n",
    "\n",
    "        # Iterate through the segments in the result\n",
    "        for segment in result[\"segments\"]:\n",
    "            # If new sentence starts, save the previous one and reset variables\n",
    "            if segment[\"start\"] != start_time and sentence:\n",
    "                transcript.append({\n",
    "                    \"sentence\": sentence.strip() + \".\",\n",
    "                    \"timestamp_start\": start_time,\n",
    "                    \"timestamp_end\": segment[\"start\"],\n",
    "                })\n",
    "                sentence = \"\"\n",
    "                start_time = segment[\"start\"]\n",
    "\n",
    "            # Add the word to the current sentence\n",
    "            sentence += segment[\"text\"] + \" \"\n",
    "\n",
    "        # Add the final sentence\n",
    "        if sentence:\n",
    "            transcript.append({\n",
    "                \"sentence\": sentence.strip() + \".\",\n",
    "                \"timestamp_start\": start_time,\n",
    "                \"timestamp_end\": result[\"segments\"][-1][\"end\"],\n",
    "            })\n",
    "\n",
    "        # Save the transcript to a file\n",
    "        with open(\"transcription.txt\", \"w\") as file:\n",
    "            for item in transcript:\n",
    "                sentence = item[\"sentence\"]\n",
    "                start_time, end_time = item[\"timestamp_start\"], item[\"timestamp_end\"]\n",
    "                file.write(f\"{start_time}s to {end_time}s: {sentence}\\n\")\n",
    "\n",
    "        return transcript\n",
    "\n",
    "    except FileNotFoundError:\n",
    "        return \"The specified audio file could not be found.\"\n",
    "    except Exception as e:\n",
    "        return f\"An unexpected error occurred: {e!s}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072de235",
   "metadata": {},
   "source": [
    "Now, start the chat:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed549b75-b4ea-4ec5-8c0b-a15e93ffd618",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_proxy.initiate_chat(\n",
    "    assistant,\n",
    "    message=f\"For the video located in {target_video}, recognize the speech and transfer it into a script file, \"\n",
    "    f\"then translate from {source_language} text to a {target_language} video subtitle text. \",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeea924a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "front_matter": {
   "description": "Use tools to extract and translate the transcript of a video file.",
   "tags": [
    "whisper",
    "multimodal",
    "tool/function"
   ]
  },
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