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docs/docs/integrations/document_loaders/parsers/azure_openai_whisper_parser.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,192 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Azure OpenAI Whisper Parser" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| ">[Azure OpenAI Whisper Parser](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/whisper-overview) is a wrapper around the Azure OpenAI Whisper API which utilizes machine learning to transcribe audio files to english text. \n", | ||
| ">\n", | ||
| ">The Parser supports `.mp3`, `.mp4`, `.mpeg`, `.mpga`, `.m4a`, `.wav`, and `.webm`.\n", | ||
| "\n", | ||
| "The current implementation follows LangChain core principles and can be used with other loaders to handle both audio downloading and parsing. As a result of this the parser will `yield` an `Iterator[Document]`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Prerequisites" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The service requires Azure credentials, Azure endpoint and Whisper Model deployment, which can be set up by following the guide [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/whisper-quickstart?tabs=command-line%2Cpython-new%2Cjavascript&pivots=programming-language-python). Furthermore, the required dependencies must be installed.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%pip install -Uq langchain langchain-community openai" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Example 1" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The `AzureOpenAIWhisperParser`'s method, `.lazy_parse`, accepts a `Blob` object as a parameter containing the file path of the file to be transcribed." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from langchain_core.documents.base import Blob\n", | ||
| "\n", | ||
| "audio_path = \"path/to/your/audio/file\"\n", | ||
| "audio_blob = Blob(path=audio_path)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from langchain_community.document_loaders.parsers.audio import AzureOpenAIWhisperParser\n", | ||
| "\n", | ||
| "endpoint = \"<your_endpoint>\"\n", | ||
| "key = \"<your_api_key\"\n", | ||
| "version = \"<your_api_version>\"\n", | ||
| "name = \"<your_deployment_name>\"\n", | ||
| "\n", | ||
| "parser = AzureOpenAIWhisperParser(\n", | ||
| " api_key=key, azure_endpoint=endpoint, api_version=version, deployment_name=name\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "documents = parser.lazy_parse(blob=audio_blob)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "for doc in documents:\n", | ||
| " print(doc.page_content)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Example 2" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The `AzureOpenAIWhisperParser` can also be used in conjuction with audio loaders, like the `YoutubeAudioLoader` with a `GenericLoader`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from langchain_community.document_loaders.blob_loaders.youtube_audio import (\n", | ||
| " YoutubeAudioLoader,\n", | ||
| ")\n", | ||
| "from langchain_community.document_loaders.generic import GenericLoader" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Must be a list\n", | ||
| "url = [\"www.youtube.url.com\"]\n", | ||
| "\n", | ||
| "save_dir = \"save/directory/\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "name = \"<your_deployment_name>\"\n", | ||
| "\n", | ||
| "loader = GenericLoader(\n", | ||
| " YoutubeAudioLoader(url, save_dir), AzureOpenAIWhisperParser(deployment_name=name)\n", | ||
| ")\n", | ||
| "\n", | ||
| "docs = loader.load()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "for doc in documents:\n", | ||
| " print(doc.page_content)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.12.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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