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This introduces the `YoutubeAudioLoader`, which will load blobs from a YouTube url and write them. Blobs are then parsed by `OpenAIWhisperParser()`, as show in this [PR](https://github.com/hwchase17/langchain/pull/5580), but we extend the parser to split audio such that each chuck meets the 25MB OpenAI size limit. As shown in the notebook, this enables a very simple UX: ``` # Transcribe the video to text loader = GenericLoader(YoutubeAudioLoader([url],save_dir),OpenAIWhisperParser()) docs = loader.load() ``` Tested on full set of Karpathy lecture videos: ``` # Karpathy lecture videos urls = ["https://youtu.be/VMj-3S1tku0" "https://youtu.be/PaCmpygFfXo", "https://youtu.be/TCH_1BHY58I", "https://youtu.be/P6sfmUTpUmc", "https://youtu.be/q8SA3rM6ckI", "https://youtu.be/t3YJ5hKiMQ0", "https://youtu.be/kCc8FmEb1nY"] # Directory to save audio files save_dir = "~/Downloads/YouTube" # Transcribe the videos to text loader = GenericLoader(YoutubeAudioLoader(urls,save_dir),OpenAIWhisperParser()) docs = loader.load() ``` |
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| .. | ||
| _static | ||
| additional_resources | ||
| ecosystem | ||
| getting_started | ||
| integrations | ||
| modules | ||
| reference | ||
| templates | ||
| tracing | ||
| use_cases | ||
| conf.py | ||
| dependents.md | ||
| index.rst | ||
| integrations.rst | ||
| make.bat | ||
| Makefile | ||
| reference.rst | ||
| requirements.txt | ||