OpenAI Whisper tutorial: How to use Whisper to transcribe a YouTube video
Unraveling Whisper: OpenAI's Premier Speech Recognition System
OpenAI Whisper emerges as OpenAI's state-of-the-art speech recognition solution, meticulously trained with 680,000 hours of web-sourced multilingual and multitask data. This extensive dataset bolsters increased resistance to accents, ambient noise, and technical jargon. Additionally, it supports transcribing in numerous languages and translating them into English. Distinct from DALLE-2 and GPT-3, Whisper is a free and open-source model. OpenAI delivers access to its models and codes, fostering the creation of valuable speech recognition applications.
Mastering YouTube Video Transcription with Whisper
Throughout this Whisper tutorial, you'll gain expertise in utilizing Whisper to transcribe a YouTube video. We'll employ the Python package "Pytube" to download and convert the audio into an
MP4 file. Visit Pytube's repository for more information.
First, we need to install the Pytube Library. You can do this by running the following command in your terminal:
!pip install -— upgrade pytube
For this tutorial I'll be using this "Python in 100 Seconds" Video.
Next, we need to import Pytube, provide the link to the YouTube video, and convert the audio to
#Importing Pytube library import pytube # Reading the YouTube link video = "https://www.youtube.com/watch?v=x7X9w_GIm1s" data = pytube.YouTube(video) # Converting and downloading as 'MP4' file audio = data.streams.get_audio_only() audio.download()
The output is a file named like the video title in your current directory. In our case, the file is named
Python in 100 Seconds.mp4
Now, the next step is to convert audio into text. We can do this in three lines of code using whisper. First, we install and import
whisper. Then we load the model and finally we transcribe the audio file.
Installing Whisper libary
!pip install git+https://github.com/openai/whisper.git -q
Load the model. We'll use the "base" model for this tutorial. You can find more information about the models here. Each one of them has tradeoffs between accuracy and speed (compute needed).
model = whisper.load_model("base") text = model.transcribe("Python in 100 Seconds.mp4")
And now we can print out the output.
#printing the transcribe text['text']
You can find the full code as Jupyter Notebook
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