OpenAI Whisper Tutorial: Mastering Speech Recognition

Introducing Whisper: OpenAI's Groundbreaking Speech Recognition System

Whisper stands tall as OpenAI's cutting-edge speech recognition solution, expertly honed with 680,000 hours of web-sourced multilingual and multitask data. This robust and versatile dataset cultivates exceptional resilience to accents, ambient noise, and technical terminology. Furthermore, it supports seamless transcription in various languages and translation into English. OpenAI graciously unveils models and codes, paving the way for ingenious developers to construct valuable applications that harness the remarkable potential of speech recognition.

How to Use Whisper

The Whisper model is available on GitHub. You can easily download it with the following command directly in the Jupyter Notebook:

!pip install git+https://github.com/openai/whisper.git

Whisper requires ffmpeg installed on your current machine to work correctly. You may already have it installed, but it's likely you need to install this program first. OpenAI refers to multiple ways to install this package, but we will be using the Scoop package manager. Here is a brief tutorial on how to do it manually.

Installing ffmpeg Manually

In the Jupyter Notebook, install ffmpeg with the following command:

!scoop install ffmpeg

After the installation, a restart is required if you are using your local machine.

Importing Necessary Libraries

Next, import all necessary libraries:

import whisper

Using a GPU is the preferred way to use Whisper. You can check if you have a GPU available on your local machine by running the following commands:

import torch
print(torch.cuda.is_available())

The first line returns False if a CUDA-compatible Nvidia GPU is not available and True if it is available. The second line of code sets the model to prefer GPU whenever it is available.

Loading the Whisper Model

Load the Whisper model with the following command:

model = whisper.load_model("base")

Please note that there are multiple models available. You can find all of them here. Each model has tradeoffs between accuracy and speed (compute needed), but we will use the 'base' model for this tutorial.

Transcribing Audio Files

Next, you need to load your audio file you want to transcribe. Use the detect_language function to detect the language of your audio file:

language = model.detect_language("your_audio_file.mp3")

To transcribe the first 30 seconds of the audio, use the DecodingOptions and the decode command:

options = whisper.DecodingOptions(language=language)
result = model.decode(audio, options)
print(result.text)

To transcribe the whole audio file, you simply run:

result_full = model.transcribe("your_audio_file.mp3")
print(result_full["text"])

This will print out the entire audio file transcribed after the execution is finished. You can find the full code as a Jupyter Notebook here.

Leveraging Whisper for Creative Applications

Now it's up to you to create your own Whisper application. Get creative and have fun! Think about the various ways this technology can be used, whether in education, accessibility, or enhanced user experiences. The best approach is to identify a problem around you and craft a solution with Whisper's capabilities. Perhaps during our upcoming AI Hackathons, you can collaborate and innovate!

Conclusion

Whisper is set to revolutionize the field of speech recognition with its robust capabilities and user-friendly model. By understanding how to use it, developers and enthusiasts alike can create applications that make communication more effective, accessible, and engaging. Dive in, experiment, and make the most of this groundbreaking technology!