Whisper AI
Health care
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In this blog post, I will introduce Whisper and show you how to use it for your own speech processing applications. Whisper is available as an API that you can access from Python or other programming languages. You will need an OpenAI API key to use Whisper, which you can get from https://beta.openai.com/.
To use Whisper from Python, you will need to install the openai package using pip:
pip install openai
Then, you can import the openai module and set your API key:
import openai
openai.api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx"
To transcribe a speech audio file into text, you can use the openai.Audio.transcribe method. You need to specify the name of the Whisper model ("whisper-1") and the audio file object. For example, if you have an mp3 file named "speech.mp3", you can do:
audio_file = open("speech.mp3", "rb")
transcript = openai.Audio.transcribe("whisper-1", audio_file)
The transcript object will contain a text attribute that has the transcribed text. You can print it or save it to a file:
print(transcript.text)
with open("transcript.txt", "w") as f:
f.write(transcript.text)
To translate a speech audio file into English, you can use the same method but add a special token "" at the beginning of the text attribute. For example, if you have a Spanish speech audio file named "speech_es.mp3", you can do:
audio_file = open("speech_es.mp3", "rb")
translation = openai.Audio.transcribe("whisper-1", audio_file)
translation.text = "" + translation.text
The translation object will contain a text attribute that has the translated text. You can print it or save it to a file:
print(translation.text)
with open("translation.txt", "w") as f:
f.write(translation.text)
Whisper can handle multiple languages and tasks with a single model. You can check the model card for more details on what languages and tasks are supported: https://github.com/openai/whisper/blob/main/model_card.md.
Whisper is a powerful and easy-to-use tool for speech recognition and translation. It can help you create voice-enabled applications and make speech content more accessible to a wider audience. I hope you enjoyed this blog post and learned how to use Whisper for your own projects.
Pros
- Whisper is a free and open-source speech recognition and translation tool developed by OpenAI .
- Whisper can transcribe speech audio into text in the language it is spoken (ASR) as well as translated into English (speech translation).
- Whisper has been trained on 680,000 hours of multilingual and multitask supervised data collected from the web, which makes it robust to accents, background noise and technical language .
- Whisper can handle multiple languages, such as English, Japanese, Spanish, French, German, etc. .
- Whisper has a simple end-to-end architecture, implemented as an encoder-decoder Transformer, which makes it easy to use and modify.
Cons
- Whisper is not a specialized model for any specific dataset or task, so it may not beat models that are fine-tuned for certain benchmarks or domains.
- Whisper is only available as an API, which means that users need to have some programming skills or tools to access and use it.
- Whisper may not be able to transcribe or translate speech audio that is very low quality, noisy, distorted or contains multiple speakers.
- Whisper may not be able to handle languages that are not well represented in its training data or that have complex grammar or writing systems.
- Whisper may not be able to capture the nuances, emotions or intentions of the speakers in the speech audio.