This is why we will implement a token counter to measure the number of tokens in the input and split it accordingly. To handle large texts, we need to ensure that the text is split into smaller chunks, each within the token limit. It’s important to note that GPT-3.5 Turbo has a token limit of 4097 tokens per API call. We will leverage OpenAI’s API to process the text and save the summarized output to a file. In this tutorial, we will learn how to transcribe a podcast(audio file) into text and then use OpenAI’s ChatGPT API (GPT-3.5 Turbo model) to summarize the transcription. Example – Summarizing Large Texts from Podcast Transcriptions Using Whisper and ChatGPT API The model can perform many different tasks on the same input audio signal, such as transcription, translation, voice activity detection, alignment, and language identification. The model architecture of Whisper is an encoder-decoder Transformer. The team also performs de-duplication at a transcript level between the training dataset and the evaluation datasets to avoid contamination. If the two do not match, the (audio, transcript) pair is not included in the dataset as a speech recognition training example. Whisper uses an audio language detector to ensure that the spoken language matches the language of the transcript. Many transcripts on the internet are not human-generated but the output of existing ASR systems. In this context, the term “transcript-ese” describes the phenomenon of ASR-generated transcripts that are not naturalistic and do not accurately reflect how humans speak. In addition, to avoid learning “transcript-ese,” the OpenAI team developed many heuristics to detect and remove machine-generated transcripts from the training dataset. The OpenAI team developed several automated filtering methods to enhance the training dataset’s quality. This approach allows a diverse dataset covering a broad audio distribution from various environments, recording setups, speakers, and languages. The training process relies on the expressiveness of sequence-to-sequence models to learn to map between utterances and their transcribed forms. The Whisper model employs a minimalist approach to data pre-processing and trains to predict the raw text of transcripts without applying significant standardization. The main focus of the Whisper approach is to simplify the speech recognition pipeline by removing the need for a separate inverse text normalization step to produce naturalistic transcriptions. It leverages web-scale text from the internet for training machine learning systems. Whisper is a state-of-the-art automatic speech recognition model developed by OpenAI. This is accomplished by splitting the audio signal into smaller parts, such as individual sounds or phonemes, and comparing those components with a large database of recognized words and language patterns.Īfter transcribing speech to text, you can use it for various purposes, including creating closed captions for videos, enabling hands-free conversations on mobile devices and smart speakers, and giving access to audio information for people with hearing impairments. The fundamental concept underlying speech-to-text technology is to analyze audio input using algorithms and statistical models to identify uttered words and phrases. Speech-to-text technology has many potential benefits, including downstream data generation & transformation, improved accessibility for people with disabilities, increased data entry and transcription efficiency, and enhanced communication capabilities in various settings. Although it has been around for a while, current developments in machine learning and artificial intelligence have increased its accuracy and accessibility. Speech-to-text technology, commonly referred to as Automatic Speech Recognition (ASR), enables computers to identify and convert spoken language into text. Mantium is the fastest way to achieve step one in the AI pipeline with automated, synced data preparation that gets your data cleaned and ready for use. To demonstrate, we will transcribe a podcast(mp3 file) to text, and use OpenAI’s ChatGPT API to summarize the text. In this article, we are going to examine the speech-to-text capabilities of OpenAI’s automatic speech recognition model – Whisper, and its API.
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