December 2022 Summaries

OpenAI has launched a new generative AI platform called Point-E, which is capable of rendering 3D images out of digital noise. Similar to DALL-E, another model by OpenAI that generates 2D images from text prompts, Point-E can create 3D objects based on text descriptions. It also has the ability to generate 3D point clouds from 2D images and convert point clouds into 3D meshes. The technology is still in its experimental stage but could become an integral part of various industries such as video game design, hardware product development, and more. OpenAI researchers acknowledge that there are areas for improvement, including the need to train generators that condition on real-world images and enhance the resolution of 3D shapes produced by Point-E.

The article discusses the use of AI-generated recipes using OpenAI's GPT-3 language model. It highlights a video where food writer Priya Krishna uses GPT-3 to generate personalized Thanksgiving recipes, which are then criticized by veteran NYT food writers for being dry and under-sweetened. The author argues that while the AI-generated recipes may not be perfect, they can still serve as a useful tool for home cooks by providing inspiration and suggestions based on available ingredients. The article also provides tips and examples of how to use GPT-3 in generating meal plans and recipes tailored to individual preferences and needs.

In the sitcom The Office, a Python developer at Dunder Mifflin Paper Company is tasked with transcribing a sales meeting audio between regional manager Michael Scott and VP of Sales Jan. The goal is to identify sales insights using AI speech recognition and Python. Deepgram's Python SDK is used for the transcription process, which includes features like summarization and search. The resulting JSON response provides a summary of the conversation and highlights mentions of business, deals, or jokes. This helps the President of Sales understand what happened in the sales call, how to coach Michael Scott on closing future deals, and how to follow up with the customer.

In this comparison of open-source ASR models, Kaldi performs poorly across all metrics and domains. Whisper outperforms wav2vec 2.0 in terms of accuracy but is significantly slower. The choice between these two options would depend on the specific needs of the user.

Deepgram introduces a new beta feature called Smart Format that enhances the readability of transcripts by automatically formatting dates and numbers. This feature simplifies complex text, such as converting "eight thirty seven pm on november second twenty twenty two" to "08:37 PM on 11/02/2022". To use Smart Format, specify smart_format=true in API requests. The company plans to continually improve this feature over time. For more information and feedback, visit Deepgram's documentation and GitHub discussions.

Deepgram celebrated a year of connecting with developers and supporting the community through various events such as conferences and hackathons. They sponsored nine conferences, providing captions for an accessible experience at some of them. Deepgram also participated in hackathons like Hack Cambridge, Dev.to Hackathon, and Learn Build Teach Hackathon, showcasing innovative projects using their speech-to-text technology. The company plans to attend more events in 2023 and encourages feedback through their GitHub discussions.

The author shares their experience of wanting a speech-to-text chatbot while ordering emergency supplies for an injured hand. This led them to create a Python-based agent assist bot using Deepgram, a speech recognition provider, and ChatterBot, a chatbot based on machine learning. They explain the reasons why AI speech-to-text technology and chatbots are important in customer service interactions. The author then provides step-by-step instructions for setting up the necessary environment and libraries to build an agent assist bot with Python. Finally, they share the full code of their speech-to-text chatbot implementation using Python.

Deepgram has released an Enhanced French (beta) Speech-to-Text Language Model, expanding its language support for customers worldwide. The model is designed to accurately transcribe French speakers and can be used in various applications such as phone call transcription, meeting recordings, and agent assist solutions. Developers can leverage Deepgram's Enhanced French Language Model with a variety of use case models, including Phone Call, Meeting, Voicemail, and Conversational AI. The model is available for pre-recorded and streaming audio and can be used on-premises or through the Deepgram Cloud. As Deepgram continues to support its growing global customer base, it plans to expand its EU language capabilities in the coming months.

Deepgram has released an Enhanced Korean (beta) speech-to-text language model, offering extended support for the platform. Developers can use this model to transcribe recordings from call centers and meetings, create agent assist solutions, and more. The model is designed with accuracy in mind, providing upwards of 90% accuracy depending on the use case. It supports pre-recorded and streaming audio transcription and can be used on-premises or through the Deepgram Cloud.

Deepgram has released an Enhanced Italian (beta) Speech-to-Text Language Model, expanding its support for the European Union languages on its platform. The model is designed to transcribe Italian speakers with high accuracy and can be used in various applications such as call center recordings, meeting discussions, and sales team productivity solutions. Developers can access this feature by creating an account on Deepgram Console or calling the Enhanced Italian model using specific arguments if they are already customers. The company plans to continue enhancing its EU language capabilities to support its growing global customer base.

This article discusses topic detection using BERT (Bidirectional Encoder Representations from Transformers), a large language model created and published in 2018. It explains the history of transformer models, including their evolution from Recurrent Neural Networks (RNNs) to more recent NLP architecture models like Gated Recurrent Unit (GRU). The article also introduces BERTopic, an open-source library that uses a BERT model for topic detection with class-based TF-IDF procedure. It demonstrates how to use the BERTopic library in Python for topic modeling and visualizing document clusters.

This article discusses the importance of adding end-to-end testing to a React project using Cypress. It provides five reasons for doing so, including ensuring reliability and decreasing bugs in code. The author guides readers through setting up a React project with Cypress installed and explains how to write an end-to-end test using describe and it blocks. Additionally, the article introduces the Cypress Testing Library and demonstrates its use in writing more readable tests. Finally, the author encourages readers to explore further by checking out the GitHub repository for a sample project with implemented end-to-end tests.

Deepgram has released an Enhanced Japanese (beta) Speech-to-Text Language Model, which supports more EU languages and offers a beta rollout for the Japanese language model. Developers can use this model to transcribe recordings from their European call center or understand topics commonly discussed by customers in meetings. The model provides up to 90% accuracy and is available for pre-recorded and streaming audio, with on-premises or cloud deployment options. Deepgram plans to expand its language capabilities further in the coming months to support a growing global customer base.

In this tutorial, learn how to take notes with voice using Python by transcribing audio files into text format. The project utilizes Deepgram's speech-to-text APIs for high-quality AI-generated transcripts from pre-recorded audio sources. Key features include diarization and summarization. The tutorial guides users through setting up a Python project, installing necessary libraries, transcribing local or online audio files, and using advanced speech-to-text features to enhance note-taking with voice in Python.