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Catch the Wave of Transcription Innovation with Amazon Transcribe: Breaking Language Barriers, One Word at a Time!

In a groundbreaking move, Amazon Transcribe, the automatic speech recognition (ASR) service, has recently expanded its capabilities, now supporting over 100 languages. Unveiled at the AWS re: Invent event, this advancement empowers AWS customers to seamlessly integrate speech-to-text functionality into their applications on the AWS Cloud. The official announcement by Sumit Kumar and Vivek Singh, AWS experts at Amazon, highlighted the extensive training process, utilizing millions of hours of unlabeled audio data across diverse languages.

Previously accommodating 79 languages, Amazon Transcribe has significantly broadened its linguistic reach. The training methodology involves smart data sampling to ensure a balanced representation of languages, enhancing accuracy even for traditionally under-represented languages. The result is an application that not only delivers on its promise of transcription but also exhibits a remarkable 20-50% accuracy improvement across most languages.

In the complex realm of telephony speech, characterized by challenges and a scarcity of data, Amazon Transcribe stands out with an impressive 30-70% accuracy enhancement. This capability positions the service as a reliable solution for applications requiring transcription in difficult and data-scarce domains. Amazon Transcribe goes beyond basic transcription, offering a suite of advanced features such as automatic punctuation, custom vocabulary, automatic language identification, and custom vocabulary filters.

One notable advantage of Amazon Transcribe is its adaptability to various formats and environments. The service can accurately recognize speech in both audio and video formats, even in noisy environments. This versatility makes it a valuable tool for a range of applications, from content creators needing accurate transcriptions for video content to businesses looking to analyze customer calls for insights and improvements.

The impact of Amazon Transcribe extends to thousands of enterprises seeking to unlock valuable insights from their audio content. The service’s high accuracy across different accents and noise conditions, coupled with its support for a vast number of languages, enhances accessibility and discoverability of audio and video content. For instance, contact centers can leverage the transcription capabilities to analyze customer calls, identify insights, and subsequently enhance both customer experience and agent productivity.

While Amazon Transcribe takes a significant leap forward, it is worth noting that other players in the field also contribute to the landscape of AI-powered transcription services. Otter, a notable contender, has been providing exceptional AI transcriptions for consumers and enterprises. Meanwhile, Meta, a tech giant, is exploring an AI-powered translation model capable of recognizing nearly 100 spoken languages, providing a different dimension to language-related AI services.

SeamlessM4T, an emerging player in the space, stands out with its Massively Multilingual and Multimodal Machine Translation capabilities. This service goes beyond speech-to-text, offering translation for both speech-to-text and text-to-text across nearly 100 languages. The competition in the AI transcription and translation arena is heating up, with each service bringing its unique strengths to the table.

As businesses and content creators navigate the evolving landscape of transcription services, Amazon Transcribe emerges as a frontrunner, offering not only expanded language support but also a suite of features that enhance accuracy and usability. The future of transcription services is undoubtedly exciting, with technology continually pushing boundaries to make audio and video content more accessible and actionable.

Meta' Llama chat2

Today marks a significant milestone as Meta’s cutting-edge Llama 2 Chat 13B large language model (LLM) is now available on Amazon Bedrock. This launch positions Amazon Bedrock as the pioneer public cloud service to offer a fully managed API for Llama 2, Meta’s advanced LLM. This development opens up access to Llama 2 Chat models on Amazon Bedrock for organizations of all sizes, eliminating the need to handle the intricacies of underlying infrastructure. It represents a paradigm shift in accessibility to state-of-the-art language models.

Amazon Bedrock, a fully managed service, provides a selection of high-performing foundation models (FMs) from leading AI entities, such as AI21 Labs, Anthropic, Cohere, Stability AI, Amazon, and now Meta. Alongside this, it offers a comprehensive set of capabilities for constructing generative AI applications, streamlining development processes while prioritizing privacy and security. Further insights about Amazon Bedrock can be found in Antje’s detailed post.

Llama 2, a family of publicly available LLMs by Meta, has its base model pre-trained on a staggering 2 trillion tokens sourced from online public data. Meta reports that the training of Llama 2 13B consumed 184,320 GPU hours, equivalent to the workload of a single GPU over 21.04 years, excluding leap years.

The Llama 2 Chat model, situated atop the base model, is tailored for dialogue-oriented applications. Fine-tuned with over 1 million human annotations using reinforcement learning from human feedback (RLHF), this model has undergone rigorous testing by Meta to identify and address performance gaps and potential issues in chat scenarios, including the prevention of offensive or inappropriate responses.

In fostering a responsible and collaborative AI innovation ecosystem, Meta has provided an array of resources for all Llama 2 users, including individuals, creators, developers, researchers, academics, and businesses of all scales. The Meta Responsible Use Guide is a standout resource for developers, offering best practices and considerations for building products powered by LLMs in a responsible manner. Covering various stages of development from inception to deployment, this guide seamlessly integrates with the suite of AWS tools and resources designed to facilitate responsible AI development.

Now, incorporating the Llama 2 Chat model into your applications, regardless of the programming language used, is simplified. This can be achieved by calling the Amazon Bedrock API or utilizing the AWS SDKs or the AWS Command Line Interface (AWS CLI).

For those eager to witness Llama 2 Chat in action, we are committed to demonstrating the technologies we discuss. As avid readers of the AWS News blog are aware, we often provide practical examples. In the spirit of this, let’s delve into coding to interact with Llama 2.

Recently, during the AWS UG PerĂº Conf, a notable event that Jeff and Marcia attended alongside myself, Jeff delivered an inspiring talk on generative AI. The opening of the conference featured a captivating display of generated images of llamas, the emblematic animal of Peru. Therefore, what better subject to explore with Llama 2 Chat than the fascinating world of llamas? Stay tuned for an engaging exploration of Llama 2 Chat in action, as we delve into the realm of generative AI.

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