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Description
MAI-Transcribe-2 represents the pinnacle of Microsoft AI's transcription capabilities, engineered to provide rapid and precise speech recognition across various real-world audio scenarios. This model includes features like speaker diarization, enabling it to differentiate between speakers and correctly attribute dialogue, as well as offering word-level timestamps for enhanced alignment, searching, navigation, and editing purposes. Additionally, it utilizes keyword biasing to improve the recognition of specialized terms, abbreviations, and names that may otherwise be challenging to identify from their contextual usage. Developers are afforded the flexibility to select from different transcription styles: a verbatim option that retains filler words and false starts for thorough analysis and compliance, or a clean option that eliminates such elements for clearer captions and more polished published transcripts. Furthermore, the model is adept at handling code-switching, seamlessly transitioning between languages during conversations, even accommodating mixed language combinations like Hinglish and Spanglish, while automatically identifying the language being spoken. This makes MAI-Transcribe-2 an invaluable tool for diverse linguistic environments and applications.
Description
Harness the power of voice and video content through automation to enhance creation, foster growth through promotion, and provide context for monetization. The spoken word remains our most crucial means of communication. In today’s digital landscape, we have addressed the primary challenges associated with spoken word content. By extracting and structuring this data, we can automatically develop revenue-generating assets that dramatically boost productivity. Automating the development of targeted promotional materials helps to effectively grow your audience and increase engagement levels. While distributing content for discoverability is important, it often proves to be a tedious and time-consuming endeavor. Take control of distribution and discoverability with resources that attract audiences to your content and enhance its searchability. Given that voice is analog and inherently unstructured, it faces challenges in the digital realm. Sonnant transforms spoken word data into organized, tagged information, facilitating various applications like search, activation, and advertising. Ultimately, this innovation not only streamlines processes but also opens new avenues for content creators to maximize their impact in a crowded marketplace.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$10 per hour
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Microsoft AI
Founded
2024
Country
United States
Website
microsoft.ai/news/mai-transcribe-2-is-the-fastest-most-accurate-and-cheapest-speech-recognition-model-in-the-world/
Vendor Details
Company Name
Sonnant
Founded
2020
Country
Australia
Website
www.sonnant.com
Product Features
Transcription
AI / Machine Learning
Annotations
Audio/Video File Upload
Automatic Transcription
Collaboration Tools
File Sharing
For Manual Transcription
Full Text Search
Multi-Language Support
Natural Language Processing (NLP)
Playback Controls
Speech Recognition
Subtitles
Text Editor
Timecoding
Product Features
Transcription
AI / Machine Learning
Annotations
Audio/Video File Upload
Automatic Transcription
Collaboration Tools
File Sharing
For Manual Transcription
Full Text Search
Multi-Language Support
Natural Language Processing (NLP)
Playback Controls
Speech Recognition
Subtitles
Text Editor
Timecoding