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Description

Ink 2 represents Cartesia's most advanced and precise streaming speech-to-text model, designed specifically for production voice agents, boasting the lowest word error rate and superior turn detection of any available streaming STT. This model excels in accurately transcribing structured data types like phone numbers, dates, and email addresses on the first attempt, while intuitively recognizing when a speaker begins and ends their speech, eliminating the need for a separate voice activity detection mechanism. Integrated turn detection allows voice agents to respond to events seamlessly, rather than sifting through raw transcript segments. Ink 2 generates a comprehensive array of turn events, providing agents with definitive cues regarding when to listen, interrupt, contemplate, prepare to respond, retract an untimely reply, or engage in conversation. Additionally, the transcript retains a cumulative nature within each turn, ensuring that every update presents the complete text transcribed up to that point rather than just the incremental changes, and the emitted text is considered final the moment it is sent. This innovative design enhances the interaction quality between voice agents and users, making conversations smoother and more effective.

Description

Muse Voice Transcribe represents Meta’s inaugural venture into real-time audio perception, providing instantaneous automatic speech recognition (ASR), speaker diarization, and endpointing capabilities. This autoregressive multimodal model, part of the Muse Spark series, analyzes audio segments of 80 milliseconds and makes real-time decisions on whether to keep listening or to convert the spoken words into text. The adaptive delay mechanism allows it to adjust the audio context utilized for each word according to the complexity of the speech, thus optimizing the balance between transcription precision and response time. With training encompassing over 70 languages, 25 of which were rigorously validated at the time of its release, the model also seamlessly accommodates arbitrary code-switching, allowing transitions within and across sentences. Furthermore, language, keyword, and contextual biasing features enhance the recognition capabilities for specific names, locations, contacts, or specialized terms. The streaming diarization functionality enables the model to recognize shifts in speakers and can differentiate between more than 20 individual voices. Additionally, the endpointing feature is adept at identifying the commencement of speech and knowing when a user has completed their statement, ensuring a fluid interaction experience. Overall, Muse Voice Transcribe stands out as a cutting-edge tool in the realm of speech recognition technology, merging advanced features with user-friendly application.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

MachinesFluent

Integrations

MachinesFluent

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
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

Cartesia

Founded

2023

Country

United States

Website

docs.cartesia.ai/build-with-cartesia/stt/latest

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

research.meta.ai/blog/introducing-muse-voice-transcribe

Product Features

Product Features

Alternatives

Alternatives

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