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Description

Inworld Realtime STT is a streaming API for speech-to-text that captures more than just spoken words. This innovative tool merges low-latency speech recognition with voice profiling capabilities, allowing it to analyze emotions, vocal style, accent, age, and pitch from raw audio inputs, which enhances the responsiveness and expressiveness of downstream LLMs and TTS systems. Developers have the flexibility to stream audio in real time, transcribe entire files, or gather voice profile signals via a single, comprehensive API. The system features real-time bidirectional streaming over WebSocket, synchronous transcription for complete audio files, and offers voice profile signals for each streaming segment, all while supporting multiple providers through one model ID. Each audio segment provides a dynamic profile of the speaker, complete with confidence scores, equipping LLMs with structured context that indicates the emotional state of the user, such as whether they sound sad, frustrated, soft-spoken, high-pitched, or calm. This capability allows for a more nuanced interaction, enriching the user experience by adapting responses to the speaker’s emotional tone and vocal characteristics.

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

No details available.

Integrations

No details available.

Pricing Details

Free
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

Inworld

Founded

2021

Country

United States

Website

inworld.ai/speech-to-text

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

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

Product Features

Product Features

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