Use case

Workspace
Collaboration

Speech AI at the service of international teams

Enhance collaboration across departments, streamline operations, and improve knowledge management. Gladia API is optimized to improve communication across languages and cultures, boost knowledge sharing and enhance team collaboration.

SaaS collaboration platforms
communication platforms
note-taking
producitivty apps
travel
telecommunications
e-commerce
global tech support
finance

Top features

Voice-to-text messages

Transcribe corporate voice memos into text format, allowing team members to easily read and respond to messages without having to listen to long voicemails.

Translation

Transcribe voice and video conversations in real time and translate them into 99 languages, allowing international team members to communicate seamlessly in their preferred language.

Transcription

Automatically transcribe voice and video meetings, allowing team members access and review meeting notes, decisions made, and action items. Ideal for remote teams and companies that keep track of meeting minutes for compliance or project management purposes.

Audio Indexing & NER

Index every transcribed audio and video in your content library by topics and keywords for easy searchability and accessibility. Invaluable for companies that produce and distribute a large volume of content.

API
Campaign
SEO
Subtitles
Online meeting
Business
Hight
Key decision
Marketing
Optimisation
Content value
Acquisition
Growth

Some stats on performance

78
%
boost in sales
1881
hours
saved processing calls
23
k $
gained in quarterly budget

Customized
for your needs

Transcription

Gladia API utilizes automatic speech recognition technology to convert audio, video files, or URL to text format. It transcribes 1h of audio in less than 60s.

Diarization

Based on a proprietary algorithm, automatically partitions an audio recording into segments corresponding to different speakers.

Topic classification

The process of categorizing content into one of the 698 predefined topic categories for content indexation.

Sentiment analysis

Determining the sentiment or opinion behind a piece of audio, such as a conversation or dialogue, using natural language processing.

Speech moderation

Allows to automatically identify and flag hate speech or other inappropriate and offensive verbal content according to pre-determined parameters.

Emotion detection

Our emotion recognition system is built upon the latest research and aims to accurately identify and distinguish between 27 human emotions.

Pricing

Free

Perfect for developers, early-stage startups, and individuals

0

$
/month

(10h/month included)

Pro

Designed to grow with scaling digital companies

0.612

$
/hour

+ $0.144 / hour for live transcription

Entreprise

Custom plan tailored to the modern enterprise

Contact us

We initially attempted to host Whisper AI, which required significant effort to scale. Switching to Gladia's transcription service brought a welcome change.

Robin lambert, CPO LIVESTORM

Read more

Case Studies

How VEED is streamlining video editing and subtitles with AI transcription

User-generated content has become a cornerstone of the internet-driven economy. As part of this shift, various platforms have emerged to provide easy-to-use tools to create high-quality video content in a matter of minutes — with AI transcription playing a foundational role in their product development.

Tutorials

How to build a speaker identification system for recorded online meetings

Virtual meeting recordings are becoming increasingly used as a source of valuable business knowledge. However, given the large amount of audio data produced in meetings by companies, getting the full value out of recorded meetings can be tricky.

Speech-To-Text

Should you trust WER?

Word Error Rate (WER) is a metric that evaluates the performance of ASR systems by analyzing the accuracy of speech-to-text results. WER metric allows developers, scientists, and researchers to assess ASR performance. A lower WER indicates better ASR performance, and vice versa. The assessment allows for optimizing the ASR technologies over time and helps to compare speech-to-text models and providers for commercial use.