Embed private voice AI into any product instantly
Technology
Picovoice provides core technology to embed private voice AI into any device. Picovoice's SDK makes it possible to build products that can be activated, controlled, and conversed with using voice without cloud connection. Our software runs on Linux, Mac, Windows, Android, iOS, watchOS, Raspberry Pi, and a growing number of embedded processors.
Private
Runs locally without an internet connection. Nothing is sent to the cloud to fully protect user's data.
Efficient
Uses proprietary deep learning technology that enables cutting-edge models to run on commodity (embedded) hardware efficiently.
Accurate
Resilient to noise, reverberation, and works across a variety of accents. Works everywhere for anyone.
Plug and Play
Enables customizations within seconds. Reduces time to market while promoting user's product, brand, and identity.
Cross-Platform
Runs across all major platforms. Linux, Mac, Windows, Android, iOS, watchOS, Raspberry Pi, ARM Cortex-A, ARM Cortex-M, and a growing list of embedded platforms are supported.
Open Benchmarks
Picovoice empowers users to evaluate its technology independently. Facilitates data-driven decision making.
Wake Word Detection Engine (Voice Control Engine)
The wake word detection library enables makers to build products that can be activated and controlled using voice. It makes it possible to activate the device similar to “Alexa” or “OK Google” but using your hotword (wake word) of choice to promote your product, brand, and identity. Additionally, the library allows the user to control the device via a configurable set of voice commands.
Zero Lead Time
Uses proprietary AI algorithms to build models for any custom wake word/command within seconds (instead of weeks). Removes the hassle of time-consuming and costly data gathering phase.
Scalable
Can detect many hotwords (wake up words and commands) concurrently with no additional CPU or memory footprint.
Lightweight
Natural fit for IoT. Can run with as low as 240 KB of memory and 3.4% CPU usage on Raspberry Pi 3.
Highly-Accurate
Outperforms existing solutions with high margins in clean and noisy environments. Read more here.
Ease of Integration
Expedite your development by reusing the many publicly available references designs with our fast-growing and vibrant open-source community.
Language Independent
Can create models for almost any phrase in any language.
Speech to Text Engine
The speech to text SDK facilitates building conversational interfaces without the need to any cloud connection. It is powered by a novel end-to-end learning algorithm which makes it possible to perform accurate speech recognition on IoT platforms with limited memory/CPU budget.
Open Vocabulary
No limit on the size of vocabulary. Provides large vocabulary transcription capabilities on embedded platforms.
Continuous and Real-Time
Transcribes audio in real time. Reduces latency and improves the user experience.
Lightweight
Runs in real-time with only 5.6 MB of memory and 25% CPU usage on a Raspberry Pi 3. Natural fit for IoT platforms. Android, iOS, Raspberry Pi, and a growing number of IoT platforms are supported.
Company
Picovoice is a team of applied scientists and engineers who strive to build a future where our lives are enhanced with ambient voice AIs, while respecting your privacy. Picovoice is founded by Alireza Kenarsari. Prior to Picovoice Alireza was a Senior Engineer at Amazon and has been also an early-stage engineer in a few successful technology startups (one reached IPO). He is the inventor of five US patents within the fields of deep learning and speech recognition. Read more about the beginnings of Picovoice here.
News
[August 26, 2018] Picovoice's speech to text repository ranks among top 10 open source machine learning projects. We are extremely excited to be on this list alongside names such as OpenAI, NVIDIA, Facebook Research, Airbnb, and Google. Read more here.
[July 23, 2018] Picovoice ranks among top 10 open source machine learning projects. We are extremely excited to be on this list alongside names such as Facebook Research, Salesforce, Baidu Research, and NVIDIA. Read more here.