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| esp-tts | ||
| include | ||
| lib | ||
| model | ||
| src | ||
| tool | ||
| .gitignore | ||
| CHANGELOG.md | ||
| CMakeLists.txt | ||
| component.mk | ||
| Kconfig.projbuild | ||
| libversion | ||
| LICENSE | ||
| README.md | ||
esp_sr
Espressif esp_sr provides basic algorithms for Speech Recognition applications. Now, this framework has four modules:
- The wake word detection model WakeNet
- The speech command recognition model MultiNet
- Audio Front-End AFE
- The txt to speech model esp-tts
These algorithms are provided in the form of a component, so they can be integrated into your projects with minimum efforts.
Wake Word Engine
Espressif wake word engine WakeNet is specially designed to provide a high performance and low memory footprint wake word detection algorithm for users, which enables devices always listen wake words, such as “Alexa”, “天猫精灵” (Tian Mao Jing Ling) and “小爱同学” (Xiao Ai Tong Xue).
Currently, Espressif has not only provided an official wake word "Hi,Lexin","Hi,ESP" to public for free, but also allows customized wake words. For details on how to customize your own wake words, please see Espressif Speech Wake Words Customization Process.
Speech Command Recognition
Espressif's speech command recognition model MultiNet is specially designed to provide a flexible off-line speech command recognition model. With this model, you can easily add your own speech commands, eliminating the need to train model again.
Currently, Espressif MultiNet supports up to 200 Chinese or English speech commands, such as “打开空调” (Turn on the air conditioner) and “打开卧室灯” (Turn on the bedroom light).
Audio Front End
Espressif Audio Front-End AFE integrates AEC (Acoustic Echo Cancellation), VAD (Voice Activity Detection),MASE(Mic Array Speech Enhancement) and NS (Noise Suppression).
Our two-mic Audio Front-End (AFE) have been qualified as a “Software Audio Front-End Solution” for Amazon Alexa Built-in devices.
In order to achieve optimal performance:
- Please refer to software design esp-skainet.