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ESP-SR 语音识别框架
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bugfix(afe): Update esp_afe_config.h and esp_afe_sr_iface.h for esp32

See merge request speech-recognition-framework/esp-sr!5
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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 text 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. ESP32-S3 is recommended, which supports AI instructions and larger, high-speech octal SPI PSRAM. The new algorithms will no longer support ESP32 chips.

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”, “Hi,lexin” and “Hi,ESP”. You can refer to Model loading method to build your project.

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. You can refer to Model loading method to build your project.

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), BSS(Blind Source Separation) 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: