readme

# esp_sr

Espressif esp_sr provides basic algorithms for **Speech Recognition** applications. Now, this framework has four modules:

* The wake word detection model [WakeNet](docs/wake_word_engine/README.md)
* The speech command recognition model [MultiNet](docs/speech_command_recognition/README.md) 
* Audio Front-End [AFE](docs/audio_front_end/README.md)
* The text to speech model [esp-tts](esp-tts/README.md)

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](docs/wake_word_engine/README.md) 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](./docs/flash_model/README.md) 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](docs/wake_word_engine/ESP_Wake_Words_Customization.md).

- [WakeNet Performance](docs/benchmark/README.md)

## Speech Command Recognition

Espressif's speech command recognition model [MultiNet](docs/speech_command_recognition/README.md) 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](./docs/flash_model/README.md) 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).

- [MultiNet Performance](docs/benchmark/README.md)

## Audio Front End

Espressif Audio Front-End [AFE](docs/audio_front_end/README.md) 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](https://developer.amazon.com/en-US/alexa/solution-providers/dev-kits#software-audio-front-end-dev-kits).

- [Audio Front-End Performance](docs/benchmark/README.md)

**In order to achieve optimal performance:**

* Please refer to software design [esp-skainet](https://github.com/espressif/esp-skainet).

changelog

# Change log for esp-sr

## 0.8.0
support ESP32S3 chip
add wakenet7 & update wakenet5 to support multi-channel detection
remove wakenet6
add AFE pipeline for speech recognition 

## 0.7.0
add chinese tts
update noise suppression v2
update AEC v3

## 0.6.0
update multinet_cn_1.4 and add CONTINUOUS RECOGNITION mode  
improve hilexin wakeNet5X3 model(v5)    
support IDFv4.0 build system  
replace MAP algorithm with MASE(Mic Array Speech Enhancement) algorithm v1.0  

## 0.5.0
add multinet1 English model v1.0  
update multinet1 Chinese model v2.0  
add Mic Array Processing(MAP) algorithm  
Fix the bug of parsing speech command  
fix the bug of decoder  


## 0.3.0
add wakenet6  
support cmake  
add free wake word: hi jeson  
update wakenet5X3 wake word model(v2)  

## 0.2.0
add acoustic algorithm, include AEC, AGC, VAD ,NS  
add wakenet5X2 and wakenet5X3  

## 0.1.0 
Initial commit, include wakenet4,wakenet5 and multinet1_cni  

Links

License: MIT

To add this component to your project, run:

idf.py add-dependency "espressif/esp-sr^1.0.2"

or download archive

Dependencies

  • ESP-IDF >=4.4
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