From c540eeeeaf00568936b5df0770962fcf18fd935e Mon Sep 17 00:00:00 2001 From: sxy Date: Fri, 22 Oct 2021 19:35:09 +0800 Subject: [PATCH] doc(README): Update README doc --- README.md | 9 ++++----- README_cn.md | 33 --------------------------------- 2 files changed, 4 insertions(+), 38 deletions(-) delete mode 100644 README_cn.md diff --git a/README.md b/README.md index 8fc2a5e..0566256 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# esp_sr [[中文]](./README_cn.md) +# esp_sr Espressif esp_sr provides basic algorithms for **Speech Recognition** applications. Now, this framework has three modules: @@ -21,14 +21,13 @@ Espressif's speech command recognition model [MultiNet](speech_command_recogniti Currently, Espressif **MultiNet** supports up to 100 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). -## Acoustic algorithm +## Audio Front End -Espressif acoustic algorithm module is specially designed to improve speech recognition performance in far-field or noisy environments. +Espressif Audio Front-End [AFE](audio_front_end/README.md) integrates AEC (Acoustic Echo Cancellation), VAD (Voice Activity Detection),BSS (Blind Source Separation) and NS (Noise Suppression). -Currently, MASE algorithm supports 2-mic linear array and 3-mic circular array. +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). **In order to achieve optimal performance:** -* Please refer to hardware design [ESP32_Korvo](https://github.com/espressif/esp-skainet/tree/master/docs/zh_CN/hw-reference/esp32/user-guide-esp32-korvo-v1.1.md) or [ESP32-LyraT-Mini](https://docs.espressif.com/projects/esp-adf/en/latest/get-started/get-started-esp32-lyrat-mini.html). * Please refer to software design [esp-skainet](https://github.com/espressif/esp-skainet). \ No newline at end of file diff --git a/README_cn.md b/README_cn.md deleted file mode 100644 index e7732a7..0000000 --- a/README_cn.md +++ /dev/null @@ -1,33 +0,0 @@ -# esp_sr [[English]](./README.md) - -esp_sr 提供语音识别相关方向算法模型，目前主要包括三个模块： - -* 唤醒词识别模型 [WakeNet](wake_word_engine/README_cn.md) -* 语音命令识别模型 [MultiNet](speech_command_recognition/README_cn.md) -* 声学算法：MASE(Mic Array Speech Enhancement), AEC(Acoustic Echo Cancellation), VAD(Voice Activity Detection), AGC(Automatic Gain Control), NS(Noise Suppression) - -这些算法以组件的形式提供，因此可以轻松地将它们集成到您的项目中。 - -## 唤醒词识别 - -唤醒词模型 [WakeNet](wake_word_engine/README_cn.md)，致力于提供一个低资源消耗的的高性能模型，支持类似“Alexa”，“天猫精灵”，“小爱同学”等唤醒词的识别。 - -目前乐鑫免费开放“Hi，乐鑫”唤醒词。如果用户需要其它唤醒词，乐鑫提供有唤醒词定制服务，具体可参考 [乐鑫语音唤醒词定制流程](wake_word_engine/乐鑫语音唤醒词定制流程.md)。 - -## 语音命令识别 - -命令词识别模型 [MultiNet](speech_command_recognition/README_cn.md) ，致力于提供一个灵活的离线语音命词识别框架。用户可方便根据需求自定义语音命令，无需重新训练模型。 - -目前模型支持类似“打开空调”，“打开卧室灯”等中文命令词识别和"Turn on/off the light" 等英文命令词识别，自定义语音命令词最大个数为 100。 - - -## 声学算法 - -声学算法模块, 致力于提高复杂声学环境下的语音识别性能。MASE算法可有效改善远程或嘈杂环境下的语音识别性能。 - -目前MASE算法支持2-mic线性阵列和3-mic环形阵列。 - -**算法性能与硬件设计与软件配置息息相关，为达到最优性能:** - -* 硬件设计建议参考 [ESP32_Korvo](https://github.com/espressif/esp-skainet/tree/master/docs/zh_CN/hw-reference/esp32/user-guide-esp32-korvo-v1.1.md) 或 [ESP32-LyraT-Mini](https://docs.espressif.com/projects/esp-adf/en/latest/get-started/get-started-esp32-lyrat-mini.html)。 -* 软件设计建议参考 [esp-skainet](https://github.com/espressif/esp-skainet) 中相关示例。