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doc: Update performance doc

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sxy 2022-09-08 16:54:26 +08:00
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README.md
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@ -18,18 +18,23 @@ Espressif wake word engine [WakeNet](docs/wake_word_engine/README.md) is special
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/performance_test/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/performance_test/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/performance_test/README.md)
**In order to achieve optimal performance:**

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docs/performance_test/README.md
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@ -36,19 +36,20 @@
|Model Type|RAM|PSRAM|Average Running Time per Frame| Frame Length|
|:---:|:---:|:---:|:---:|:---:|
|Quantised WakeNet8 @ 2 channel|50 KB|1640 KB|10 ms|32 ms|
|Quantised WakeNet9 @ 2 channel|16 KB|320 KB|4 ms|32 ms|
|Quantised WakeNet8 @ 2 channel|50 KB|1640 KB|10.0 ms|32 ms|
|Quantised WakeNet9 @ 2 channel|16 KB|324 KB|3.0 ms|32 ms|
|Quantised WakeNet9 @ 3 channel|20 KB|347 KB|4.3 ms|32 ms|
### 2.3 Performance
|Distance| Quiet | Stationary Noise (SNR = 4 dB)| Speech Noise (SNR = 4 dB)| AEC Interruption (-10 dB)|
|:---:|:---:|:---:|:---:|:---:|
|1 m|98%|96%|94%|96%|
|3 m|98%|94%|92%|94%|
|3 m|98%|96%|94%|94%|
False triggering rate: 1 time in 12 hours
**Note**: We use the ESP32-S3-Korvo V4.0 development board and the WakeNet8(Alexa) model in our test.
**Note**: We use the ESP32-S3-Korvo V4.0 development board and the WakeNet9(Alexa) model in our test.
## 3. MultiNet
@ -64,7 +65,7 @@ False triggering rate: 1 time in 12 hours
|:---:|:---:|:---:|:---:|:---:|
|MultiNet 4|16.8KB|1866 KB|18 ms|32 ms|
|MultiNet 4 Q8|10.5 KB|1009 KB|11 ms|32 ms|
|MultiNet 5 Q8|14 KB||12 ms|32 ms|
|MultiNet 5 Q8|16 KB |2310 KB|12 ms|32 ms|
### 2.3 Performance with AFE