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README_microphone.md
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Repository files navigation

wmt_ai_study

My AI study

弃身锋刃端，性命安可怀？父母且不顾，何言子与妻！  
名编壮士籍，不得中顾私。捐躯赴国难，视死忽如归！  
——曹植《白马篇》

Recorder code work

see https://github.com/weimingtom/wmt_recorder_study

Wireless code work

see https://github.com/weimingtom/wmt_iot_study

TFLite work

1000 loop about 13sec  
Program Size: Code=134412 RO-data=135500 RW-data=572 ZI-data=181192

我测试过如果要用RT1020-EVK跑通无麦克风硬件输入的tflm 2.4 micro_speech例子，
大概需要flash是263k，
SRAM是177k，1000循环大概13秒（我估算的，可能不准确）
我是用MDK5的AC6编译器O3优化和打开LTO开关（相当于开着LLVM优化？），
所以可能用RT1010-EVK不够内存跑通这份代码（不确定，纯猜测）

1000 loop about 13sec  
Program Size: Code=97232 RO-data=95436 RW-data=36 ZI-data=38660

串口不用接，默认被连接到st-link的虚拟串口上
usart3, TX, PD8(not need), USART3_TX
usart3, RX, PD9(not need), USART3_RX

INMP441
SCK,WS,LR
[===]xxxxxxRRRRR
SD,VDD,GND

INMP441<->STM32F446
SCK(left top 1)<->SAI1_SCK_B, PF8 (left 4 bottom 4)
SD(right top 1)<->SAI1_SD_B, PE3 (left 4 bottom 5)
WS(left top 2)<->SAI1_FS_B, PF9 (left 4 bottom 2)
VDD(right top 2)<->3.3(left 3 top 4)
L/R(left top 3)<->GND
GND(right top 3)<->GND(left 3 top 7)
  • (done, 1000 loops 27 seconds) d133cbs, with luban-lite/rt-thread
    https://github.com/weimingtom/d133cbs_playground
    see helloworld_v8_final_success.7z
    (x) helloworld_v2_failed.7z
    using artinchip_20240203_luban-lite-master.zip
    (TODO) but some other codes not run well, see helloworld_v3.1_no_recog_output.7z
终于，我把artinchip的d133cbs跑通了tflm 2.4.0 micro_speech了，一度想放弃，
后来发现可以通过注释INIT_APP_EXPORT(lvgl_thread_init);来节省内存使用，
这样就能跑起来（运行的时候屏幕会显示彩色格子而非LVGL表盘），
比较过和h743的输出结果相同，1000次循环时间大概是27秒，
相当于stm32h743的2倍（13秒），不过也难怪，
因为这是同时运行了rt-thread的其他线程，可能实际还能更快，
相关代码我有空会开源到gh上

TFLite micro esp32, MSM261S4030H0R

(TODO) TFLite micro ARM, NXP MIMXRT1050-EVKB or Arm虚拟硬件(AVH)

(TODO) ArduCAM/pico-tflmicro, ArduCam Pico4ML

TinyML: 基于TensorFlow Lite在Arduino和超低功耗微控制器上部署机器学习

ML-KWS work

  • mlkws_stm32f411re_v8_first_success.rar
    (STM32) with STM32CubeMX, Keil MDK5 AC6 and NUCLEO-F411RE
    (stm32cubemx) Heap_Size==0x12000, Stack_Size==0x6000
    (DONE, SUCCESS, same result as mbed-cli) port to Keil MDK5 AC6 project
    (Dependency, using CMSIS version) search baidupan, CMSIS_5-5.4.0.zip
    https://github.com/ARM-software/CMSIS_5/releases/tag/5.4.0
    small change in CMSIS_5\CMSIS\Core\Include\core_cm4.h
//__FPU_PRESENT=1,
//#warning "Compiler generates FPU instructions for a device without an FPU (check __FPU_PRESENT)"
//#define __FPU_USED       0U
#if !defined(__FPU_PRESENT)
#define __FPU_PRESENT 1
#endif
#define __FPU_USED       1U

《嵌入式系统案例教程》第3章FPU，硬浮点和__FPU_PRESENT和__FPU_USED，在system_stm32f4xx.c，
需要全局定义__FPU_PRESENT和__FPU_USED，
见void SystemInit(void)函数。在stm32f429xx.h中有__FPU_PRESENT=1的宏定义，
或者在Keil 5的Floating Point Hardware中选择Use Single Precision
会产生__FPU_USED=1的宏定义
stm32f429属于Cortex-M4F架构，所以有FPU

Total time : 164061 us  
Detected right (99%)  
Total time : 164060 us  
Detected right (99%)

Speech-Recognition Python work

  • https://github.com/weimingtom/Speech-Recognition_mod
    work
  • Speech-Recognition-master_v3_success.tar.gz
    (Python3, TF2) With Tensorflow 2.x and xubuntu-20.04-desktop-amd64
    Keyword speech recognition, Speech commands, CNN, 10 words
    (install tf2) pip3 install tensorflow-cpu (if in xubuntu, use pip3 and python3)
    (origin) https://github.com/iamlekh/Speech-Recognition
  • Speech-Recognition_v7.tar.gz
    (Python3, TF2) with AI Studio, not done
    (install tf2) pip install tensorflow-cpu (if in AIStudio, use pip and python)

(TODO, porting difficult) voice_control_led, Maixduino MFCC-DTW + VAD (stm32-speech-recognition like)

  • voice_control_led_v9.rar
    (NOT DONE, Many Problems) TODO, with VS2013, windows port, calculate DTW distance get zero, don't know reason
    (TODO, need DOC) about fft, see yinxiangbiji
  • voice_control_led_en_v2_success.rar
    for Maixduino, with Arduino IDE and Sipeed Maix Dock (K210)
    (origin) Maix_Speech_Recognition
    https://github.com/sipeed/Maixduino/blob/master/libraries/Maix_Speech_Recognition
    examples/voice_control_led_en/voice_control_led_en.ino
    examples/get_voice_model/get_voice_model.ino

(TODO) stm32-speech-recognition, STM32 MFCC-DTW + VAD

(TODO, NOT IMP) cortex-m-kws, aid_speech, tengine-lite

NOTE: This demo may be not good,
because of lack of training method and model data，only providing model convert tool
Details see:
https://github.com/OAID/cortex-m-kws/blob/master/Documentation/Tengine%20on%20STM32F7%20Setup%20Manual.pdf
Tengine provides three versions:
a) https://github.com/OAID/Tengine version (only arm)
b) http://www.eaidk.com version (EAIDK-310, EAIDK-610)
c) http://www.tengine.org.cn version (rpi3b, rk3399, hi3516, etc)

(TODO) KWS_MCU, TC-ResNet8

https://github.com/Alex-Riviello/KWS_MCU

(TODO???) UT2UH/ML-KWS-for-ESP32

https://github.com/UT2UH/ML-KWS-for-ESP32
xtensa_copy_q7 ???????where????????
https://github.com/UT2UH/ML-KWS-for-ESP32/tree/master/Source/XMSIS/NN

(TODO) VAD

(TODO) Python GMM Chapter07 speech_recognizer.py

(TODO) html5, MFCC-DTW

(TODO) micro_speech (TFLM 2.0) train

(TODO) TensorFlow入门教程(17)语音识别

search baidupan, 17.speech_recognition.zip
TensorFlow入门教程(17)语音识别(上)
https://blog.csdn.net/rookie_wei/article/details/84527839

(TODO) diy-alexa, voice-controlled-robot, ESP32

https://github.com/atomic14/diy-alexa
Voice-Controlled Robot With the ESP32
https://github.com/atomic14/voice-controlled-robot

(TODO) MACE Micro Examples

(TODO) hyperconnect/TC-ResNet

https://github.com/hyperconnect/TC-ResNet
https://github.com/hyperconnect/TC-ResNet/blob/master/tflite_tools/run_benchmark.sh
https://github.com/tranHieuDev23/TC-ResNet
https://github.com/jianvora/Continual_Learning_for_KWS
https://github.com/olimjon-ibragimov/TC-ResNet-TensorFlow-2

(TODO) pytorch speech command tutorial

https://github.com/pytorch/tutorials/blob/master/intermediate_source/speech_command_recognition_with_torchaudio.py
https://pytorch.org/tutorials/intermediate/speech_command_recognition_with_torchaudio.html
(TODO) kws_game
https://github.com/chrisworld/kws_game

(TODO) tflite python

TensorFlow Lite Tutorial Part 3: Speech Recognition on Raspberry Pi
https://www.digikey.com/en/maker/projects/tensorflow-lite-tutorial-part-3-speech-recognition-on-raspberry-pi/8a2dc7d8a9a947b4a953d37d3b271c71
TensorFlow Lite Speech Recognition Demo
https://github.com/ShawnHymel/tflite-speech-recognition
【TensorFlow】Raspberry Piへのインストール方法3選
https://www.sejuku.net/blog/47178
TensorFlow Lite Python
https://tensorflow.google.cn/lite/guide/python
How to Run TensorFlow Lite Models on Raspberry Pi
https://blog.paperspace.com/tensorflow-lite-raspberry-pi/

(TODO) pico-wake-word

https://github.com/henriwoodcock/pico-wake-word
https://www.adafruit.com/product/1063
MAX4466 (like MAX9814???)
search baidupan, pico-wake-word-main.zip
tensorflow 2.4.1, 2021-03-31

(TODO) pytorch CRNN (???)

https://github.com/isadrtdinov/kws-attention

(TODO???) Edge Impulse, Recognize sounds from audio

https://docs.edgeimpulse.com/docs/audio-classification
http://www.elecfans.com/d/1532483.html
seeedstudio wio terminal tinyml
https://wiki.seeedstudio.com/Wio-Terminal-TinyML-EI-1/
https://wiki.seeedstudio.com/Wio-Terminal-TinyML-EI-3/

pico-microphone, Raspberry Pi Pico

while (1) {
	// store and clear the samples read from the callback
	int sample_count = samples_read;
	samples_read = 0;

	// loop through any new collected samples
	int maxValue = 0;
	for (int i = 0; i < sample_count; i++) { //or min(sample_count, 5)
		int value = sample_buffer[i];
		if (fabs(value) > fabs(maxValue)) {
			maxValue = value;
		}
	}
	//if M5stack PDM  
	printf("%d,%d,%d\n", maxValue / 128, -256, 256);
	//if max9814
	//printf("%d,%d,%d\n", maxValue, -512, 512);
	sleep_ms(50);
}
  • hello_pdm_microphone, using MP34DT01 PDM MEMS Microphone<->Rpi Pico, why this ok? see below:
    (self) 3V<-> (self) SEL
    GND<-> GND
  • hello_analog_microphone, use MAX9814, for 3.3V and 5V
  • hello_analog_microphone, use MAX9813H, for 5V only
  • hello_analog_microphone, use MAX9812L, for 3.3V only, not good, go stable slowly

(IMP, TODO) Edge Impulse, tensorflow 2.3

  • linux, xubuntu200464
    example-standalone-inferencing-master_v1.tar.gz
  • windows, vs2013
    helloei_v3_success.rar

画了个V3S，慢更语音助手（LUCKY）

https://whycan.com/t_7000.html
记录自制linux的过程（基于全志V3s）
https://blog.csdn.net/qq_46604211/article/details/116024970
https://github.com/dakun-create/voice-assistant-byV3S

(???) NVIDIA NeMo

https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html

(TODO) Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge

https://github.com/tugstugi/pytorch-speech-commands

(TODO) [深度学习进阶 - 实操笔记] 语音识别基础

https://blog.csdn.net/weixin_41809530/article/details/106585116
[深度学习进阶 - 实操笔记] 语音识别SPEECH_COMMANDS数据集
https://www.freesion.com/article/5523850245/
https://blog.csdn.net/weixin_41809530/article/details/106585116
https://blog.csdn.net/weixin_41809530/article/details/106669728

(TODO) Offline Speech Recognition on Raspberry Pi 4 with Respeaker

https://github.com/AIWintermuteAI/DeepSpeech_RaspberryPi4_Hotword
https://www.hackster.io/dmitrywat/offline-speech-recognition-on-raspberry-pi-4-with-respeaker-c537e7

(IMP) esp32 dsp (with asm)

https://github.com/espressif/esp-dsp

(???) Python, sipeed MaixPy, maix-asr, (speech_recognizer)

https://blog.csdn.net/xuguoliang757/article/details/118462079
https://github.com/sipeed/MaixPy_scripts/blob/master/multimedia/speech_recognizer/maix_asr_2900k_0x500000.kmodel
https://bbs.sipeed.com/thread/988

(IMP, TODO) TensorFlow语音识别实战

  • search book_TensorFlow语音识别实战
  • search baidupan, TensorFlow语音识别实战-源代码.7z
  • 第十章——基于MFCC和CTC的语音汉字转换
  • 第一章——语音识别之路, 1.7 实战——基于特征词的语音唤醒
  • 这本书的原创代码较多，可以研究
  • tensorflow-gpu==2.1.0

(TODO) 以前用树莓派3b运行deepspeech和vosk的备份

  • 除了Sphinx外，在树莓派上可以运行的较完整的离线语音识别引擎有deepspeech和vosk
    search work_deepspeech_vosk_raspberry_pi_upload
    search deepspeech_readme.txt

(TODO) Openai-whisper (for pyTorch) and whisper.cpp (for C++)

  • （可能）新的选择：openai-whisper
    我试过可以在python 3.7下运行（openai-whisper最旧版本），需要ffmpeg，
    但仅限于PC，没有在树莓派上测试，模型文件较小，但识别英文单词时间可能需要30秒以上
  • 较快的whisper推理版：whisper.cpp
    https://github.com/ggerganov/whisper.cpp
    比较容易编译（在ubuntu和aistudio），可以tiny-en model，可以识别句子，但识别单词似乎有问题（不知道为啥），待考
  • whisper and whisper.cpp command line
有没有人试一下树莓派4b上跑whisper.cpp的速度如何？我试过（以前）在aistudio上用openai-whisper（20230117）
和whisper.cpp上运行语句识别，前者是10秒左右，后者是3秒左右。后者之所以快还因为模型文件格式变了（使用ggml）。
调用方式如下（我可能需要记录一下）：
whisper 2830-3980-0043.wav --language en --model tiny.en
./main -m models/ggml-tiny.bin -f samples/audio/2830-3980-0043.wav
  • whisper.cpp on rpi4
我用树莓派4b 4gb版运行whisper.cpp成功（tiny-en模型，任务管理器里面那个main进程就是），
大概占用CPU全部100%，
占用内存150MB（增加到450MB，桌面环境下），耗时大概10秒（我之前用aistudio测试是3秒）
相当于whisper pytorch版的速度。至于树莓派4b运行whisper pytorch的速度如何，暂时还没测试。

尝试用树莓派4b 4gb运行whisper pytorch版，结果失败了，装倒是可以很容易装上去
（指定-i参数镜像加速），但运行会有错误，不会搞，等以后研究pytorch再说吧，
对我而言暂时没有多大的需要，除非我能看懂pytorch和懂得如何在树莓派上调试 ​​​
  • whisper python, audio-0.6.0.tar.gz, Makefile
	pip install openai-whisper==20230117
	whisper yes.2a6d6pep.wav --language en --model tiny.en
	(cd audio; whisper 2830-3980-0043.wav --language en --model tiny.en)
	(cd audio; whisper 4507-16021-0012.wav --language en --model tiny.en)
	(cd audio; whisper 8455-210777-0068.wav --language en --model tiny.en)
  • whisper.cpp, build.sh
./main -m models/ggml-tiny.bin -f samples/jfk.wav 
./main -m models/ggml-tiny.bin -f samples/audio/2830-3980-0043.wav
./main -m models/ggml-tiny.bin -f samples/audio/4507-16021-0012.wav
./main -m models/ggml-tiny.bin -f samples/audio/8455-210777-0068.wav
rm -rf samples/output.wav
ffmpeg -i samples/yes.2a6d6pep.wav -ar 16000 -ac 1 -c:a pcm_s16le samples/output.wav
./main -m models/ggml-tiny.bin -f samples/output.wav

micro_speech

Facebook flashlight and wav2letter, in C++

Swift机器学习：面向iOS的人工智能实战

TensorFlow Model

PaddleSpeech

(TODO?) TinyMaix, KWS example

(TODO, IMP) Demonstrator project for speech recognition featuring several technologies, with tensorflow.js

(TODO) 基于DNN和DTW算法配合VAD截取的微语音识别框架, some sources lost, keil

(TODO) LSTM + CTC (?), TensorFlow 2深度学习实战, 人民邮电出版社

我试过用aistudio跑《TensorFlow 2深度学习实战里面》那个LSTM例子代码，
的确是可以跑得通的（我用的是TensorFlow 2.3）。
不过我以前还试过另一个LSTM的语音识别，结果失败了，是这个：
《Python+TensorFlow机器学习实战》第9.2章
（可能这里已经改好了：llSourcell/tensorflow_speech_recognition_demo）。
有时间可以对照改一下。

(TODO) LSTM + CTC (???), Python自然语言处理实战

TFLearn LSTM, tensorflow_speech_recognition_demo, and 《Python+TensorFlow机器学习实战》第9.2章

(TODO, IMP, not exists now) ESP8266 Baidu ASR Demo (AT command)

app/include/rest.h, lib/librest.a, vop.baidu.com/pro_api    
int send_baidu(int sta_socket);

基于百度语音的识别语音，转文字

ESP32 语音唤醒+离线识别+百度在线识别

(TODO) DTW, 用“芯”探核：龙芯派开发实战

(TODO) TinyML Cookbook

Deep Learning for NLP and Speech Recognition

Voiceprint-Recognition

sherpa-ncnn

ThatProject, ESP32_MICROPHONE

(TODO) Keras 2D CNN+RNN+CTC

KWS-SoC——基于Wujian100的音频流关键词检测SoC拓展开发笔记之二

IoT for Beginners - A Curriculum, Recognize speech with an IoT device

PyTorch2.0深度学习从零开始学, pytorch

  • 第14章, 创建你自己的小精灵—基于MFCC的语音唤醒实战
  • PyTorch2.0深度学习从零开始学-源码.rar

(TODO) NLP

  • 中文语料库, 中文语料
  • 知识图谱, 知网, csdn, wordnet
  • search baidupan, 语料，Corpus

(TODO) Whisper.cpp for Android

gd32f450-run-tflite-micro-speech-demo

跑通梁山派gd32的tflite micro, micro speech
https://github.com/newffnow/gd32f450-run-tflite-micro-speech-demo
search, micro_speech_v3_O3_success_liangshan_mdk5_ac6.7z
我跑通了newffnow/gd32f450-run-tflite-micro-speech-demo，
对应这个《[009]检测给定音频是否包含“yes”和“no”——
单片机运行tensorflow lite的micro speech例程》，非传感器，纯数据处理。
我用梁山派测试，串口需要单独用工具接UART4的两个脚，计算速度还是比较快的。
以后有机会再尝试用传感器测试。编译这个需要改很多地方，例如：
（1）只能用-O2,-O3,-Ofast编译，其他可能会编译错误
（2）可能需要改static inline，但不一定有效，见问题1
（3）用MDK5的AC6编译，AC5不行
调试串口
https://lckfb.com/project/detail/lckfb_lspi?param=drawing
梁山派<->USB-TTL
left left 7, PD2=UART4_RX<->TXD(orange)
left right 7, PC12=UART4_TX<->RXD(yellow)
right left bottom 1, GND=GND<->GND(black)

(TODO) SYSTRAN/faster-whisper

whisper (python version, pytorch version, openai-whisper) installed by pip of raspberry pi 4b

  • 树莓派4b安装whisper（don't sudo pip install）
    Newest version of openai-whisper is ok, no need to install old version 20230117.
    After installing, need to pip uninstall torch and reinstall old version torch==1.13.1, see below.
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple openai-whisper==20230117  
pip install typing-extensions==4.3.0
/home/pi/.local/bin/whisper --help
pi@raspberrypi:~/whisper.cpp_aistudio/audio $ /home/pi/.local/bin/whisper 4507-16021-0012.wav --language en --model tiny.en

cp tiny.en.pt ./.cache/whisper/tiny.en.pt

gedit /home/pi/.local/lib/python3.9/site-packages/whisper/decoding.py
  • running failed, to solve this problem, see below, pip uninstall torch==2.0.0
vi /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/whisper/decoding.py
line 468
        print(tokenizer.sot, self.initial_tokens, "test")
        if self.initial_tokens.count(tokenizer.sot) > 0:
        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
        else:
            self.initial_tokens = (tokenizer.sot,)
            self.sot_index: int = 1

pip list | grep torch
pip uninstall torch==2.0.0
pip install torch==1.13.1
  • 转，《【RaspberryPi】Whisperで音声認識させてみた》
    我测试过这篇文章的方法，可以成功用树莓派4b的6位os运行起whisper pytorch版。方法是重新安装pytorch降级到pytorch 1，如果用pytorch 2会报错：
pip uninstall torch==2.1.0  
pip install torch==1.13.1

测试方法是：whisper 2830-3980-0043.wav --language en --model tiny.en
耗时大概是13秒

fquirin/speech-recognition-experiments, Whisper TFlite

  • https://github.com/fquirin/speech-recognition-experiments
  • 各种新兴语音识别引擎在树莓派上的性能准确率对比
  • 我记错了，实际上应该是aistudio的whisper.cpp略快于aarch安卓版whisper.cpp，aarch安卓版whisper.cpp快于树莓派4b版的whisper.cpp，而树莓派4b版的whisper.cpp快于armeabi-v7a安卓版的whisper.cpp，分别是3秒<4秒<10秒<12秒 。另外机器的性能越好，速度越快，例如平板的安卓会比手机的安卓跑whisper.cpp的速度更快
  • gh上有人评测了各种新兴语音识别引擎在树莓派4和香橙派5上的运行速度对比（RTF是实时率，没什么用），fquirin/speech-recognition-experiments，这里我注释一下，其一树莓派4上跑whisper python似乎有问题（补注：已解决，卸载torch 2重新安装1.13.1版），可能这个是作者自己编译运行的。其二我测试whisper.cpp在树莓派4b上是10秒左右，我以前记错了，我以为是3秒（3秒应该是aistudio的速度）

(TODO, TODO) Whisper TFlite (for android)

whisper.cpp for android (yeyupiaoling/Whisper-Finetune), fastest speed on Android is 4 seconds per one Engish sentence (64bit Android fastest is 4 seconds, 32bit Android fastest is 12 seconds)

  • mod from https://gitee.com/yeyupiaoling/Whisper-Finetune
  • or https://github.com/yeyupiaoling/Whisper-Finetune
  • whisper.cpp版研究。好了，目前最好的记录是一句英文的语音识别需要4秒（如果是32位安卓手机则为12秒），我这次用的代码是yeyupiaoling/Whisper-Finetune的安卓版代码，替换英文版的模型文件，然后在Application.mk指定APP_OPTIM := release（作者说的一定要打包成发布版，可以通过这种方式加速）。如果不指定这个，就会达到100多秒。那就是说：（1）必须指定APP_OPTIM（参考Whisper-Finetune）（2）必须使用最新版NDK（3）必须保证代码没修改过，使用指定正确的C和C++宏定义（我之前编译的版本用的宏定义和Whisper-Finetune里面的不同）
  • search baidupan, Whisper-Finetune-master_v2.apk
  • search baidupan, Whisper-Finetune-master_v2_very_good_redmi12c_4sec_redmi9a_12sec.7z
  • Whisper-Finetune
    https://gitee.com/yeyupiaoling/Whisper-Finetune/tree/master/AndroidDemo
    search whisper.apk

whisper.cpp for android arm64 (whisper.android.java in ggerganov/whisper.cpp/examples), 5 seconds (tested on Redmi 12C)

  • https://github.com/ggerganov/whisper.cpp/tree/master/examples/whisper.android.java
  • 快到5秒可以运行到，但编译成arm64，而且用最新NDK编译不是r10
  • search whisper.android.java_simple_v1_success_redmi12c_5sec.7z
  • search whisper.cpp-1.0.4_see_whisper_android-master.zip
  • search whisper.cpp_old_version.7z
  • 我比较过，那个运行正确且速度较快（在20秒内转换一个英语句子）的whisper.cpp安卓版，是来源于1.0.4版本附近，相当于最早的发布版（有很少的修改，但代码和1.0.4几乎一样。这个项目火起来是在2023年4月左右，大概是版本1.3左右），这么看来如果有耐心的话可以从1.0.4版一直测试到1.5.0版，找到合适自己的版本编译成安卓版测试速度，就可以知道whisper.cpp安卓版最快可以在多少秒内语音识别出一个英语句子了，粗略估计可以在10秒内，最快可以到达5秒，如果手机是稍微高档一点（比红米好，主频在2.0GHz以上）或者支持显示加速，估计可以达到树莓派4b的水平，可以在3秒左右识别出英语句子，当然这目前只是我臆想猜测的，我还没有实际测试过（我手头上配置最好的设备时华为平板，可以找时间测试）
  • search whisper.android.java_simple_v2_success_redmi12c_5sec_redmi9a_16sec.rar
  • <1> whisper 1.0.4 android：
    https://github.com/Digipom/WhisperCppAndroidDemo
  • <2> whisper tflite：
    https://github.com/vilassn/whisper_android
  • 安卓whisper.cpp.android.java上传网盘的编译文件名
    whisper.android.java_v3_2830wav_use_146630ms.7z
    https://developer.android.google.cn/ndk/guides/cpu-arm-neon?hl=zh-cn#ndk-build_1
    LOCAL_ARM_NEON := true
    https://developer.android.google.cn/ndk/guides/cpp-support?hl=zh-cn
    APP_STL 变量指定 c++_shared、c++_static、none 或 system。例如：
    https://www.itxm.cn/post/edafbg2b5.html

mozilla/Deepspeech test audio wav files

Format                         : Wave
Overall bit rate mode          : Constant
Overall bit rate               : 256 kb/s

Audio
Format                         : PCM
Format settings, Endianness    : Little
Format settings, Sign          : Signed
Codec ID                       : 1
Bit rate mode                  : Constant
Bit rate                       : 256 kb/s
Channel(s)                     : 1 channel
Sampling rate                  : 16.0 kHz
Bit depth                      : 16 bits

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(TODO) TODO list, keep putting here at last

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