thewh1teagle

Cross compile espeak-ng for aarch64 Linux

# Cross compile for aarch64 Linux


# Alsa
git clone https://github.com/alsa-project/alsa-lib
cd alsa-lib

NOTE

These are NOT product / license keys that are valid for Windows activation.
These keys only select the edition of Windows to install during setup, but they do not activate or license the installation.

Index

arch -x86_64 cmake -G Ninja -B build -DGGML_METAL=1 .
arch -x86_64 cmake --build build
wget https://github.com/thewh1teagle/vibe/raw/main/samples/short.wav
arch -x86_64 ./main --no-prints -m '/Users/user/Library/Application Support/github.com.thewh1teagle.vibe/ggml-medium-q8_0.bin' -f short.wav

Build for android and ios with clang

# IOS arm64
xcrun --sdk iphoneos --show-sdk-path
clang -target arm64-apple-ios -isysroot $(xcrun --sdk iphoneos --show-sdk-path) -o main main.c

# Android arm64
export NDK="$HOME/Library/Android/sdk/ndk/$(ls -1 $HOME/Library/Android/sdk/ndk | sort | tail -n 1)"
export CC="$NDK/toolchains/llvm/prebuilt/darwin-x86_64/bin/aarch64-linux-android35-clang"

Prepare project

mkdir project
cd project
cargo init --lib

Edit Cargo.toml and add

Install mold linker on macos

Build

cd /tmp
git clone https://github.com/bluewhalesystems/sold.git
cd sold

cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_COMPILER=clang -B build

	import sys

	with open(sys.argv[1], 'r', encoding='utf-8') as file:
	content = file.read()

	lines = []
	for line in content.splitlines():
	if line.startswith('?') or line.startswith('<i>?'):
	line = line.replace('?', '')
	line = line.replace('<i>?', '<i>')

	/**
	Supress @everyone and @here mentions across all Discord servers.
	1. Open https://discord.com
	2. Extract the token from dev tools -> Local Storage -> token
	3. Run
	*/
	const token = "your token";

	const headers = {
	authorization: token,

	"""
	git clone https://github.com/troyliu0105/pyspeexaec
	python3 -m venv venv
	source venv/bin/activate

	pip install soundfile numpy pybind11
	CC=clang++ python setup.py install

	wget https://github.com/thewh1teagle/aec-rs/releases/download/audio-files/rec.wav
	wget https://github.com/thewh1teagle/aec-rs/releases/download/audio-files/echo.wav

	'''
	Using the emotion representation model
	rec_result only contains {'feats'}
	granularity="utterance": {'feats': [*768]}
	granularity="frame": {feats: [T*768]}

	python main.py
	'''

	from funasr import AutoModel