https://twitter.com/lewangx/status/1730172194553286874
# 安装命令行依赖
brew install swig sox
# 安装 portaudio，pyaudio 依赖这个
examples/C++/install_portaudio.sh
brew install portaudio
# 安装 pyaudio，先更新 pip 和 setuptools
pip install --upgrade pip setuptools
pip install pyaudio
cd swig/Python3
# 修改 Makefile 中的 python3-config 为 python3.10-config
make
cd examples/Python3
# 修改 snowboydecoder.py 中的 from . import snowboydetect 为 import snowboydetect
# 运行 demo
python demo.py resources/models/snowboy.umdl
自定义唤醒词模型制作
使用 https://github.com/SYSTRAN/faster-whisper 项目首页的示例代码，安装完 faster-whisper 之后，直接运行。
安装 faster-whisper
pip install faster-whisper示例代码
使用 CPU 运行，将模型改为 small，并修改模型创建参数，运行时会自动下载 small 模型，无需提前下载。
from faster_whisper import WhisperModel
model_size = "small"
# Run on GPU with FP16
# model = WhisperModel(model_size, device="cuda", compute_type="float16")
# or run on GPU with INT8
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
model = WhisperModel(model_size, device="cpu", compute_type="int8")
segments, info = model.transcribe("audio.mp3", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))使用 SpeechRecognition 来录音，然后调用 faster-whisper 来做语音识别。
pip install SpeechRecognition修改 snowboy 的示例代码 demo4.py
#!env python
import snowboydecoder
import sys
import signal
import speech_recognition as sr
import os
"""
This demo file shows you how to use the new_message_callback to interact with
the recorded audio after a keyword is spoken. It uses the speech recognition
library in order to convert the recorded audio into text.
Information on installing the speech recognition library can be found at:
https://pypi.python.org/pypi/SpeechRecognition/
"""
interrupted = False
import time
from faster_whisper import WhisperModel
# model_size = "large-v3"
whisper_model_size = "models/faster-whisper-small"
whisper_model = WhisperModel(whisper_model_size, device="cpu", compute_type="int8")
def audioRecorderCallback(fname):
print("converting audio to text")
r = sr.Recognizer()
with sr.AudioFile(fname) as source:
audio = r.record(source) # read the entire audio file
# recognize speech using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
start_time = time.time()
segments, info = whisper_model.transcribe(fname, beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
print("cost", time.time() - start_time)
# print(r.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
os.remove(fname)
def detectedCallback():
print('recording audio...', end='', flush=True)
def signal_handler(signal, frame):
global interrupted
interrupted = True
def interrupt_callback():
global interrupted
return interrupted
if len(sys.argv) == 1:
print("Error: need to specify model name")
print("Usage: python demo.py your.model")
sys.exit(-1)
model = sys.argv[1]
# capture SIGINT signal, e.g., Ctrl+C
signal.signal(signal.SIGINT, signal_handler)
detector = snowboydecoder.HotwordDetector(model, sensitivity=0.38)
print('Listening... Press Ctrl+C to exit')
# main loop
detector.start(detected_callback=detectedCallback,
audio_recorder_callback=audioRecorderCallback,
interrupt_check=interrupt_callback,
sleep_time=0.01)
detector.terminate()使用网易开源的 EmotiVoice 来进行文本转语音
安装依赖
pip install torch torchaudio
pip install numpy numba scipy transformers soundfile yacs g2p_en jieba pypinyin下载 EmotiVoice
git clone https://github.com/netease-youdao/EmotiVoice.git --depth=1安装预训练模型
需要进入 EmotiVoice 目录
git clone https://www.modelscope.cn/syq163/WangZeJun.git
rm -rf outputs
git clone https://www.modelscope.cn/syq163/outputs.git运行 UI 交互界面
# 安装依赖
pip install streamlit
# 运行界面
streamlit run demo_page.py --server.port 6006 --logger.level debug运行类似 OpenAI 的 API
# 安装 ffmpeg 用来转 mp3
brew install ffmpeg
# 安装依赖
pip install fastapi pydub uvicorn
# 运行 API
python -m uvicorn openaiapi:app --reload示例代码
#!env python
import requests
# 设置API端点
api_endpoint = "http://127.0.0.1:8000/v1/audio/speech"
# 设置请求标头
headers = {
"Content-Type": "application/json"
}
# 要转换的文本内容
text_to_convert = "你好，欢迎使用OpenAI的TTS模型。"
# 构建JSON请求体
request_body = {
"input": text_to_convert,
"voice": "8051",
"model": "emoti-voice",
"language": "zh-us" # 选择语音风格（可选，根据需要添加）
}
# 发送POST请求
response = requests.post(api_endpoint, json=request_body, headers=headers)
# 解析API响应
if response.status_code == 200:
# 从响应中获取语音文件
audio_data = response.content
# 处理语音数据，例如保存为文件或进行其他处理
# 将语音数据保存为本地文件
with open("output_audio.mp3", "wb") as audio_file:
audio_file.write(audio_data)
print("语音文件已保存为output_audio.mp3")
else:
print("API请求失败：", response.status_code, response.text)测试
python test_emotivoice_api.py && afplay output_audio.mp3安装依赖
pip install socksio示例代码
from openai import OpenAI
client = OpenAI(
api_key = ""
)
print("call openai api")
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a poetic assistant, skilled in explaining complex programming concepts with creative flair."},
{"role": "user", "content": "Compose a poem that explains the concept of recursion in programming."}
]
)
print(completion.choices[0].message)