diff --git a/funasr/runtime/python/vad_asr_websocket_client/vad_asr_websocket_client.py b/funasr/runtime/python/vad_asr_websocket_client/vad_asr_websocket_client.py deleted file mode 100644 index 7f5ed539b..000000000 --- a/funasr/runtime/python/vad_asr_websocket_client/vad_asr_websocket_client.py +++ /dev/null @@ -1,197 +0,0 @@ -#""" from https://github.com/cgisky1980/550W_AI_Assistant """ - -from modelscope.pipelines import pipeline -from modelscope.utils.constant import Tasks -from modelscope.utils.logger import get_logger -import logging -logger = get_logger(log_level=logging.CRITICAL) -logger.setLevel(logging.CRITICAL) -import websocket -import pyaudio -import time -import json -import threading - - -# ---------WebsocketClient相关 主要处理 on_message on_open 已经做了断线重连处理 -class WebsocketClient(object): - def __init__(self, address, message_callback=None): - super(WebsocketClient, self).__init__() - self.address = address - self.message_callback = None - - def on_message(self, ws, message): - try: - messages = json.loads( - (message.encode("raw_unicode_escape")).decode() - ) # 收到WS消息后的处理 - if messages.get("type") == "ping": - self.ws.send('{"type":"pong"}') - except json.JSONDecodeError as e: - print(f"JSONDecodeError: {e}") - except KeyError: - print("KeyError!") - - def on_error(self, ws, error): - print("client error:", error) - - def on_close(self, ws): - print("### client closed ###") - self.ws.close() - self.is_running = False - - def on_open(self, ws): # 连上ws后发布登录信息 - self.is_running = True - self.ws.send( - '{"type":"login","uid":"asr","pwd":"tts9102093109"}' - ) # WS链接上后的登陆处理 - - def close_connect(self): - self.ws.close() - - def send_message(self, message): - try: - self.ws.send(message) - except BaseException as err: - pass - - def run(self): # WS初始化 - websocket.enableTrace(True) - self.ws = websocket.WebSocketApp( - self.address, - on_message=lambda ws, message: self.on_message(ws, message), - on_error=lambda ws, error: self.on_error(ws, error), - on_close=lambda ws: self.on_close(ws), - ) - websocket.enableTrace(False) # 要看ws调试信息，请把这行注释掉 - self.ws.on_open = lambda ws: self.on_open(ws) - self.is_running = False - # WS断线重连判断 - while True: - if not self.is_running: - self.ws.run_forever() - time.sleep(3) # 3秒检测一次 - - -class WSClient(object): - def __init__(self, address, call_back): - super(WSClient, self).__init__() - self.client = WebsocketClient(address, call_back) - self.client_thread = None - - def run(self): - self.client_thread = threading.Thread(target=self.run_client) - self.client_thread.start() - - def run_client(self): - self.client.run() - - def send_message(self, message): - self.client.send_message(message) - - -def vad(data): # VAD推理 - segments_result = vad_pipline(audio_in=data) - if segments_result["text"] == "[]": - return False - else: - return True - - -# 创建一个VAD对象 -vad_pipline = pipeline( - task=Tasks.voice_activity_detection, - model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", - model_revision="v1.2.0", - output_dir=None, - batch_size=1, -) - -param_dict = dict() -param_dict["hotword"] = "小五 小五月" # 设置热词，用空格隔开 - - -# 创建一个ASR对象 -inference_pipeline2 = pipeline( - task=Tasks.auto_speech_recognition, - model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", - param_dict=param_dict, -) - -# 创建一个PyAudio对象 -p = pyaudio.PyAudio() - -# 定义一些参数 -FORMAT = pyaudio.paInt16 # 采样格式 -CHANNELS = 1 # 单声道 -RATE = 16000 # 采样率 -CHUNK = int(RATE / 1000 * 300) # 每个片段的帧数（300毫秒） -RECORD_NUM = 0 # 录制时长（片段） - -# 打开输入流 -stream = p.open( - format=FORMAT, - channels=CHANNELS, - rate=RATE, - input=True, - frames_per_buffer=CHUNK, -) - -print("开始...") - -# 创建一个WS连接 -ws_client = WSClient("ws://localhost:7272", None) -ws_client.run() - -frames = [] # 存储所有的帧数据 -buffer = [] # 存储缓存中的帧数据（最多两个片段） -silence_count = 0 # 统计连续静音的次数 -speech_detected = False # 标记是否检测到语音 - -# 循环读取输入流中的数据 -while True: - data = stream.read(CHUNK) # 读取一个片段的数据 - buffer.append(data) # 将当前数据添加到缓存中 - - if len(buffer) > 2: - buffer.pop(0) # 如果缓存超过两个片段，则删除最早的一个 - - if speech_detected: - frames.append(data) - RECORD_NUM += 1 - # print(str(RECORD_NUM)+ "\r") - - if vad(data): # VAD 判断是否有声音 - if not speech_detected: - print("开始录音...") - speech_detected = True # 标记为检测到语音 - frames = [] - frames.extend(buffer) # 把之前2个语音数据快加入 - silence_count = 0 # 重置静音次数 - - else: - silence_count += 1 # 增加静音次数 - #检测静音次数4次 或者已经录了50个数据块，则录音停止 - if speech_detected and (silence_count > 4 or RECORD_NUM > 50): - print("停止录音...") - audio_in = b"".join(frames) - rec_result = inference_pipeline2(audio_in=audio_in) # ws播报数据 - rec_result["type"] = "nlp" # 添加ws播报数据 - ws_client.send_message( - json.dumps(rec_result, ensure_ascii=False) - ) # ws发送到服务端 - print(rec_result) - frames = [] # 清空所有的帧数据 - buffer = [] # 清空缓存中的帧数据（最多两个片段） - silence_count = 0 # 统计连续静音的次数清零 - speech_detected = False # 标记是否检测到语音 - RECORD_NUM = 0 - -print("结束录制...") - -# 停止并关闭输入流 -stream.stop_stream() -stream.close() - -# 关闭PyAudio对象 -p.terminate() diff --git a/funasr/runtime/python/vad_asr_websocket_client/ws_server_demo.py b/funasr/runtime/python/vad_asr_websocket_client/ws_server_demo.py deleted file mode 100644 index 55d0315ce..000000000 --- a/funasr/runtime/python/vad_asr_websocket_client/ws_server_demo.py +++ /dev/null @@ -1,13 +0,0 @@ -# server.py -import asyncio -import websockets - -async def echo(websocket, path): - async for message in websocket: - print(message) # 打印收到的消息 - await websocket.send(message) - -start_server = websockets.serve(echo, "localhost", 7272) - -asyncio.get_event_loop().run_until_complete(start_server) -asyncio.get_event_loop().run_forever() \ No newline at end of file diff --git a/funasr/runtime/websocket/ASR_client.py b/funasr/runtime/python/websocket/ASR_client.py similarity index 100% rename from funasr/runtime/websocket/ASR_client.py rename to funasr/runtime/python/websocket/ASR_client.py diff --git a/funasr/runtime/websocket/ASR_server.py b/funasr/runtime/python/websocket/ASR_server.py similarity index 100% rename from funasr/runtime/websocket/ASR_server.py rename to funasr/runtime/python/websocket/ASR_server.py