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| | | logger = get_logger(log_level=logging.CRITICAL) |
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| | | logger.setLevel(logging.CRITICAL) |
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| | | |
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| | | import asyncio |
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| | | import websockets #区别客户端这里是 websockets库 |
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| | | import websockets |
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| | | import time |
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| | | from queue import Queue |
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| | | import threading |
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| | | import argparse |
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| | | |
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| | | parser = argparse.ArgumentParser() |
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| | | parser.add_argument("--host", |
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| | | type=str, |
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| | | default="0.0.0.0", |
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| | | required=False, |
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| | | help="host ip, localhost, 0.0.0.0") |
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| | | parser.add_argument("--port", |
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| | | type=int, |
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| | | default=10095, |
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| | | required=False, |
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| | | help="grpc server port") |
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| | | parser.add_argument("--asr_model", |
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| | | type=str, |
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| | | default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", |
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| | | help="model from modelscope") |
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| | | parser.add_argument("--vad_model", |
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| | | type=str, |
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| | | default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", |
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| | | help="model from modelscope") |
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| | | |
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| | | parser.add_argument("--punc_model", |
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| | | type=str, |
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| | | default="", |
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| | | help="model from modelscope") |
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| | | parser.add_argument("--ngpu", |
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| | | type=int, |
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| | | default=1, |
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| | | help="0 for cpu, 1 for gpu") |
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| | | |
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| | | args = parser.parse_args() |
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| | | |
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| | | print("model loading") |
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| | | voices = Queue() |
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| | | speek = Queue() |
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| | | |
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| | | # 创建一个VAD对象 |
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| | | vad_pipline = pipeline( |
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| | | task=Tasks.voice_activity_detection, |
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| | | model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", |
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| | | model=args.vad_model, |
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| | | model_revision="v1.2.0", |
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| | | output_dir=None, |
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| | | batch_size=1, |
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| | | mode='online' |
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| | | ) |
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| | | param_dict_vad = {'in_cache': dict(), "is_final": False} |
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| | | |
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| | | # 创建一个ASR对象 |
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| | | param_dict = dict() |
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| | | param_dict["hotword"] = "小五 小五月" # 设置热词，用空格隔开 |
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| | | # param_dict["hotword"] = "小五 小五月" # 设置热词，用空格隔开 |
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| | | inference_pipeline2 = pipeline( |
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| | | task=Tasks.auto_speech_recognition, |
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| | | model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", |
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| | | model=args.asr_model, |
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| | | param_dict=param_dict, |
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| | | ) |
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| | | print("model loaded") |
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| | | |
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| | | |
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| | | |
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| | | async def echo(websocket, path): |
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| | | async def ws_serve(websocket, path): |
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| | | global voices |
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| | | try: |
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| | | async for message in websocket: |
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| | |
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| | | except Exception as e: |
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| | | print('Exception occurred:', e) |
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| | | |
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| | | start_server = websockets.serve(echo, "localhost", 8899, subprotocols=["binary"],ping_interval=None) |
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| | | start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None) |
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| | | |
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| | | |
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| | | def vad(data): # 推理 |
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| | | global vad_pipline |
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| | | global vad_pipline, param_dict_vad |
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| | | #print(type(data)) |
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| | | segments_result = vad_pipline(audio_in=data) |
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| | | #print(segments_result) |
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| | | if len(segments_result) == 0: |
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| | | return False |
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| | | else: |
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| | | return True |
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| | | # print(param_dict_vad) |
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| | | segments_result = vad_pipline(audio_in=data, param_dict=param_dict_vad) |
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| | | # print(segments_result) |
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| | | # print(param_dict_vad) |
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| | | speech_start = False |
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| | | speech_end = False |
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| | | |
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| | | if len(segments_result) == 0 or len(segments_result["text"]) > 1: |
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| | | return speech_start, speech_end |
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| | | if segments_result["text"][0][0] != -1: |
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| | | speech_start = True |
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| | | if segments_result["text"][0][1] != -1: |
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| | | speech_end = True |
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| | | return speech_start, speech_end |
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| | | |
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| | | def asr(): # 推理 |
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| | | global inference_pipeline2 |
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| | |
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| | | def main(): # 推理 |
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| | | frames = [] # 存储所有的帧数据 |
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| | | buffer = [] # 存储缓存中的帧数据（最多两个片段） |
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| | | silence_count = 0 # 统计连续静音的次数 |
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| | | speech_detected = False # 标记是否检测到语音 |
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| | | # silence_count = 0 # 统计连续静音的次数 |
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| | | # speech_detected = False # 标记是否检测到语音 |
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| | | RECORD_NUM = 0 |
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| | | global voices |
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| | | global speek |
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| | | speech_start, speech_end = False, False |
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| | | while True: |
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| | | while not voices.empty(): |
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| | | |
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| | |
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| | | if len(buffer) > 2: |
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| | | buffer.pop(0) # 如果缓存超过两个片段，则删除最早的一个 |
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| | | |
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| | | if speech_detected: |
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| | | if speech_start: |
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| | | frames.append(data) |
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| | | RECORD_NUM += 1 |
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| | | |
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| | | if vad(data): |
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| | | if not speech_detected: |
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| | | print("检测到人声...") |
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| | | speech_detected = True # 标记为检测到语音 |
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| | | frames = [] |
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| | | frames.extend(buffer) # 把之前2个语音数据快加入 |
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| | | silence_count = 0 # 重置静音次数 |
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| | | else: |
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| | | silence_count += 1 # 增加静音次数 |
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| | | |
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| | | if speech_detected and (silence_count > 4 or RECORD_NUM > 50): #这里 50 可根据需求改为合适的数据快数量 |
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| | | print("说话结束或者超过设置最长时间...") |
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| | | audio_in = b"".join(frames) |
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| | | #asrt = threading.Thread(target=asr,args=(audio_in,)) |
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| | | #asrt.start() |
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| | | speek.put(audio_in) |
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| | | #rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑 |
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| | | frames = [] # 清空所有的帧数据 |
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| | | buffer = [] # 清空缓存中的帧数据（最多两个片段） |
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| | | silence_count = 0 # 统计连续静音的次数清零 |
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| | | speech_detected = False # 标记是否检测到语音 |
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| | | RECORD_NUM = 0 |
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| | | RECORD_NUM += 1 |
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| | | speech_start_i, speech_end_i = vad(data) |
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| | | # print(speech_start_i, speech_end_i) |
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| | | if speech_start_i: |
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| | | speech_start = speech_start_i |
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| | | # if not speech_detected: |
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| | | # print("检测到人声...") |
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| | | # speech_detected = True # 标记为检测到语音 |
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| | | frames = [] |
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| | | frames.extend(buffer) # 把之前2个语音数据快加入 |
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| | | # silence_count = 0 # 重置静音次数 |
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| | | if speech_end_i or RECORD_NUM > 300: |
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| | | # silence_count += 1 # 增加静音次数 |
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| | | # speech_end = speech_end_i |
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| | | speech_start = False |
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| | | # if RECORD_NUM > 300: #这里 50 可根据需求改为合适的数据快数量 |
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| | | # print("说话结束或者超过设置最长时间...") |
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| | | audio_in = b"".join(frames) |
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| | | #asrt = threading.Thread(target=asr,args=(audio_in,)) |
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| | | #asrt.start() |
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| | | speek.put(audio_in) |
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| | | #rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑 |
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| | | frames = [] # 清空所有的帧数据 |
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| | | buffer = [] # 清空缓存中的帧数据（最多两个片段） |
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| | | # silence_count = 0 # 统计连续静音的次数清零 |
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| | | # speech_detected = False # 标记是否检测到语音 |
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| | | RECORD_NUM = 0 |
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| | | time.sleep(0.01) |
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| | | time.sleep(0.01) |
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| | | |
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| | | s.start() |
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| | | |
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| | | asyncio.get_event_loop().run_until_complete(start_server) |
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| | | asyncio.get_event_loop().run_forever() |
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| | | asyncio.get_event_loop().run_forever() |
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