websocket

This commit is contained in:
游雁 2023-03-23 15:41:57 +08:00
parent b006d268cd
commit 4798614e68

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@ -85,10 +85,15 @@ def vad(data): # 推理
#print(type(data)) #print(type(data))
segments_result = vad_pipline(audio_in=data) segments_result = vad_pipline(audio_in=data)
#print(segments_result) #print(segments_result)
if len(segments_result) == 0: speech_start = False
speech_end = False
if len(segments_result) == 0 or len(segments_result["text"] > 1):
return False return False
else: elif segments_result["text"][0][0] != -1:
return True speech_start = True
elif segments_result["text"][0][1] != -1:
speech_end = True
return speech_start, speech_end
def asr(): # 推理 def asr(): # 推理
global inference_pipeline2 global inference_pipeline2
@ -106,11 +111,12 @@ def asr(): # 推理
def main(): # 推理 def main(): # 推理
frames = [] # 存储所有的帧数据 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) 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 # silence_count = 0 # 统计连续静音的次数
speech_detected = False # 标记是否检测到语音 # speech_detected = False # 标记是否检测到语音
RECORD_NUM = 0 RECORD_NUM = 0
global voices global voices
global speek global speek
speech_start, speech_end = False, False
while True: while True:
while not voices.empty(): while not voices.empty():
@ -121,32 +127,34 @@ def main(): # 推理
if len(buffer) > 2: 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 buffer.pop(0) # 如果缓存超过两个片段,则删除最早的一个
if speech_detected: if speech_start:
frames.append(data) frames.append(data)
RECORD_NUM += 1 RECORD_NUM += 1
speech_start_i, speech_end_i = vad(data)
if vad(data): if speech_start_i:
if not speech_detected: speech_start = speech_start_i
print("检测到人声...") # if not speech_detected:
speech_detected = True # 标记为检测到语音 print("检测到人声...")
frames = [] # speech_detected = True # 标记为检测到语音
frames.extend(buffer) # 把之前2个语音数据快加入 frames = []
silence_count = 0 # 重置静音次数 frames.extend(buffer) # 把之前2个语音数据快加入
else: # silence_count = 0 # 重置静音次数
silence_count += 1 # 增加静音次数 elif speech_end_i or RECORD_NUM > 300:
# silence_count += 1 # 增加静音次数
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 # speech_end = speech_end_i
print("说话结束或者超过设置最长时间...") speech_start = False
audio_in = b"".join(frames) # if RECORD_NUM > 300: #这里 50 可根据需求改为合适的数据快数量
#asrt = threading.Thread(target=asr,args=(audio_in,)) print("说话结束或者超过设置最长时间...")
#asrt.start() audio_in = b"".join(frames)
speek.put(audio_in) #asrt = threading.Thread(target=asr,args=(audio_in,))
#rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑 #asrt.start()
frames = [] # 清空所有的帧数据 speek.put(audio_in)
buffer = [] # 清空缓存中的帧数据(最多两个片段) #rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑
silence_count = 0 # 统计连续静音的次数清零 frames = [] # 清空所有的帧数据
speech_detected = False # 标记是否检测到语音 buffer = [] # 清空缓存中的帧数据(最多两个片段)
RECORD_NUM = 0 # 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
time.sleep(0.01) time.sleep(0.01)
time.sleep(0.01) time.sleep(0.01)