speech_recognition/FunASR
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Merge pull request #285 from alibaba-damo-academy/dev_gzf

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Dev gzf
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zhifu gao 2023-03-23 17:16:14 +08:00 committed by GitHub
commit ba4256ecab
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2 changed files with 53 additions and 54 deletions
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7
funasr/bin/vad_inference_online.py
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@ -30,14 +30,7 @@ from funasr.models.frontend.wav_frontend import WavFrontendOnline
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.bin.vad_inference import Speech2VadSegment
header_colors = '\033[95m'
end_colors = '\033[0m'
global_asr_language: str = 'zh-cn'
global_sample_rate: Union[int, Dict[Any, int]] = {
'audio_fs': 16000,
'model_fs': 16000
}
class Speech2VadSegmentOnline(Speech2VadSegment):

100
funasr/runtime/python/websocket/ASR_server.py
View File

@ -38,6 +38,10 @@ parser.add_argument("--punc_model",
type=str,
default="",
help="model from modelscope")
parser.add_argument("--ngpu",
type=int,
default=1,
help="0 for cpu, 1 for gpu")
args = parser.parse_args()
@ -52,7 +56,9 @@ vad_pipline = pipeline(
model_revision="v1.2.0",
output_dir=None,
batch_size=1,
mode='online'
)
param_dict_vad = {'in_cache': dict(), "is_final": False}
# 创建一个ASR对象
param_dict = dict()
@ -81,14 +87,22 @@ start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["b
def vad(data): # 推理
global vad_pipline
global vad_pipline, param_dict_vad
#print(type(data))
segments_result = vad_pipline(audio_in=data)
#print(segments_result)
if len(segments_result) == 0:
return False
else:
return True
# print(param_dict_vad)
segments_result = vad_pipline(audio_in=data, param_dict=param_dict_vad)
# print(segments_result)
# print(param_dict_vad)
speech_start = False
speech_end = False
if len(segments_result) == 0 or len(segments_result["text"]) > 1:
return speech_start, speech_end
if segments_result["text"][0][0] != -1:
speech_start = True
if segments_result["text"][0][1] != -1:
speech_end = True
return speech_start, speech_end
def asr(): # 推理
global inference_pipeline2
@ -106,11 +120,12 @@ def asr(): # 推理
def main(): # 推理
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
# silence_count = 0 # 统计连续静音的次数
# speech_detected = False # 标记是否检测到语音
RECORD_NUM = 0
global voices
global speek
speech_start, speech_end = False, False
while True:
while not voices.empty():
@ -121,32 +136,35 @@ def main(): # 推理
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:
if speech_start:
frames.append(data)
RECORD_NUM += 1
if vad(data):
if not speech_detected:
print("检测到人声...")
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
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
print("说话结束或者超过设置最长时间...")
audio_in = b"".join(frames)
#asrt = threading.Thread(target=asr,args=(audio_in,))
#asrt.start()
speek.put(audio_in)
#rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑
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
RECORD_NUM += 1
speech_start_i, speech_end_i = vad(data)
# print(speech_start_i, speech_end_i)
if speech_start_i:
speech_start = speech_start_i
# if not speech_detected:
# print("检测到人声...")
# 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
if speech_end_i or RECORD_NUM > 300:
# silence_count += 1 # 增加静音次数
# speech_end = speech_end_i
speech_start = False
# if RECORD_NUM > 300: #这里 50 可根据需求改为合适的数据快数量
# print("说话结束或者超过设置最长时间...")
audio_in = b"".join(frames)
#asrt = threading.Thread(target=asr,args=(audio_in,))
#asrt.start()
speek.put(audio_in)
#rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑
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
time.sleep(0.01)
time.sleep(0.01)
@ -158,16 +176,4 @@ s = threading.Thread(target=asr)
s.start()
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()
asyncio.get_event_loop().run_forever()