Merge branch 'main' of github.com:alibaba-damo-academy/FunASR

funasr/bin/vad_inference_online.py

@@ -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):

funasr/runtime/python/websocket/ASR_client.py

@@ -5,13 +5,35 @@ import websockets
 import asyncio
 from queue import Queue
 # import threading
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--host",
+                    type=str,
+                    default="localhost",
+                    required=False,
+                    help="host ip, localhost, 0.0.0.0")
+parser.add_argument("--port",
+                    type=int,
+                    default=10095,
+                    required=False,
+                    help="grpc server port")
+parser.add_argument("--chunk_size",
+                    type=int,
+                    default=300,
+                    help="ms")
+
+args = parser.parse_args()
+
 voices = Queue()
-async def hello():
+async def ws_client():
     global ws # 定义一个全局变量ws，用于保存websocket连接对象
-    uri = "ws://localhost:8899"
+    # uri = "ws://11.167.134.197:8899"
+    uri = "ws://{}:{}".format(args.host, args.port)
     ws = await websockets.connect(uri, subprotocols=["binary"]) # 创建一个长连接
     ws.max_size = 1024 * 1024 * 20
     print("connected ws server")
+    
 async def send(data):
     global ws # 引用全局变量ws
     try:
@@ -21,7 +43,7 @@ async def send(data):
     
 
 
-asyncio.get_event_loop().run_until_complete(hello()) # 启动协程  
+asyncio.get_event_loop().run_until_complete(ws_client()) # 启动协程
 
 
 # 其他函数可以通过调用send(data)来发送数据，例如：
@@ -31,7 +53,7 @@ async def test():
     FORMAT = pyaudio.paInt16
     CHANNELS = 1
     RATE = 16000
-    CHUNK = int(RATE / 1000 * 300)
+    CHUNK = int(RATE / 1000 * args.chunk_size)
 
     p = pyaudio.PyAudio()
 
@@ -70,4 +92,4 @@ async def main():
      
     await asyncio.gather(task, task2)
 
-asyncio.run(main())
+asyncio.run(main())

funasr/runtime/python/websocket/ASR_server.py

@@ -6,37 +6,73 @@ import logging
 
 logger = get_logger(log_level=logging.CRITICAL)
 logger.setLevel(logging.CRITICAL)
+
 import asyncio
-import websockets  #区别客户端这里是 websockets库
+import websockets
 import time
 from queue import Queue
 import threading
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--host",
+                    type=str,
+                    default="0.0.0.0",
+                    required=False,
+                    help="host ip, localhost, 0.0.0.0")
+parser.add_argument("--port",
+                    type=int,
+                    default=10095,
+                    required=False,
+                    help="grpc server port")
+parser.add_argument("--asr_model",
+                    type=str,
+                    default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+                    help="model from modelscope")
+parser.add_argument("--vad_model",
+                    type=str,
+                    default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+                    help="model from modelscope")
+
+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()
 
 print("model loading")
 voices = Queue()
 speek = Queue()
+
 # 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=args.vad_model,
     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()
-param_dict["hotword"] = "小五 小五月"  # 设置热词，用空格隔开
+# param_dict["hotword"] = "小五 小五月"  # 设置热词，用空格隔开
 inference_pipeline2 = pipeline(
     task=Tasks.auto_speech_recognition,
-    model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
+    model=args.asr_model,
     param_dict=param_dict,
 )
 print("model loaded")
 
 
 
-async def echo(websocket, path):
+async def ws_serve(websocket, path):
     global voices
     try:
         async for message in websocket:
@@ -47,18 +83,26 @@ async def echo(websocket, path):
     except Exception as e:
         print('Exception occurred:', e)
 
-start_server = websockets.serve(echo, "localhost", 8899, subprotocols=["binary"],ping_interval=None)
+start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
 
 
 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
@@ -76,11 +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():
             
@@ -91,32 +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)
             
@@ -128,16 +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()

funasr/runtime/python/websocket/README.md

@@ -0,0 +1,46 @@
+# Using funasr with websocket
+We can send streaming audio data to server in real-time with grpc client every 300 ms e.g., and get transcribed text when stop speaking.
+The audio data is in streaming, the asr inference process is in offline.
+
+# Steps
+
+## For the Server
+
+Install the modelscope and funasr
+
+```shell
+pip install "modelscope[audio_asr]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+git clone https://github.com/alibaba/FunASR.git && cd FunASR
+pip install --editable ./
+```
+
+Install the requirements for server
+
+```shell
+cd funasr/runtime/python/websocket
+pip install -r requirements_server.txt
+```
+
+Start server
+
+```shell
+python ASR_server.py --host "0.0.0.0" --port 10095 --asr_model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+```
+
+## For the client
+
+Install the requirements for client
+```shell
+git clone https://github.com/alibaba/FunASR.git && cd FunASR
+cd funasr/runtime/python/websocket
+pip install -r requirements_client.txt
+```
+
+Start client
+
+```shell
+python ASR_client.py --host "127.0.0.1" --port 10095 --chunk_size 300
+```
+
+## Acknowledge
+1. We acknowledge [cgisky1980](https://github.com/cgisky1980/FunASR) for contributing the websocket service.

funasr/runtime/python/websocket/requirements_client.txt

@@ -0,0 +1,2 @@
+websockets
+pyaudio

funasr/runtime/python/websocket/requirements_server.txt

@@ -0,0 +1 @@
+websockets