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

nichongjia-2007

2023-03-23 72d561531ffedfbefa1456f6b0c6c88466154c55

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 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 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 @@
    


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 @@
    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 @@
     
    await asyncio.gather(task, task2)

asyncio.run(main())
asyncio.run(main())

 funasr/runtime/python/websocket/ASR_server.py

@@ -6,37 +6,73 @@

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 @@
    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 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 @@
            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.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

New file
@@ -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

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

 funasr/runtime/python/websocket/requirements_server.txt

New file
@@ -0,0 +1 @@
websockets