websocket

游雁

2023-04-27 7e0652f8d5701e5952a1c81770de4e06e0019f9b

websocket

 funasr/runtime/python/websocket/ASR_client.py

@@ -1,5 +1,4 @@

# import websocket #区别服务端这里是 websocket-client库
# -*- encoding: utf-8 -*-
import time
import websockets
import asyncio
@@ -50,18 +49,21 @@
                    rate=RATE,
                    input=True,
                    frames_per_buffer=CHUNK)

    is_speaking = True
    while True:

        data = stream.read(CHUNK)
        data = data.decode('ISO-8859-1')
        message = json.dumps({"chunk": args.chunk_size, "is_speaking": is_speaking, "audio": data})
        
        voices.put(data)
        voices.put(message)
        #print(voices.qsize())

        await asyncio.sleep(0.01)

# 其他函数可以通过调用send(data)来发送数据，例如：
async def record_from_scp():
    import wave
    global voices
    if args.audio_in.endswith(".scp"):
        f_scp = open(args.audio_in)
@@ -71,15 +73,31 @@
    for wav in wavs:
        wav_splits = wav.strip().split()
        wav_path = wav_splits[1] if len(wav_splits) > 1 else wav_splits[0]
        bytes = open(wav_path, "rb")
        bytes = bytes.read()
        
        # bytes_f = open(wav_path, "rb")
        # bytes_data = bytes_f.read()
        with wave.open(wav_path, "rb") as wav_file:
            # 获取音频参数
            params = wav_file.getparams()
            # 获取头信息的长度
            # header_length = wav_file.getheaders()[0][1]
            # 读取音频帧数据，跳过头信息
            # wav_file.setpos(header_length)
            frames = wav_file.readframes(wav_file.getnframes())

        # 将音频帧数据转换为字节类型的数据
        audio_bytes = bytes(frames)
        stride = int(args.chunk_size/1000*16000*2)
        chunk_num = (len(bytes)-1)//stride + 1
        chunk_num = (len(audio_bytes)-1)//stride + 1
        print(stride)
        is_speaking = True
        for i in range(chunk_num):
            if i == chunk_num-1:
                is_speaking = False
            beg = i*stride
            data_chunk = bytes[beg:beg+stride]
            voices.put(data_chunk)
            data = audio_bytes[beg:beg+stride]
            data = data.decode('ISO-8859-1')
            message = json.dumps({"chunk": args.chunk_size, "is_speaking": is_speaking, "audio": data})
            voices.put(message)
            # print("data_chunk: ", len(data_chunk))
            # print(voices.qsize())
        

 funasr/runtime/python/websocket/ASR_server_streaming.py

New file
@@ -0,0 +1,261 @@
import asyncio
import json
import websockets
import time
from queue import Queue
import threading
import argparse

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
import tracemalloc
import numpy as np

tracemalloc.start()

logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)


websocket_users = set()  #维护客户端列表

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="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
                    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")

def load_bytes(input):
    middle_data = np.frombuffer(input, dtype=np.int16)
    middle_data = np.asarray(middle_data)
    if middle_data.dtype.kind not in 'iu':
        raise TypeError("'middle_data' must be an array of integers")
    dtype = np.dtype('float32')
    if dtype.kind != 'f':
        raise TypeError("'dtype' must be a floating point type")

    i = np.iinfo(middle_data.dtype)
    abs_max = 2 ** (i.bits - 1)
    offset = i.min + abs_max
    array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
    return array

# vad
inference_pipeline_vad = pipeline(
    task=Tasks.voice_activity_detection,
    model=args.vad_model,
    model_revision=None,
    output_dir=None,
    batch_size=1,
    mode='online',
    ngpu=args.ngpu,
)
# param_dict_vad = {'in_cache': dict(), "is_final": False}
  
# # asr
# param_dict_asr = {}
# # param_dict["hotword"] = "小五 小五月"  # 设置热词，用空格隔开
# inference_pipeline_asr = pipeline(
#     task=Tasks.auto_speech_recognition,
#     model=args.asr_model,
#     param_dict=param_dict_asr,
#     ngpu=args.ngpu,
# )
# if args.punc_model != "":
#     # param_dict_punc = {'cache': list()}
#     inference_pipeline_punc = pipeline(
#         task=Tasks.punctuation,
#         model=args.punc_model,
#         model_revision=None,
#         ngpu=args.ngpu,
#     )
# else:
#     inference_pipeline_punc = None


inference_pipeline_asr_online = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
    model_revision=None)


print("model loaded")



async def ws_serve(websocket, path):
    #speek = Queue()
    frames = []  # 存储所有的帧数据
    frames_online = []  # 存储所有的帧数据
    buffer = []  # 存储缓存中的帧数据（最多两个片段）
    RECORD_NUM = 0
    global websocket_users
    speech_start, speech_end = False, False
    # 调用asr函数
    websocket.param_dict_vad = {'in_cache': dict(), "is_final": False}
    websocket.param_dict_punc = {'cache': list()}
    websocket.speek = Queue()  #websocket 添加进队列对象 让asr读取语音数据包
    websocket.send_msg = Queue()   #websocket 添加个队列对象  让ws发送消息到客户端
    websocket_users.add(websocket)
    # ss = threading.Thread(target=asr, args=(websocket,))
    # ss.start()
    
    websocket.param_dict_asr_online = {"cache": dict(), "is_final": False}
    websocket.speek_online = Queue()  # websocket 添加进队列对象 让asr读取语音数据包
    ss_online = threading.Thread(target=asr_online, args=(websocket,))
    ss_online.start()
    
    try:
        async for data in websocket:
            #voices.put(message)
            #print("put")
            #await websocket.send("123")
            
            data = json.loads(data)
            # message = data["data"]
            message = bytes(data['audio'], 'ISO-8859-1')
            chunk = data["chunk"]
            chunk_num = 600//chunk
            is_speaking = data["is_speaking"]
            websocket.param_dict_vad["is_final"] = not is_speaking
            buffer.append(message)
            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_start:
                # frames.append(message)
                frames_online.append(message)
                # RECORD_NUM += 1
                if len(frames_online) % chunk_num == 0:
                    audio_in = b"".join(frames_online)
                    websocket.speek_online.put(audio_in)
                    frames_online = []

            speech_start_i, speech_end_i = vad(message, websocket)
            #print(speech_start_i, speech_end_i)
            if speech_start_i:
                # RECORD_NUM += 1
                speech_start = speech_start_i
                # frames = []
                # frames.extend(buffer)  # 把之前2个语音数据快加入
                frames_online = []
                # frames_online.append(message)
                frames_online.extend(buffer)
                # RECORD_NUM += 1
                websocket.param_dict_asr_online["is_final"] = False
            if speech_end_i:
                speech_start = False
                # audio_in = b"".join(frames)
                # websocket.speek.put(audio_in)
                # frames = []  # 清空所有的帧数据
                frames_online = []
                websocket.param_dict_asr_online["is_final"] = True
                # buffer = []  # 清空缓存中的帧数据（最多两个片段）
                # RECORD_NUM = 0
            if not websocket.send_msg.empty():
                await websocket.send(websocket.send_msg.get())
                websocket.send_msg.task_done()

     
    except websockets.ConnectionClosed:
        print("ConnectionClosed...", websocket_users)    # 链接断开
        websocket_users.remove(websocket)
    except websockets.InvalidState:
        print("InvalidState...")    # 无效状态
    except Exception as e:
        print("Exception:", e)
 

# def asr(websocket):  # ASR推理
#         global inference_pipeline_asr
#         # global param_dict_punc
#         global websocket_users
#         while websocket in  websocket_users:
#             if not websocket.speek.empty():
#                 audio_in = websocket.speek.get()
#                 websocket.speek.task_done()
#                 if len(audio_in) > 0:
#                     rec_result = inference_pipeline_asr(audio_in=audio_in)
#                     if inference_pipeline_punc is not None and 'text' in rec_result:
#                         rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict=websocket.param_dict_punc)
#                     # print(rec_result)
#                     if "text" in rec_result:
#                         message = json.dumps({"mode": "offline", "text": rec_result["text"]})
#                         websocket.send_msg.put(message)  # 存入发送队列  直接调用send发送不了
#
#             time.sleep(0.1)


def asr_online(websocket):  # ASR推理
    global inference_pipeline_asr_online
    # global param_dict_punc
    global websocket_users
    while websocket in websocket_users:
        if not websocket.speek_online.empty():
            audio_in = websocket.speek_online.get()
            websocket.speek_online.task_done()
            if len(audio_in) > 0:
                # print(len(audio_in))
                audio_in = load_bytes(audio_in)
                # print(audio_in.shape)
                rec_result = inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online)

                # print(rec_result)
                if "text" in rec_result:
                    if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice":
                        message = json.dumps({"mode": "online", "text": rec_result["text"]})
                        websocket.send_msg.put(message)  # 存入发送队列  直接调用send发送不了
        
        time.sleep(0.1)

def vad(data, websocket):  # VAD推理
    global inference_pipeline_vad, param_dict_vad
    #print(type(data))
    # print(param_dict_vad)
    segments_result = inference_pipeline_vad(audio_in=data, param_dict=websocket.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

 
start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()

 funasr/runtime/python/websocket/ASR_server_streaming_asr.py

New file
@@ -0,0 +1,149 @@
import asyncio
import json
import websockets
import time
from queue import Queue
import threading
import argparse

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
import tracemalloc
import numpy as np

tracemalloc.start()

logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)


websocket_users = set()  #维护客户端列表

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="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
                    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")

def load_bytes(input):
    middle_data = np.frombuffer(input, dtype=np.int16)
    middle_data = np.asarray(middle_data)
    if middle_data.dtype.kind not in 'iu':
        raise TypeError("'middle_data' must be an array of integers")
    dtype = np.dtype('float32')
    if dtype.kind != 'f':
        raise TypeError("'dtype' must be a floating point type")

    i = np.iinfo(middle_data.dtype)
    abs_max = 2 ** (i.bits - 1)
    offset = i.min + abs_max
    array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
    return array

inference_pipeline_asr_online = pipeline(
    task=Tasks.auto_speech_recognition,
    # model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
    model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
    model_revision=None)


print("model loaded")



async def ws_serve(websocket, path):
    frames_online = []
    global websocket_users
    websocket.send_msg = Queue()
    websocket_users.add(websocket)
    websocket.param_dict_asr_online = {"cache": dict()}
    websocket.speek_online = Queue()
    ss_online = threading.Thread(target=asr_online, args=(websocket,))
    ss_online.start()
    try:
        async for message in websocket:
            message = json.loads(message)
            audio = bytes(message['audio'], 'ISO-8859-1')
            chunk = message["chunk"]
            chunk_num = 500//chunk
            is_speaking = message["is_speaking"]
            websocket.param_dict_asr_online["is_final"] = not is_speaking
            frames_online.append(audio)

            if len(frames_online) % chunk_num == 0 or not is_speaking:
                audio_in = b"".join(frames_online)
                websocket.speek_online.put(audio_in)
                frames_online = []

            if not websocket.send_msg.empty():
                await websocket.send(websocket.send_msg.get())
                websocket.send_msg.task_done()

     
    except websockets.ConnectionClosed:
        print("ConnectionClosed...", websocket_users)    # 链接断开
        websocket_users.remove(websocket)
    except websockets.InvalidState:
        print("InvalidState...")    # 无效状态
    except Exception as e:
        print("Exception:", e)
 


def asr_online(websocket):  # ASR推理
    global inference_pipeline_asr_online
    global websocket_users
    while websocket in websocket_users:
        if not websocket.speek_online.empty():
            audio_in = websocket.speek_online.get()
            websocket.speek_online.task_done()
            if len(audio_in) > 0:
                # print(len(audio_in))
                audio_in = load_bytes(audio_in)
                # print(audio_in.shape)
                print(websocket.param_dict_asr_online["is_final"])
                rec_result = inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online)
                if websocket.param_dict_asr_online["is_final"]:
                    websocket.param_dict_asr_online["cache"] = dict()

                print(rec_result)
                if "text" in rec_result:
                    if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice":
                        message = json.dumps({"mode": "online", "text": rec_result["text"]})
                        websocket.send_msg.put(message)  # 存入发送队列  直接调用send发送不了
        
        time.sleep(0.005)

 
start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()