mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
261 lines
9.4 KiB
Python
261 lines
9.4 KiB
Python
import asyncio
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import json
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import websockets
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import time
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from queue import Queue
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import threading
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import argparse
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.logger import get_logger
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import logging
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import tracemalloc
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import numpy as np
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tracemalloc.start()
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logger = get_logger(log_level=logging.CRITICAL)
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logger.setLevel(logging.CRITICAL)
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websocket_users = set() #维护客户端列表
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parser = argparse.ArgumentParser()
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parser.add_argument("--host",
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type=str,
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default="0.0.0.0",
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required=False,
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help="host ip, localhost, 0.0.0.0")
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parser.add_argument("--port",
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type=int,
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default=10095,
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required=False,
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help="grpc server port")
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parser.add_argument("--asr_model",
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type=str,
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default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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help="model from modelscope")
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parser.add_argument("--vad_model",
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type=str,
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default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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help="model from modelscope")
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parser.add_argument("--punc_model",
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type=str,
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default="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
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help="model from modelscope")
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parser.add_argument("--ngpu",
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type=int,
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default=1,
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help="0 for cpu, 1 for gpu")
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args = parser.parse_args()
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print("model loading")
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def load_bytes(input):
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middle_data = np.frombuffer(input, dtype=np.int16)
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middle_data = np.asarray(middle_data)
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if middle_data.dtype.kind not in 'iu':
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raise TypeError("'middle_data' must be an array of integers")
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dtype = np.dtype('float32')
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if dtype.kind != 'f':
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raise TypeError("'dtype' must be a floating point type")
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i = np.iinfo(middle_data.dtype)
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abs_max = 2 ** (i.bits - 1)
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offset = i.min + abs_max
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array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
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return array
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# vad
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inference_pipeline_vad = pipeline(
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task=Tasks.voice_activity_detection,
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model=args.vad_model,
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model_revision=None,
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output_dir=None,
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batch_size=1,
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mode='online',
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ngpu=args.ngpu,
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)
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# param_dict_vad = {'in_cache': dict(), "is_final": False}
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# # asr
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# param_dict_asr = {}
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# # param_dict["hotword"] = "小五 小五月" # 设置热词，用空格隔开
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# inference_pipeline_asr = pipeline(
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# task=Tasks.auto_speech_recognition,
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# model=args.asr_model,
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# param_dict=param_dict_asr,
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# ngpu=args.ngpu,
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# )
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# if args.punc_model != "":
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# # param_dict_punc = {'cache': list()}
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# inference_pipeline_punc = pipeline(
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# task=Tasks.punctuation,
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# model=args.punc_model,
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# model_revision=None,
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# ngpu=args.ngpu,
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# )
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# else:
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# inference_pipeline_punc = None
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inference_pipeline_asr_online = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
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model_revision=None)
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print("model loaded")
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async def ws_serve(websocket, path):
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#speek = Queue()
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frames = [] # 存储所有的帧数据
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frames_online = [] # 存储所有的帧数据
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buffer = [] # 存储缓存中的帧数据（最多两个片段）
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RECORD_NUM = 0
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global websocket_users
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speech_start, speech_end = False, False
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# 调用asr函数
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websocket.param_dict_vad = {'in_cache': dict(), "is_final": False}
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websocket.param_dict_punc = {'cache': list()}
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websocket.speek = Queue() #websocket 添加进队列对象 让asr读取语音数据包
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websocket.send_msg = Queue() #websocket 添加个队列对象 让ws发送消息到客户端
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websocket_users.add(websocket)
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# ss = threading.Thread(target=asr, args=(websocket,))
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# ss.start()
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websocket.param_dict_asr_online = {"cache": dict(), "is_final": False}
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websocket.speek_online = Queue() # websocket 添加进队列对象 让asr读取语音数据包
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ss_online = threading.Thread(target=asr_online, args=(websocket,))
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ss_online.start()
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try:
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async for data in websocket:
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#voices.put(message)
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#print("put")
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#await websocket.send("123")
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data = json.loads(data)
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# message = data["data"]
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message = bytes(data['audio'], 'ISO-8859-1')
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chunk = data["chunk"]
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chunk_num = 600//chunk
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is_speaking = data["is_speaking"]
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websocket.param_dict_vad["is_final"] = not is_speaking
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buffer.append(message)
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if len(buffer) > 2:
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buffer.pop(0) # 如果缓存超过两个片段，则删除最早的一个
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if speech_start:
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# frames.append(message)
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frames_online.append(message)
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# RECORD_NUM += 1
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if len(frames_online) % chunk_num == 0:
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audio_in = b"".join(frames_online)
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websocket.speek_online.put(audio_in)
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frames_online = []
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speech_start_i, speech_end_i = vad(message, websocket)
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#print(speech_start_i, speech_end_i)
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if speech_start_i:
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# RECORD_NUM += 1
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speech_start = speech_start_i
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# frames = []
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# frames.extend(buffer) # 把之前2个语音数据快加入
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frames_online = []
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# frames_online.append(message)
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frames_online.extend(buffer)
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# RECORD_NUM += 1
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websocket.param_dict_asr_online["is_final"] = False
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if speech_end_i:
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speech_start = False
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# audio_in = b"".join(frames)
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# websocket.speek.put(audio_in)
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# frames = [] # 清空所有的帧数据
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frames_online = []
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websocket.param_dict_asr_online["is_final"] = True
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# buffer = [] # 清空缓存中的帧数据（最多两个片段）
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# RECORD_NUM = 0
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if not websocket.send_msg.empty():
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await websocket.send(websocket.send_msg.get())
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websocket.send_msg.task_done()
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except websockets.ConnectionClosed:
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print("ConnectionClosed...", websocket_users) # 链接断开
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websocket_users.remove(websocket)
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except websockets.InvalidState:
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print("InvalidState...") # 无效状态
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except Exception as e:
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print("Exception:", e)
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# def asr(websocket): # ASR推理
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# global inference_pipeline_asr
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# # global param_dict_punc
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# global websocket_users
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# while websocket in websocket_users:
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# if not websocket.speek.empty():
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# audio_in = websocket.speek.get()
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# websocket.speek.task_done()
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# if len(audio_in) > 0:
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# rec_result = inference_pipeline_asr(audio_in=audio_in)
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# if inference_pipeline_punc is not None and 'text' in rec_result:
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# rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict=websocket.param_dict_punc)
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# # print(rec_result)
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# if "text" in rec_result:
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# message = json.dumps({"mode": "offline", "text": rec_result["text"]})
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# websocket.send_msg.put(message) # 存入发送队列 直接调用send发送不了
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#
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# time.sleep(0.1)
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def asr_online(websocket): # ASR推理
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global inference_pipeline_asr_online
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# global param_dict_punc
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global websocket_users
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while websocket in websocket_users:
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if not websocket.speek_online.empty():
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audio_in = websocket.speek_online.get()
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websocket.speek_online.task_done()
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if len(audio_in) > 0:
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# print(len(audio_in))
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audio_in = load_bytes(audio_in)
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# print(audio_in.shape)
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rec_result = inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online)
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# print(rec_result)
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if "text" in rec_result:
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if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice":
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message = json.dumps({"mode": "online", "text": rec_result["text"]})
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websocket.send_msg.put(message) # 存入发送队列 直接调用send发送不了
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time.sleep(0.1)
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def vad(data, websocket): # VAD推理
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global inference_pipeline_vad, param_dict_vad
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#print(type(data))
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# print(param_dict_vad)
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segments_result = inference_pipeline_vad(audio_in=data, param_dict=websocket.param_dict_vad)
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# print(segments_result)
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# print(param_dict_vad)
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speech_start = False
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speech_end = False
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if len(segments_result) == 0 or len(segments_result["text"]) > 1:
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return speech_start, speech_end
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if segments_result["text"][0][0] != -1:
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speech_start = True
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if segments_result["text"][0][1] != -1:
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speech_end = True
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return speech_start, speech_end
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start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
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asyncio.get_event_loop().run_until_complete(start_server)
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asyncio.get_event_loop().run_forever() |