import asyncio
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import json
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import websockets
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import time
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import logging
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import tracemalloc
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import numpy as np
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from parse_args import args
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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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from funasr.runtime.python.onnxruntime.funasr_onnx.utils.frontend import load_bytes
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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()
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print("model loading")
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# asr
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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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ngpu=args.ngpu,
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ncpu=args.ncpu,
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model_revision=None)
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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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ncpu=args.ncpu,
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)
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if args.punc_model != "":
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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="v1.0.2",
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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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=args.asr_model_online,
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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model_revision='v1.0.4')
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print("model loaded")
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async def ws_serve(websocket, path):
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frames = []
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frames_asr = []
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frames_asr_online = []
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global websocket_users
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websocket_users.add(websocket)
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websocket.param_dict_asr = {}
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websocket.param_dict_asr_online = {"cache": dict()}
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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.vad_pre_idx = 0
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speech_start = False
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speech_end_i = False
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websocket.wav_name = "microphone"
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print("new user connected", flush=True)
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try:
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async for message in websocket:
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if isinstance(message, str):
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messagejson = json.loads(message)
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if "is_speaking" in messagejson:
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websocket.is_speaking = messagejson["is_speaking"]
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websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking
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if "chunk_interval" in messagejson:
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websocket.chunk_interval = messagejson["chunk_interval"]
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if "wav_name" in messagejson:
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websocket.wav_name = messagejson.get("wav_name")
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if "chunk_size" in messagejson:
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websocket.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"]
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if len(frames_asr_online) > 0 or len(frames_asr) > 0 or not isinstance(message, str):
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if not isinstance(message, str):
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frames.append(message)
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duration_ms = len(message)//32
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websocket.vad_pre_idx += duration_ms
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# asr online
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frames_asr_online.append(message)
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websocket.param_dict_asr_online["is_final"] = speech_end_i
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if len(frames_asr_online) % websocket.chunk_interval == 0 or websocket.param_dict_asr_online["is_final"]:
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audio_in = b"".join(frames_asr_online)
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await async_asr_online(websocket, audio_in)
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frames_asr_online = []
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if speech_start:
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frames_asr.append(message)
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# vad online
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speech_start_i, speech_end_i = await async_vad(websocket, message)
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if speech_start_i:
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speech_start = True
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beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms
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frames_pre = frames[-beg_bias:]
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frames_asr = []
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frames_asr.extend(frames_pre)
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# asr punc offline
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if speech_end_i or not websocket.is_speaking:
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# print("vad end point")
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audio_in = b"".join(frames_asr)
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await async_asr(websocket, audio_in)
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frames_asr = []
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speech_start = False
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# frames_asr_online = []
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# websocket.param_dict_asr_online = {"cache": dict()}
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if not websocket.is_speaking:
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websocket.vad_pre_idx = 0
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frames = []
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websocket.param_dict_vad = {'in_cache': dict()}
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else:
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frames = frames[-20:]
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except websockets.ConnectionClosed:
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print("ConnectionClosed...", websocket_users)
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websocket_users.remove(websocket)
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except websockets.InvalidState:
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print("InvalidState...")
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except Exception as e:
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print("Exception:", e)
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async def async_vad(websocket, audio_in):
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segments_result = inference_pipeline_vad(audio_in=audio_in, param_dict=websocket.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 = segments_result["text"][0][0]
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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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async def async_asr(websocket, audio_in):
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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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rec_result = inference_pipeline_asr(audio_in=audio_in,
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param_dict=websocket.param_dict_asr)
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# print(rec_result)
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if inference_pipeline_punc is not None and 'text' in rec_result and len(rec_result["text"])>0:
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rec_result = inference_pipeline_punc(text_in=rec_result['text'],
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param_dict=websocket.param_dict_punc)
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# print("offline", rec_result)
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message = json.dumps({"mode": "2pass-offline", "text": rec_result["text"], "wav_name": websocket.wav_name})
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await websocket.send(message)
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async def async_asr_online(websocket, audio_in):
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if len(audio_in) > 0:
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audio_in = load_bytes(audio_in)
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# print(websocket.param_dict_asr_online.get("is_final", False))
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rec_result = inference_pipeline_asr_online(audio_in=audio_in,
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param_dict=websocket.param_dict_asr_online)
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# print(rec_result)
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if websocket.param_dict_asr_online.get("is_final", False):
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return
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# websocket.param_dict_asr_online["cache"] = dict()
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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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# print("online", rec_result)
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message = json.dumps({"mode": "2pass-online", "text": rec_result["text"], "wav_name": websocket.wav_name})
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await websocket.send(message)
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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()
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