mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
147 lines
4.8 KiB
Python
147 lines
4.8 KiB
Python
import asyncio
|
|
import json
|
|
import websockets
|
|
import time
|
|
from queue import Queue
|
|
import threading
|
|
import logging
|
|
import tracemalloc
|
|
import numpy as np
|
|
import ssl
|
|
from parse_args import args
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.logger import get_logger
|
|
from funasr.runtime.python.onnxruntime.funasr_onnx.utils.frontend import load_bytes
|
|
|
|
tracemalloc.start()
|
|
|
|
logger = get_logger(log_level=logging.CRITICAL)
|
|
logger.setLevel(logging.CRITICAL)
|
|
|
|
|
|
websocket_users = set()
|
|
|
|
|
|
print("model loading")
|
|
|
|
inference_pipeline_asr_online = pipeline(
|
|
task=Tasks.auto_speech_recognition,
|
|
model=args.asr_model_online,
|
|
ngpu=args.ngpu,
|
|
ncpu=args.ncpu,
|
|
model_revision='v1.0.4')
|
|
|
|
# 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,
|
|
ncpu=1,
|
|
)
|
|
|
|
print("model loaded")
|
|
|
|
|
|
|
|
async def ws_serve(websocket, path):
|
|
frames = []
|
|
frames_asr_online = []
|
|
global websocket_users
|
|
websocket_users.add(websocket)
|
|
websocket.param_dict_asr_online = {"cache": dict()}
|
|
websocket.param_dict_vad = {'in_cache': dict()}
|
|
websocket.wav_name = "microphone"
|
|
print("new user connected",flush=True)
|
|
try:
|
|
async for message in websocket:
|
|
|
|
|
|
if isinstance(message, str):
|
|
messagejson = json.loads(message)
|
|
|
|
if "is_speaking" in messagejson:
|
|
websocket.is_speaking = messagejson["is_speaking"]
|
|
websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking
|
|
websocket.param_dict_vad["is_final"] = not websocket.is_speaking
|
|
# need to fire engine manually if no data received any more
|
|
if not websocket.is_speaking:
|
|
await async_asr_online(websocket, b"")
|
|
if "chunk_interval" in messagejson:
|
|
websocket.chunk_interval=messagejson["chunk_interval"]
|
|
if "wav_name" in messagejson:
|
|
websocket.wav_name = messagejson.get("wav_name")
|
|
if "chunk_size" in messagejson:
|
|
websocket.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"]
|
|
# if has bytes in buffer or message is bytes
|
|
if len(frames_asr_online) > 0 or not isinstance(message, str):
|
|
if not isinstance(message, str):
|
|
frames_asr_online.append(message)
|
|
# frames.append(message)
|
|
# duration_ms = len(message) // 32
|
|
# websocket.vad_pre_idx += duration_ms
|
|
speech_start_i, speech_end_i = await async_vad(websocket, message)
|
|
websocket.is_speaking = not speech_end_i
|
|
|
|
if len(frames_asr_online) % websocket.chunk_interval == 0 or not websocket.is_speaking:
|
|
websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking
|
|
audio_in = b"".join(frames_asr_online)
|
|
await async_asr_online(websocket, audio_in)
|
|
frames_asr_online = []
|
|
|
|
|
|
except websockets.ConnectionClosed:
|
|
print("ConnectionClosed...", websocket_users)
|
|
websocket_users.remove(websocket)
|
|
except websockets.InvalidState:
|
|
print("InvalidState...")
|
|
except Exception as e:
|
|
print("Exception:", e)
|
|
|
|
|
|
async def async_asr_online(websocket,audio_in):
|
|
if len(audio_in) >= 0:
|
|
audio_in = load_bytes(audio_in)
|
|
# print(websocket.param_dict_asr_online.get("is_final", False))
|
|
rec_result = inference_pipeline_asr_online(audio_in=audio_in,
|
|
param_dict=websocket.param_dict_asr_online)
|
|
# print(rec_result)
|
|
if websocket.param_dict_asr_online.get("is_final", False):
|
|
websocket.param_dict_asr_online["cache"] = dict()
|
|
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"], "wav_name": websocket.wav_name})
|
|
await websocket.send(message)
|
|
|
|
|
|
async def async_vad(websocket, audio_in):
|
|
segments_result = inference_pipeline_vad(audio_in=audio_in, param_dict=websocket.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 = segments_result["text"][0][0]
|
|
if segments_result["text"][0][1] != -1:
|
|
speech_end = True
|
|
return speech_start, speech_end
|
|
|
|
if len(args.certfile)>0:
|
|
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
|
|
|
|
# Generate with Lets Encrypt, copied to this location, chown to current user and 400 permissions
|
|
ssl_cert = args.certfile
|
|
ssl_key = args.keyfile
|
|
|
|
ssl_context.load_cert_chain(ssl_cert, keyfile=ssl_key)
|
|
start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None,ssl=ssl_context)
|
|
else:
|
|
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() |