mirror of
https://github.com/modelscope/FunASR
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Merge branch dev_gzf_llm2 into dev_gzf_deepspeed
Title: wss 本次代码评审主要加入了时间戳打印，用于追踪WebSocket服务器中语音处理的关键步骤时间点，并新增了一个多轮对话处理的WebSocket服务器实现，包括语音合成和模型推理过程，提高了代码的可追溯性和诊断能力。 Link: https://code.alibaba-inc.com/zhifu.gzf/FunASR/codereview/18228062
This commit is contained in:
zhifu.gzf
commit
0941f8edad
@ -10,6 +10,7 @@ import ssl
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import nls
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from collections import deque
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import threading
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from datetime import datetime
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class NlsTtsSynthesizer:
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@ -48,6 +49,7 @@ class NlsTtsSynthesizer:
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)
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def on_data(self, data, *args):
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print(f"on_data: {datetime.now()}, len: {len(data)}")
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self.count += len(data)
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self.tts_fifo.append(data)
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# with open('tts_server.pcm', 'ab') as file:
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@ -262,6 +264,7 @@ async def model_inference(
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history=None,
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text_usr="",
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):
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print(f"model_inference: {datetime.now()}")
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fifo_queue = deque()
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synthesizer = NlsTtsSynthesizer(
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websocket=websocket, tts_fifo=fifo_queue, token=appkey_token, appkey=appkey
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@ -302,11 +305,12 @@ async def model_inference(
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)
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contents_i = [{"role": "system", "content": system_prompt}] + contents_i[3:]
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# print(f"contents_i: {contents_i}")
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print(f"contents_i: {len(contents_i)}")
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inputs_embeds, contents, batch, source_ids, meta_data = model.inference_prepare(
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[contents_i], None, "test_demo", tokenizer, frontend, device=device
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)
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print(f"speech_encoder: {datetime.now()}")
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model_inputs = {}
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model_inputs["inputs_embeds"] = inputs_embeds
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@ -324,7 +328,7 @@ async def model_inference(
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for new_text in streamer:
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end_llm = time.time()
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print(
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f"generated new text： {new_text}, time_fr_receive: {end_llm - beg0:.2f}, time_llm_decode: {end_llm - beg_llm:.2f}"
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f"{datetime.now()}, generated new text： {new_text}, time_fr_receive: {end_llm - beg0:.2f}, time_llm_decode: {end_llm - beg_llm:.2f}"
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)
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if len(new_text) > 0:
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new_text = new_text.replace("<|im_end|>", "")
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@ -403,9 +407,11 @@ async def ws_serve(websocket, path):
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websocket.mode = "2pass"
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websocket.llm_state = None
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print("new user connected", flush=True)
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print(f"connected time: {datetime.now()}")
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try:
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async for message in websocket:
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print(f"reci time: {datetime.now()}")
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if isinstance(message, str):
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messagejson = json.loads(message)
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543
runtime/python/websocket/funasr_wss_server_llm_multiturn.py
Normal file
543
runtime/python/websocket/funasr_wss_server_llm_multiturn.py
Normal file
@ -0,0 +1,543 @@
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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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import argparse
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import ssl
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import nls
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from collections import deque
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import threading
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from datetime import datetime
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class NlsTtsSynthesizer:
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def __init__(
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self,
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websocket,
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tts_fifo,
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token,
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appkey,
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url="wss://nls-gateway-cn-beijing.aliyuncs.com/ws/v1",
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):
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self.websocket = websocket
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self.tts_fifo = tts_fifo
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self.url = url
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self.token = token
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self.appkey = appkey
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self.sdk = None
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self.started = False
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self.count = 0
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self.init_sdk()
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def init_sdk(self):
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# 配置回调函数
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self.sdk = nls.NlsStreamInputTtsSynthesizer(
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url=self.url,
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token=self.token,
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appkey=self.appkey,
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on_data=self.on_data,
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on_sentence_begin=self.on_sentence_begin,
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on_sentence_synthesis=self.on_sentence_synthesis,
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on_sentence_end=self.on_sentence_end,
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on_completed=self.on_completed,
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on_error=self.on_error,
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on_close=self.on_close,
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callback_args=[],
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)
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def on_data(self, data, *args):
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print(f"on_data: {datetime.now()}, len: {len(data)}")
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self.count += len(data)
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self.tts_fifo.append(data)
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# with open('tts_server.pcm', 'ab') as file:
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# file.write(data)
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def on_sentence_begin(self, message, *args):
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print("on sentence begin =>{}".format(message))
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def on_sentence_synthesis(self, message, *args):
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print("on sentence synthesis =>{}".format(message))
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def on_sentence_end(self, message, *args):
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print("on sentence end =>{}".format(message))
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def on_completed(self, message, *args):
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print("on message data cout: =>{}".format(self.count))
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print("on completed =>{}".format(message))
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self.started = False
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def on_error(self, message, *args):
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print("on_error args=>{}".format(args))
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def on_close(self, *args):
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print("on_close: args=>{}".format(args))
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def start(self):
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self.sdk.startStreamInputTts()
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self.started = True
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def send_text(self, text):
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self.sdk.sendStreamInputTts(text)
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def stop(self):
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self.sdk.stopStreamInputTts()
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--host", type=str, default="0.0.0.0", required=False, help="host ip, localhost, 0.0.0.0"
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)
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parser.add_argument("--port", type=int, default=10096, required=False, help="grpc server port")
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parser.add_argument(
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"--asr_model",
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type=str,
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default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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help="model from modelscope",
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)
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parser.add_argument("--asr_model_revision", type=str, default="master", help="")
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parser.add_argument(
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"--asr_model_online",
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type=str,
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default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
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help="model from modelscope",
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)
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parser.add_argument("--asr_model_online_revision", type=str, default="master", help="")
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parser.add_argument(
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"--vad_model",
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type=str,
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default="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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help="model from modelscope",
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)
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parser.add_argument("--vad_model_revision", type=str, default="master", help="")
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parser.add_argument("--ngpu", type=int, default=1, help="0 for cpu, 1 for gpu")
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parser.add_argument("--device", type=str, default="cuda", help="cuda, cpu")
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parser.add_argument("--ncpu", type=int, default=4, help="cpu cores")
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parser.add_argument(
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"--certfile",
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type=str,
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default="../../ssl_key/server.crt",
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required=False,
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help="certfile for ssl",
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)
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parser.add_argument(
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"--keyfile",
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type=str,
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default="../../ssl_key/server.key",
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required=False,
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help="keyfile for ssl",
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)
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args = parser.parse_args()
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websocket_users = {}
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print("model loading")
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from funasr import AutoModel
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# vad
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model_vad = AutoModel(
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model=args.vad_model,
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model_revision=args.vad_model_revision,
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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device=args.device,
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disable_pbar=True,
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disable_log=True,
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# chunk_size=60,
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)
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import os
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# from install_model_requirements import install_requirements
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#
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# install_requirements()
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# import librosa
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# import base64
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# import io
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# import gradio as gr
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# import re
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import numpy as np
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import torch
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import torchaudio
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from transformers import TextIteratorStreamer
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from threading import Thread
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import torch
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import time
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import traceback
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# torch.backends.cuda.enable_mem_efficient_sdp(False)
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# torch.backends.cuda.enable_flash_sdp(False)
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from funasr import AutoModel
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import re
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import sys
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from modelscope.hub.api import HubApi
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api = HubApi()
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if "key" in os.environ:
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key = os.environ["key"]
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api.login(key)
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appkey = "xxx"
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appkey_token = "xxx"
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if "appkey" in os.environ:
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appkey = os.environ["appkey"]
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appkey_token = os.environ["appkey_token"]
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from modelscope.hub.snapshot_download import snapshot_download
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os.environ["MODELSCOPE_CACHE"] = "/mnt/workspace"
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llm_dir = snapshot_download("qwen/Qwen2-7B-Instruct", cache_dir=None, revision="master")
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audio_encoder_dir = snapshot_download("iic/SenseVoice", cache_dir=None, revision="master")
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# llm_dir = "/cpfs_speech/zhifu.gzf/init_model/qwen/Qwen2-7B-Instruct"
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# audio_encoder_dir = "/nfs/zhifu.gzf/init_model/SenseVoiceLargeModelscope"
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device = "cuda:0"
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all_file_paths = [
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# "/nfs/zhifu.gzf/init_model/Speech2Text_Align_V0712_modelscope"
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"FunAudioLLM/Speech2Text_Align_V0712",
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# "FunAudioLLM/Speech2Text_Align_V0718",
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# "FunAudioLLM/Speech2Text_Align_V0628",
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]
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llm_kwargs = {"num_beams": 1, "do_sample": False}
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ckpt_dir = all_file_paths[0]
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model_llm = AutoModel(
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model=ckpt_dir,
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device=device,
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fp16=False,
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bf16=False,
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llm_dtype="bf16",
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max_length=1024,
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llm_kwargs=llm_kwargs,
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llm_conf={"init_param_path": llm_dir},
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tokenizer_conf={"init_param_path": llm_dir},
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audio_encoder=audio_encoder_dir,
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)
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model = model_llm.model
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frontend = model_llm.kwargs["frontend"]
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tokenizer = model_llm.kwargs["tokenizer"]
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model_dict = {"model": model, "frontend": frontend, "tokenizer": tokenizer}
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async def send_to_client(websocket, syntheszier, tts_fifo):
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# Sending tts data to the client
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while True:
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if websocket.open and (syntheszier.started or len(tts_fifo) > 0):
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try:
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if len(tts_fifo) > 0:
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await websocket.send(tts_fifo.popleft())
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else:
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await asyncio.sleep(0.01)
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except Exception as e:
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print(f"Error sending data to client: {e}")
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else:
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print("WebSocket connection is not open or syntheszier is not started.")
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break
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def tts_sync_thread(coro):
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asyncio.run(coro)
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async def model_inference(
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websocket,
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audio_in,
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his_state=None,
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system_prompt="",
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state=None,
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turn_num=5,
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history=None,
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text_usr="",
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):
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print(f"model_inference: {datetime.now()}")
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fifo_queue = deque()
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synthesizer = NlsTtsSynthesizer(
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websocket=websocket, tts_fifo=fifo_queue, token=appkey_token, appkey=appkey
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)
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synthesizer.start()
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beg0 = time.time()
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if his_state is None:
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his_state = model_dict
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model = his_state["model"]
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frontend = his_state["frontend"]
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tokenizer = his_state["tokenizer"]
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# print(f"text_inputs: {text_inputs}")
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# print(f"audio_in: {audio_in}")
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# print(f"websocket.llm_state: {websocket.llm_state}")
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if websocket_users[websocket]["llm_state"].get("contents_i", None) is None:
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websocket_users[websocket]["llm_state"]["contents_i"] = []
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# print(f"history: {history}")
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# if history is None:
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# history = []
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# audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/tmp/1.wav"
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# user_prompt = f"<|startofspeech|>!{audio_in}<|endofspeech|>"
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user_prompt = websocket_users[websocket].get("user_prompt", "")
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user_prompt = f"{user_prompt}<|startofspeech|>!!<|endofspeech|>"
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contents_i = websocket_users[websocket]["llm_state"]["contents_i"]
|
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# print(f"contents_i_0: {contents_i}")
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system_prompt = websocket_users[websocket].get(
|
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"system_prompt",
|
||||
"你是小夏，一位典型的温婉江南姑娘。你出生于杭州，声音清甜并有亲近感，会用简洁语言表达你的想法。你是用户的好朋友。你的回答将通过逼真的文字转语音技术读出。",
|
||||
)
|
||||
|
||||
if len(contents_i) < 1:
|
||||
contents_i.append({"role": "system", "content": system_prompt})
|
||||
contents_i.append({"role": "user", "content": user_prompt, "audio": audio_in})
|
||||
contents_i.append({"role": "assistant", "content": "target_out"})
|
||||
|
||||
turn_num = websocket_users[websocket].get("turn_num", 5)
|
||||
if len(contents_i) > 2 * turn_num + 1:
|
||||
print(
|
||||
f"clip dialog pairs from: {len(contents_i)} to: {turn_num}, \ncontents_i_before_clip: {contents_i}"
|
||||
)
|
||||
contents_i = [{"role": "system", "content": system_prompt}] + contents_i[3:]
|
||||
|
||||
# print(f"contents_i: {len(contents_i)}")
|
||||
|
||||
inputs_embeds, contents, batch, source_ids, meta_data = model.inference_prepare(
|
||||
[contents_i], None, "test_demo", tokenizer, frontend, device=device
|
||||
)
|
||||
print(f"speech_encoder: {datetime.now()}")
|
||||
model_inputs = {}
|
||||
model_inputs["inputs_embeds"] = inputs_embeds
|
||||
|
||||
streamer = TextIteratorStreamer(tokenizer)
|
||||
|
||||
generation_kwargs = dict(model_inputs, streamer=streamer, max_new_tokens=1024)
|
||||
thread = Thread(target=model.llm.generate, kwargs=generation_kwargs)
|
||||
thread.start()
|
||||
res = ""
|
||||
beg_llm = time.time()
|
||||
tts_thread = Thread(
|
||||
target=tts_sync_thread, args=(send_to_client(websocket, synthesizer, fifo_queue),)
|
||||
)
|
||||
tts_thread.start()
|
||||
for new_text in streamer:
|
||||
end_llm = time.time()
|
||||
print(
|
||||
f"{datetime.now()}, generated new text： {new_text}, time_fr_receive: {end_llm - beg0:.2f}, time_llm_decode: {end_llm - beg_llm:.2f}"
|
||||
)
|
||||
if len(new_text) > 0:
|
||||
new_text = new_text.replace("<|im_end|>", "")
|
||||
res += new_text
|
||||
synthesizer.send_text(new_text)
|
||||
|
||||
contents_i[-1]["content"] = res
|
||||
websocket_users[websocket]["llm_state"]["contents_i"] = contents_i
|
||||
# history[-1][1] = res
|
||||
|
||||
mode = "2pass-online"
|
||||
message = json.dumps(
|
||||
{
|
||||
"mode": mode,
|
||||
"text": new_text,
|
||||
"wav_name": websocket_users[websocket].get("wav_name", "microphone"),
|
||||
"is_final": False,
|
||||
}
|
||||
)
|
||||
# print(f"online: {message}")
|
||||
await websocket.send(message)
|
||||
|
||||
synthesizer.stop()
|
||||
# await tts_to_client_task
|
||||
tts_thread.join()
|
||||
mode = "2pass-offline"
|
||||
message = json.dumps(
|
||||
{
|
||||
"mode": mode,
|
||||
"text": res,
|
||||
"wav_name": websocket_users[websocket].get("wav_name", "microphone"),
|
||||
"is_final": True,
|
||||
}
|
||||
)
|
||||
# print(f"offline: {message}")
|
||||
await websocket.send(message)
|
||||
|
||||
|
||||
print("model loaded! only support one client at the same time now!!!!")
|
||||
|
||||
|
||||
async def ws_reset(websocket):
|
||||
print("ws reset now, total num is ", len(websocket_users))
|
||||
|
||||
if websocket in websocket_users:
|
||||
del websocket_users[websocket]
|
||||
|
||||
await websocket.close()
|
||||
|
||||
|
||||
async def clear_websocket():
|
||||
for websocket in websocket_users:
|
||||
await ws_reset(websocket)
|
||||
websocket_users.clear()
|
||||
|
||||
|
||||
async def ws_serve(websocket, path):
|
||||
frames = []
|
||||
frames_asr = []
|
||||
frames_llm = []
|
||||
global websocket_users
|
||||
# await clear_websocket()
|
||||
websocket_users[websocket] = {}
|
||||
websocket_users[websocket]["status_dict_asr"] = {}
|
||||
websocket_users[websocket]["status_dict_vad"] = {"cache": {}, "is_final": False}
|
||||
|
||||
websocket_users[websocket]["chunk_interval"] = 10
|
||||
websocket_users[websocket]["vad_pre_idx"] = 0
|
||||
speech_start = False
|
||||
speech_end_i = -1
|
||||
websocket_users[websocket]["wav_name"] = "microphone"
|
||||
websocket_users[websocket]["mode"] = "2pass"
|
||||
websocket_users[websocket]["llm_state"] = {}
|
||||
websocket.stop_send = False
|
||||
print(f"new user connected: {len(websocket_users)}", flush=True)
|
||||
print(f"connected time: {datetime.now()}")
|
||||
|
||||
try:
|
||||
async for message in websocket:
|
||||
print(f"receive time: {datetime.now()}")
|
||||
if isinstance(message, str):
|
||||
messagejson = json.loads(message)
|
||||
|
||||
if "is_speaking" in messagejson:
|
||||
websocket_users[websocket]["is_speaking"] = messagejson["is_speaking"]
|
||||
websocket_users[websocket]["speech_start"] = messagejson["is_speaking"]
|
||||
if "chunk_interval" in messagejson:
|
||||
websocket_users[websocket]["chunk_interval"] = messagejson["chunk_interval"]
|
||||
if "wav_name" in messagejson:
|
||||
websocket_users[websocket]["wav_name"] = messagejson.get("wav_name")
|
||||
if "chunk_size" in messagejson:
|
||||
chunk_size = messagejson["chunk_size"]
|
||||
if isinstance(chunk_size, str):
|
||||
chunk_size = chunk_size.split(",")
|
||||
chunk_size = [int(x) for x in chunk_size]
|
||||
websocket_users[websocket]["status_dict_vad"]["chunk_size"] = (
|
||||
chunk_size[1] * 60 / websocket_users[websocket].get("chunk_interval", 10)
|
||||
)
|
||||
|
||||
if "mode" in messagejson:
|
||||
websocket_users[websocket]["mode"] = messagejson["mode"]
|
||||
if "is_close" in messagejson:
|
||||
websocket_users[websocket]["is_close"] = messagejson["is_close"]
|
||||
if "system_prompt" in messagejson:
|
||||
websocket_users[websocket]["system_prompt"] = messagejson["system_prompt"]
|
||||
if "user_prompt" in messagejson:
|
||||
websocket_users[websocket]["user_prompt"] = messagejson["user_prompt"]
|
||||
if len(frames_asr) > 0 or not isinstance(message, str) or len(frames_llm) > 0:
|
||||
if not isinstance(message, str):
|
||||
frames.append(message)
|
||||
|
||||
if websocket_users[websocket].get("speech_start", True):
|
||||
frames_llm.append(message)
|
||||
# duration_ms = len(message) // 32
|
||||
# websocket.vad_pre_idx += duration_ms
|
||||
|
||||
# if speech_start:
|
||||
# frames_asr.append(message)
|
||||
# # vad online
|
||||
# try:
|
||||
# speech_start_i, speech_end_i = await async_vad(websocket, message)
|
||||
# except:
|
||||
# print("error in vad")
|
||||
# if speech_start_i != -1:
|
||||
# speech_start = True
|
||||
# beg_bias = (websocket.vad_pre_idx - speech_start_i) // duration_ms
|
||||
# frames_pre = frames[-beg_bias:]
|
||||
# frames_asr = []
|
||||
# frames_asr.extend(frames_pre)
|
||||
|
||||
# if speech_end_i != -1 or not websocket.is_speaking:
|
||||
if not websocket_users[websocket].get("is_speaking", True):
|
||||
# print("vad end point")
|
||||
if (
|
||||
websocket_users[websocket].get("mode", "2pass") == "2pass"
|
||||
or websocket_users[websocket].get("mode", "2pass") == "2pass" == "offline"
|
||||
):
|
||||
# audio_in = b"".join(frames_asr)
|
||||
audio_in = b"".join(frames_llm)
|
||||
try:
|
||||
# await async_asr(websocket, audio_in)
|
||||
await model_inference(websocket, audio_in)
|
||||
except Exception as e:
|
||||
print(f"{str(e)}, {traceback.format_exc()}")
|
||||
# frames_asr = []
|
||||
# speech_start = False
|
||||
frames_llm = []
|
||||
|
||||
# if not websocket.is_speaking:
|
||||
# websocket.vad_pre_idx = 0
|
||||
# frames = []
|
||||
# websocket.status_dict_vad["cache"] = {}
|
||||
# else:
|
||||
# frames = frames[-20:]
|
||||
frames = frames[-20:]
|
||||
else:
|
||||
print(f"message: {message}")
|
||||
if websocket_users[websocket].get("is_close", False):
|
||||
print(f'is_close: {websocket_users[websocket].get("is_close", False)}')
|
||||
websocket.stop_send = True
|
||||
del websocket_users[websocket]
|
||||
# await ws_reset(websocket)
|
||||
|
||||
except websockets.ConnectionClosed:
|
||||
print("ConnectionClosed...", websocket_users, flush=True)
|
||||
await ws_reset(websocket)
|
||||
|
||||
except websockets.InvalidState:
|
||||
print("InvalidState...")
|
||||
except Exception as e:
|
||||
print("Exception:", e)
|
||||
|
||||
|
||||
# async def async_vad(websocket, audio_in):
|
||||
# segments_result = model_vad.generate(input=audio_in, **websocket.status_dict_vad)[0]["value"]
|
||||
# # print(segments_result)
|
||||
#
|
||||
# speech_start = -1
|
||||
# speech_end = -1
|
||||
#
|
||||
# if len(segments_result) == 0 or len(segments_result) > 1:
|
||||
# return speech_start, speech_end
|
||||
# if segments_result[0][0] != -1:
|
||||
# speech_start = segments_result[0][0]
|
||||
# if segments_result[0][1] != -1:
|
||||
# speech_end = segments_result[0][1]
|
||||
# return speech_start, speech_end
|
||||
|
||||
|
||||
if False: # 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()
|
||||
Loading…
Reference in New Issue
Block a user