add llm stream func
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lipku
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5e8884fcf3
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@ -3,6 +3,8 @@ Real time interactive streaming digital human， realize audio video synchronous
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[ernerf效果](https://www.bilibili.com/video/BV1PM4m1y7Q2/) [musetalk效果](https://www.bilibili.com/video/BV1gm421N7vQ/) [wav2lip效果](https://www.bilibili.com/video/BV1Bw4m1e74P/)
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## 为避免与3d数字人混淆，原项目metahuman-stream改名为livetalking，原有链接地址继续可用
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## Features
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1. 支持多种数字人模型: ernerf、musetalk、wav2lip
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2. 支持声音克隆
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65
app.py
65
app.py
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@ -24,6 +24,7 @@ import argparse
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import shutil
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import asyncio
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import string
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app = Flask(__name__)
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@ -52,14 +53,58 @@ def echo_socket(ws):
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nerfreal.put_msg_txt(message)
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def llm_response(message):
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from llm.LLM import LLM
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# llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='Your API Key', proxy_url=None)
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# llm = LLM().init_model('ChatGPT', model_path= 'gpt-3.5-turbo',api_key='Your API Key')
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llm = LLM().init_model('VllmGPT', model_path= 'THUDM/chatglm3-6b')
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response = llm.chat(message)
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print(response)
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return response
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# def llm_response(message):
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# from llm.LLM import LLM
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# # llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='Your API Key', proxy_url=None)
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# # llm = LLM().init_model('ChatGPT', model_path= 'gpt-3.5-turbo',api_key='Your API Key')
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# llm = LLM().init_model('VllmGPT', model_path= 'THUDM/chatglm3-6b')
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# response = llm.chat(message)
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# print(response)
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# return response
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def llm_response(message,nerfreal):
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start = time.perf_counter()
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from openai import OpenAI
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client = OpenAI(
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# 如果您没有配置环境变量，请在此处用您的API Key进行替换
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api_key=os.getenv("DASHSCOPE_API_KEY"),
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# 填写DashScope SDK的base_url
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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)
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end = time.perf_counter()
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print(f"llm Time init: {end-start}s")
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completion = client.chat.completions.create(
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model="qwen-plus",
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messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
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{'role': 'user', 'content': message}],
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stream=True,
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# 通过以下设置，在流式输出的最后一行展示token使用信息
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stream_options={"include_usage": True}
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)
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result=""
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first = True
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for chunk in completion:
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if len(chunk.choices)>0:
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#print(chunk.choices[0].delta.content)
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if first:
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end = time.perf_counter()
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print(f"llm Time to first chunk: {end-start}s")
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first = False
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msg = chunk.choices[0].delta.content
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lastpos=0
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#msglist = re.split('[,.!;:，。！?]',msg)
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for i, char in enumerate(msg):
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if char in ",.!;:，。！？：；" :
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result = result+msg[lastpos:i+1]
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lastpos = i+1
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if len(result)>10:
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print(result)
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nerfreal.put_msg_txt(result)
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result=""
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result = result+msg[lastpos:]
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end = time.perf_counter()
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print(f"llm Time to last chunk: {end-start}s")
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nerfreal.put_msg_txt(result)
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@sockets.route('/humanchat')
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def chat_socket(ws):
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@ -147,8 +192,8 @@ async def human(request):
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if params['type']=='echo':
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nerfreals[sessionid].put_msg_txt(params['text'])
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elif params['type']=='chat':
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res=await asyncio.get_event_loop().run_in_executor(None, llm_response(params['text']))
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nerfreals[sessionid].put_msg_txt(res)
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res=await asyncio.get_event_loop().run_in_executor(None, llm_response, params['text'],nerfreals[sessionid])
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#nerfreals[sessionid].put_msg_txt(res)
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return web.Response(
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content_type="application/json",
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@ -40,3 +40,4 @@ diffusers
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accelerate
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librosa
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openai
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29
ttsreal.py
29
ttsreal.py
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@ -37,6 +37,7 @@ class BaseTTS:
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self.state = State.PAUSE
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def put_msg_txt(self,msg):
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if len(msg)>0:
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self.msgqueue.put(msg)
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def render(self,quit_event):
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@ -99,6 +100,7 @@ class EdgeTTS(BaseTTS):
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return stream
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async def __main(self,voicename: str, text: str):
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try:
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communicate = edge_tts.Communicate(text, voicename)
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#with open(OUTPUT_FILE, "wb") as file:
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@ -112,6 +114,8 @@ class EdgeTTS(BaseTTS):
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#file.write(chunk["data"])
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elif chunk["type"] == "WordBoundary":
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pass
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except Exception as e:
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print(e)
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###########################################################################################
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class VoitsTTS(BaseTTS):
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@ -143,6 +147,7 @@ class VoitsTTS(BaseTTS):
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# req["emotion"] = emotion
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# #req["stream_chunk_size"] = stream_chunk_size # you can reduce it to get faster response, but degrade quality
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# req["streaming_mode"] = True
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try:
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res = requests.post(
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f"{server_url}/tts",
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json=req,
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@ -156,15 +161,17 @@ class VoitsTTS(BaseTTS):
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return
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first = True
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for chunk in res.iter_content(chunk_size=16000): # 1280 32K*20ms*2
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for chunk in res.iter_content(chunk_size=12800): # 1280 32K*20ms*2
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if first:
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end = time.perf_counter()
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print(f"gpt_sovits Time to first chunk: {end-start}s")
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first = False
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if chunk and self.state==State.RUNNING:
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yield chunk
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print("gpt_sovits response.elapsed:", res.elapsed)
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#print("gpt_sovits response.elapsed:", res.elapsed)
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except Exception as e:
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print(e)
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def stream_tts(self,audio_stream):
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for chunk in audio_stream:
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@ -199,6 +206,7 @@ class CosyVoiceTTS(BaseTTS):
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'tts_text': text,
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'prompt_text': reftext
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}
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try:
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files = [('prompt_wav', ('prompt_wav', open(reffile, 'rb'), 'application/octet-stream'))]
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res = requests.request("GET", f"{server_url}/inference_zero_shot", data=payload, files=files, stream=True)
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@ -210,15 +218,16 @@ class CosyVoiceTTS(BaseTTS):
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return
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first = True
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for chunk in res.iter_content(chunk_size=16000): # 1280 32K*20ms*2
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for chunk in res.iter_content(chunk_size=8820): # 882 22.05K*20ms*2
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if first:
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end = time.perf_counter()
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print(f"cosy_voice Time to first chunk: {end-start}s")
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first = False
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if chunk and self.state==State.RUNNING:
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yield chunk
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print("cosy_voice response.elapsed:", res.elapsed)
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except Exception as e:
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print(e)
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def stream_tts(self,audio_stream):
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for chunk in audio_stream:
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@ -261,6 +270,7 @@ class XTTS(BaseTTS):
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speaker["text"] = text
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speaker["language"] = language
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speaker["stream_chunk_size"] = stream_chunk_size # you can reduce it to get faster response, but degrade quality
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try:
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res = requests.post(
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f"{server_url}/tts_stream",
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json=speaker,
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@ -274,15 +284,16 @@ class XTTS(BaseTTS):
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return
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first = True
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for chunk in res.iter_content(chunk_size=960): #24K*20ms*2
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for chunk in res.iter_content(chunk_size=9600): #24K*20ms*2
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if first:
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end = time.perf_counter()
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print(f"xtts Time to first chunk: {end-start}s")
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first = False
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if chunk:
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yield chunk
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print("xtts response.elapsed:", res.elapsed)
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except Exception as e:
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print(e)
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def stream_tts(self,audio_stream):
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for chunk in audio_stream:
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