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redundant Checks, audio_prompt to Tensor, and adding accelerator+autocastfor evaluation #804

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redundant Checks, audio_prompt to Tensor, and adding accelerator+autocastfor evaluation #804

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1 change: 1 addition & 0 deletions requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -33,3 +33,4 @@ gradio==4.41.0
gradio_client
http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/modelscope_studio-0.4.0.9-py3-none-any.whl
decord
setuptools==75.8.0
2 changes: 1 addition & 1 deletion requirements_o2.6.txt
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Expand Up @@ -15,7 +15,7 @@ moviepy

# for web demo
aiofiles==23.2.1
onnxruntime==1.20.1
#onnxruntime==1.20.1
fastapi
uvicorn
gradio==4.44.1
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45 changes: 24 additions & 21 deletions web_demos/minicpm-o_2.6/model_server.py
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Expand Up @@ -18,6 +18,8 @@
import uvicorn
from fastapi import FastAPI, Header, Query, Request, HTTPException, WebSocket, WebSocketDisconnect
from fastapi.responses import JSONResponse, StreamingResponse
from accelerate import Accelerator


cur_path = os.path.split(os.path.realpath(__file__))[0]
sys.path.append(os.path.abspath(cur_path))
Expand Down Expand Up @@ -92,12 +94,16 @@ def __init__(self):

self.minicpmo_model_path = args.model #"openbmb/MiniCPM-o-2_6"
self.model_version = "2.6"

accelerator = Accelerator()
with torch.no_grad():
self.minicpmo_model = AutoModel.from_pretrained(self.minicpmo_model_path, trust_remote_code=True, torch_dtype=self.target_dtype, attn_implementation='sdpa')
self.minicpmo_tokenizer = AutoTokenizer.from_pretrained(self.minicpmo_model_path, trust_remote_code=True)
self.minicpmo_model.init_tts()
# self.minicpmo_model.tts.float()
self.minicpmo_model.to(self.device).eval()
with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
self.minicpmo_model = AutoModel.from_pretrained(self.minicpmo_model_path, trust_remote_code=True, torch_dtype=self.target_dtype, attn_implementation='sdpa', device_map="auto")
self.minicpmo_model = accelerator.prepare(self.minicpmo_model)
self.minicpmo_tokenizer = AutoTokenizer.from_pretrained(self.minicpmo_model_path, trust_remote_code=True)
self.minicpmo_model.init_tts()
# self.minicpmo_model.tts.float()
self.minicpmo_model.eval()

self.ref_path_video_default = "assets/ref_audios/video_default.wav"
self.ref_path_default = "assets/ref_audios/default.wav"
Expand Down Expand Up @@ -204,6 +210,8 @@ def no_active_stream(self):
return False

def sys_prompt_init(self, msg_type):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

if self.past_session_id == self.session_id:
return
logger.info("### sys_prompt_init ###")
Expand All @@ -226,7 +234,8 @@ def sys_prompt_init(self, msg_type):
ref_path = self.ref_path_male

audio_prompt, sr = librosa.load(ref_path, sr=16000, mono=True)
sys_msg = {'role': 'user', 'content': [audio_voice_clone_prompt + "\n", audio_prompt, "\n" + audio_assistant_prompt]}
audio_tensor = torch.tensor(audio_prompt).float().to(device)
sys_msg = {'role': 'user', 'content': [audio_voice_clone_prompt + "\n", audio_tensor, "\n" + audio_assistant_prompt]}
elif msg_type == 2: #video
voice_clone_prompt="你是一个AI助手。你能接受视频，音频和文本输入并输出语音和文本。模仿输入音频中的声音特征。"
assistant_prompt="作为助手，你将使用这种声音风格说话。"
Expand All @@ -243,7 +252,8 @@ def sys_prompt_init(self, msg_type):
ref_path = self.ref_path_male

audio_prompt, sr = librosa.load(ref_path, sr=16000, mono=True)
sys_msg = {'role': 'user', 'content': [voice_clone_prompt, audio_prompt, assistant_prompt]}
audio_tensor = torch.tensor(audio_prompt).float().to(device)
sys_msg = {'role': 'user', 'content': [voice_clone_prompt, audio_tensor, assistant_prompt]}
# elif msg_type == 3: #user start
# assistant_prompt="作为助手，你将使用这种声音风格说话。"
# if self.customized_options is not None:
Expand All @@ -268,20 +278,13 @@ def sys_prompt_init(self, msg_type):
)

self.savedir = os.path.join(f"./log_data/{args.port}/", str(time.time()))
if not os.path.exists(self.savedir):
os.makedirs(self.savedir)
if not os.path.exists(self.savedir + "/input_audio_log"):
os.makedirs(self.savedir + "/input_audio_log")
if not os.path.exists(self.savedir + "/input_audio_vad_log"):
os.makedirs(self.savedir + "/input_audio_vad_log")
if not os.path.exists(self.savedir + "/input_image_log"):
os.makedirs(self.savedir + "/input_image_log")
if not os.path.exists(self.savedir + "/output_audio_log"):
os.makedirs(self.savedir + "/output_audio_log")
if not os.path.exists(self.savedir + "/feedback_log"):
os.makedirs(self.savedir + "/feedback_log")
if not os.path.exists(self.savedir + "/input_audio"):
os.makedirs(self.savedir + "/input_audio")
os.makedirs(self.savedir, exist_ok=True)
os.makedirs(self.savedir + "/input_audio_log", exist_ok=True)
os.makedirs(self.savedir + "/input_audio_vad_log", exist_ok=True)
os.makedirs(self.savedir + "/input_image_log", exist_ok=True)
os.makedirs(self.savedir + "/output_audio_log", exist_ok=True)
os.makedirs(self.savedir + "/feedback_log", exist_ok=True)
os.makedirs(self.savedir + "/input_audio", exist_ok=True)

self.past_session_id = self.session_id
self.audio_prefill = []
Expand Down