import gradio as gr import os import time # from omegaconf import OmegaConf import shutil import os # import wget import time variable = [] speech = "" # context_2 = "" from transformers import AutoModelForCausalLM, AutoTokenizer import torch from transformers import AutoTokenizer, AutoModel import logging import torch import os import base64 from pyannote.audio import Pipeline from transformers import pipeline, AutoModelForCausalLM from diarization_utils import diarize from huggingface_hub import HfApi from pydantic import ValidationError from starlette.exceptions import HTTPException # from config import model_settings, InferenceConfig import logging from pydantic import BaseModel from pydantic_settings import BaseSettings from typing import Optional, Literal logger = logging.getLogger(__name__) class ModelSettings(BaseSettings): asr_model: str assistant_model: Optional[str] diarization_model: Optional[str] hf_token: Optional[str] class InferenceConfig(BaseModel): task: Literal["transcribe", "translate"] = "transcribe" batch_size: int = 24 assisted: bool = False chunk_length_s: int = 30 sampling_rate: int = 16000 language: Optional[str] = None num_speakers: Optional[int] = None min_speakers: Optional[int] = None max_speakers: Optional[int] = None # from nemo.collections.asr.parts.utils.diarization_utils import OfflineDiarWithASR # from nemo.collections.asr.parts.utils.decoder_timestamps_utils import ASRDecoderTimeStamps device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") # logger.info(f"Using device: {device.type}") torch_dtype = torch.float32 if device.type == "cpu" else torch.float16 tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b-32k", trust_remote_code=True) model = AutoModel.from_pretrained("THUDM/chatglm3-6b-32k", trust_remote_code=True,device_map='auto') # base_model = "lyogavin/Anima-7B-100K" # tokenizer = AutoTokenizer.from_pretrained(base_model) # model = AutoModelForCausalLM.from_pretrained( # base_model, # bnb_4bit_compute_dtype=torch.float16, # # torch_dtype=torch.float16, # trust_remote_code=True, # device_map="auto", # load_in_4bit=True # ) # model.eval() assistant_model = AutoModelForCausalLM.from_pretrained( "distil-whisper/distil-large-v3", torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) assistant_model.to(device) asr_pipeline = pipeline( "automatic-speech-recognition", model="openai/whisper-large-v3", torch_dtype=torch_dtype, device=device ) HfApi().whoami(os.getenv('HF_TOKEN')) diarization_pipeline = Pipeline.from_pretrained( checkpoint_path="pyannote/speaker-diarization-3.1", use_auth_token=os.getenv('HF_TOKEN'), ) diarization_pipeline.to(device) def upload_file(files): file_paths = [file.name for file in files] global variable variable = file_paths return file_paths def audio_function(): # Call the function and return its result to be displayed time_1 = time.time() paths = variable str1 = "processed speech" for i in paths: str1 = str1 + i str1=str1.replace("processed speech","") print("before processing ffmpeg ! ") command_to_mp4_to_wav = "ffmpeg -i {} current_out.wav -y" #-acodec pcm_s16le -ar 16000 -ac 1 os.system(command_to_mp4_to_wav.format(str1)) print("after ffmpeg") # os.system("insanely-fast-whisper --file-name {}_new.wav --task transcribe --hf_token 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".format(str1.replace("mp3",""))) parameters = InferenceConfig() generate_kwargs = { "task": parameters.task, "language": parameters.language, "assistant_model": assistant_model if parameters.assisted else None } with open("current_out.wav", 'rb') as f: audio_encoded = base64.b64encode(f.read()).decode("utf-8") file = base64.b64decode(audio_encoded) asr_outputs = asr_pipeline( file, chunk_length_s=parameters.chunk_length_s, batch_size=parameters.batch_size, generate_kwargs=generate_kwargs, return_timestamps=True, ) transcript = diarize(diarization_pipeline, file, parameters, asr_outputs) global speech speech = transcript return transcript,asr_outputs["chunks"],asr_outputs["text"] def audio_function2(): # Call the function and return its result to be displayed # global speech str2 = speech time_3 = time.time() # prompt = " {} generate medical subjective objective assessment plan (soap) notes ?".format(str2) prompt = """ {} "Did the technician introduce themselves at the start of the video?" "Did the technician mention their level of experience during the video?" "Did the technician use the customer's name during the introduction?" "Did the technician mention the name of the Customer Advisor managing the booking?" "Did the technician provide a personal recommendation statement in the video?" "Did the technician mention service plans available to the customer?" "Did the technician mention genuine Volkswagen parts during the video?" "Did the technician mention the national parts and labor warranty?" "Did the technician mention the 7-day price promise during the video?" "Did the technician thank the customer for choosing Parkway Volkswagen?" "Did the technician provide a clear NANO statement at the end of the video?" """.format(str2) # model = model.eval() response, history = model.chat(tokenizer, prompt, history=[]) print(response) # del model # del tokenizer # torch.cuda.empty_cache() time_4 = time.time() # response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history) # print(response) # inputs = tokenizer(prompt, return_tensors="pt") # inputs['input_ids'] = inputs['input_ids'].cuda() # inputs['attention_mask'] = inputs['attention_mask'].cuda() # generate_ids = model.generate(**inputs, max_new_tokens=4096, # only_last_logit=True, # to save memory # use_cache=False, # when run into OOM, enable this can save memory # xentropy=True) # output = tokenizer.batch_decode(generate_ids, # skip_special_tokens=True, # clean_up_tokenization_spaces=False) # tokenizer = AutoTokenizer.from_pretrained("togethercomputer/LLaMA-2-7B-32K") # model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K", trust_remote_code=True, torch_dtype=torch.float16,device_map="auto",bnb_4bit_compute_dtype=torch.float16,load_in_4bit=True) # input_context = "summarize "+" the following {}".format(str2) # input_ids = tokenizer.encode(input_context, return_tensors="pt").cuda() # output = model.generate(input_ids, max_new_tokens=512, temperature=0.7) # output_text = tokenizer.decode(output[0], skip_special_tokens=True) # print(output_text,"wow what happened ") # return output return response,str(int(time_4-time_3)) + " seconds" with gr.Blocks() as demo: file_output = gr.File() upload_button = gr.UploadButton("Click to Upload a File", file_types=["audio","video"], file_count="multiple") upload_button.upload(upload_file, upload_button, file_output) gr.Markdown("## Click process audio to display text from audio file") submit_button = gr.Button("Process Audio") output_text = gr.Textbox(label="Speech Diarization") output_text_2 = gr.Textbox(label="Speech chunks") submit_button.click(audio_function, outputs=[output_text,output_text_2,gr.Textbox(label=" asr_text :")]) gr.Markdown("## Click the Summarize to display call summary") submit_button = gr.Button("Summarize") output_text = gr.Textbox(label="Sales Call Notes") submit_button.click(audio_function2, outputs=[output_text,gr.Textbox(label="Time Taken :")]) demo.launch()