app.py

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?"
"Does the video show the vehicle staged on a raised ramp?"
"Does the video show the area around the vehicle clean and organized?"
"Does the video show the vehicle’s bonnet open and upright?"
"Does the technician wear gloves during the video?"
"Does the video show protective items (e.g., seat covers, mats) being used on the vehicle?"
"Does the video show suitable props like a pointer or tire depth gauge being used?"
"Does the video show the technician starting at the nearest point of reference on the vehicle?"
"Does the video demonstrate the use of the Augmented Reality (AR) function?"
"Did the technician verbally explain the condition of at least two items?" / "Does the video show evidence of at least two items (e.g., tires, brakes) being inspected?"
"Did the technician explain the percentage wear of tire treads or brake pads?" / "Does the video show measurement of tire treads or brake pads?"
"Does the video show the technician removing a wheel to demonstrate brake condition clearly?"
"Did the technician provide additional context regarding brake or tire wear?" / "Does the video visually demonstrate brake or tire wear with context?"
"Did the technician explain the consequences of any identified repair areas?" / "Does the video show repair areas or consequences visually?"
"Did the technician verbally compare a new part to a worn part?" / "Does the video show a side-by-side comparison of a new part and a worn part?"
"Does the video include or reference supporting documents (e.g., photographs of identified items)?" """.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()