app.py

import torch
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr
import os

hugapikey=os.environ['openaikey']
MODEL_NAME = "seiching/whisper-small-seiching"
#MODEL_NAME = "openai/whisper-small"
BATCH_SIZE = 8

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

from openai import OpenAI
from concurrent.futures import ThreadPoolExecutor
import tiktoken



def call_openai_api(openaiobj,transcription):

    response = openaiobj.chat.completions.create(
        model="gpt-3.5-turbo",
        temperature=0,
        messages=[
            {
                "role": "system",
                "content": "你是專業的會議紀錄製作員,請根據由語音辨識軟體將會議錄音所轉錄的逐字稿，也請注意逐字稿可能有錯,討論內容細節請略過,請列出經主席確認的會議決議，並要用比較正式及容易閱讀的寫法，避免口語化"
            },
            {
                "role": "user",
                "content": transcription
            }
        ]
    )
    return response.choices[0].message.content
def call_openai_summary(openaiobj,transcription):

    response = openaiobj.chat.completions.create(
        model="gpt-3.5-turbo",
        temperature=0,
        messages=[
            {
                "role": "system",
                "content": "你是專業的文書處理員，請根據由語音辨識軟體將會議錄音所轉錄的逐字稿，摘錄重點，要用比較正式及容易閱讀的寫法，避免口語化"
            },
            {
                "role": "user",
                "content": transcription
            }
        ]
    )
    return response.choices[0].message.content



def split_into_chunks(text, tokens=3500):
    encoding = tiktoken.encoding_for_model('gpt-3.5-turbo')
    words = encoding.encode(text)
    chunks = []
    for i in range(0, len(words), tokens):
        chunks.append(' '.join(encoding.decode(words[i:i + tokens])))
    return chunks

def process_chunks(openaikeystr,inputtext):
    # openaiobj = OpenAI(
    # # This is the default and can be omitted

    # api_key=openaikeystr,
    # )
    if hugapikey=='test':
        realkey=openaikeystr
    else:
        realkey=hugapikey   

    #openaiojb =OpenAI(base_url="http://localhost:1234/v1", api_key="not-needed")
    openaiobj =OpenAI( api_key=realkey)
    text = inputtext
    #openaikey.set_key(openaikeystr)
    #print('process_chunk',openaikey.get_key())
    chunks = split_into_chunks(text)
    response=''
    i=1
    for chunk in chunks:
      
      response=response+'第' +str(i)+'段\n'+call_openai_api(openaiobj,chunk)+'\n\n'
      i=i+1
      # response=response+call_openai_summary(openaiobj,chunk)

    #finalresponse=response+' summary \n\n' +call_openai_api(openaiobj,response)
    return response
    # # Processes chunks in parallel
    # with ThreadPoolExecutor() as executor:
    #     responses = list(executor.map(call_openai_api, [openaiobj,chunks]))
    # return responses
import torch
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr

MODEL_NAME = "seiching/whisper-small-seiching"
BATCH_SIZE = 8
transcribe_text="this is a test"

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)


# Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
    if seconds is not None:
        milliseconds = round(seconds * 1000.0)

        hours = milliseconds // 3_600_000
        milliseconds -= hours * 3_600_000

        minutes = milliseconds // 60_000
        milliseconds -= minutes * 60_000

        seconds = milliseconds // 1_000
        milliseconds -= seconds * 1_000

        hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
        return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
    else:
        # we have a malformed timestamp so just return it as is
        return seconds


def transcribe(file, task, return_timestamps):
    outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task,"language": "chinese",}, return_timestamps=return_timestamps)
    text = outputs["text"]
    if return_timestamps:
        timestamps = outputs["chunks"]
        timestamps = [
            f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
            for chunk in timestamps
        ]
        text = "\n".join(str(feature) for feature in timestamps)
    global transcribe_text
    transcribe_text=text
    # with open('asr_resul.txt', 'w') as f:
    #   f.write(text)

    # ainotes=process_chunks(text)
    # with open("ainotes_result.txt", "a") as f:
    #   f.write(ainotes)

    return text


demo = gr.Blocks()

mic_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
        gr.inputs.Checkbox(default=False, label="Return timestamps"),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="會議紀錄小幫手AINotes",
    description=(
        "可由麥克風錄音或上傳語音檔"
        f" 使用這個模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 先做語音辨識再做會議紀錄摘要"
        " 長度沒有限制"
    ),
    allow_flagging="never",
)
file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
        gr.inputs.Checkbox(default=False, label="Return timestamps"),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="會議紀錄小幫手AINotes",
    description=(
        "可由麥克風錄音或上傳語音檔"
        f" 使用這個模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 先做語音辨識再做會議紀錄摘要"
        " 長度沒有限制"
    ),
    # examples=[
    #     ["./example.flac", "transcribe", False],
    #     ["./example.flac", "transcribe", True],
    # ],
    cache_examples=True,
    allow_flagging="never",
)
def writenotes( apikeystr,inputscript):
  #text=transcribe_text
  #openaikey.set_key(inputkey)
  #openaikey = OpenAIKeyClass(inputkey)
  print('ok')
  if len(inputscript)>10:
    transcribe_text=inputscript
   
  ainotestext=process_chunks(apikeystr,transcribe_text)
   # ainotestext=inputscript
  #ainotestext=""
  # with open('asr_resul.txt', 'w') as f:
  #     #print(transcribe_text)
  #    # f.write(inputkey)
  #     f.write(transcribe_text)
  # with open('ainotes.txt','w') as f:
  #   f.write(ainotestext)
  return ainotestext
ainotes = gr.Interface(
    fn=writenotes,
    inputs=[gr.Textbox(label="OPEN AI API KEY",placeholder="請輸入sk..."),gr.Textbox(label="逐字稿",placeholder="若沒有做語音辨識，請輸入逐字稿")],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="會議紀錄小幫手AINotes",
    description=(
        "可由麥克風錄音或上傳語音檔若有逐字稿可以直接貼在逐字稿"
        f" 使用這個模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 先做語音辨識再做會議紀錄摘要"
        " 長度沒有限制"
    ),
    # examples=[
    #     ["./example.flac", "transcribe", False],
    #     ["./example.flac", "transcribe", True],
    # ],
    cache_examples=True,
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([file_transcribe,mic_transcribe,ainotes], ["語音檔辨識","麥克風語音檔辨識","產生會議紀錄" ])

demo.launch(enable_queue=True)