app.py

from datetime import datetime
import json
import math
from typing import Iterator, Union
import argparse
import time
from io import StringIO
import os
import pathlib
import tempfile
import zipfile
import numpy as np
import torch

from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
from src.hooks.progressListener import ProgressListener
from src.hooks.subTaskProgressListener import SubTaskProgressListener
from src.hooks.whisperProgressHook import create_progress_listener_handle
from src.languages import get_language_names
from src.modelCache import ModelCache
from src.prompts.jsonPromptStrategy import JsonPromptStrategy
from src.prompts.prependPromptStrategy import PrependPromptStrategy
from src.source import get_audio_source_collection
from src.vadParallel import ParallelContext, ParallelTranscription

# External programs
import ffmpeg

# UI
import gradio as gr

from src.download import ExceededMaximumDuration, download_url
from src.utils import optional_int, slugify, write_srt, write_vtt
from src.utils import post_processing, replace, copy_text, on_token_change, num_tokens_from_messages, chat_with_gpt
from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription
from src.whisper.abstractWhisperContainer import AbstractWhisperContainer
from src.whisper.whisperFactory import create_whisper_container

# Configure more application defaults in config.json5

# Gradio seems to truncate files without keeping the extension, so we need to truncate the file prefix ourself 
MAX_FILE_PREFIX_LENGTH = 17

# Limit auto_parallel to a certain number of CPUs (specify vad_cpu_cores to get a higher number)
MAX_AUTO_CPU_CORES = 8

WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]

class VadOptions:
    def __init__(self, vad: str = None, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1, 
                                        vadInitialPromptMode: Union[VadInitialPromptMode, str] = VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
        self.vad = vad
        self.vadMergeWindow = vadMergeWindow
        self.vadMaxMergeSize = vadMaxMergeSize
        self.vadPadding = vadPadding
        self.vadPromptWindow = vadPromptWindow
        self.vadInitialPromptMode = vadInitialPromptMode if isinstance(vadInitialPromptMode, VadInitialPromptMode) \
                                        else VadInitialPromptMode.from_string(vadInitialPromptMode)

class WhisperTranscriber:
    def __init__(self, input_audio_max_duration: float = None, vad_process_timeout: float = None, 
                 vad_cpu_cores: int = 1, delete_uploaded_files: bool = False, output_dir: str = None, 
                 app_config: ApplicationConfig = None):
        self.model_cache = ModelCache()
        self.parallel_device_list = None
        self.gpu_parallel_context = None
        self.cpu_parallel_context = None
        self.vad_process_timeout = vad_process_timeout
        self.vad_cpu_cores = vad_cpu_cores

        self.vad_model = None
        self.inputAudioMaxDuration = input_audio_max_duration
        self.deleteUploadedFiles = delete_uploaded_files
        self.output_dir = output_dir

        self.app_config = app_config

    def set_parallel_devices(self, vad_parallel_devices: str):
        self.parallel_device_list = [ device.strip() for device in vad_parallel_devices.split(",") ] if vad_parallel_devices else None

    def set_auto_parallel(self, auto_parallel: bool):
        if auto_parallel:
            if torch.cuda.is_available():
                self.parallel_device_list = [ str(gpu_id) for gpu_id in range(torch.cuda.device_count())]

            self.vad_cpu_cores = min(os.cpu_count(), MAX_AUTO_CPU_CORES)
            print("[Auto parallel] Using GPU devices " + str(self.parallel_device_list) + " and " + str(self.vad_cpu_cores) + " CPU cores for VAD/transcription.")

    # Entry function for the simple tab
    def transcribe_webui_simple(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                                vad, vadMergeWindow, vadMaxMergeSize, 
                                word_timestamps: bool = False, highlight_words: bool = False):
        return self.transcribe_webui_simple_progress(modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                                                     vad, vadMergeWindow, vadMaxMergeSize, 
                                                     word_timestamps, highlight_words)
    
    # Entry function for the simple tab progress
    def transcribe_webui_simple_progress(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                                         vad, vadMergeWindow, vadMaxMergeSize, 
                                         word_timestamps: bool = False, highlight_words: bool = False, 
                                         progress=gr.Progress()):
        
        vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, self.app_config.vad_padding, self.app_config.vad_prompt_window, self.app_config.vad_initial_prompt_mode)

        return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions, 
                                     word_timestamps=word_timestamps, highlight_words=highlight_words, progress=progress)

    # Entry function for the full tab
    def transcribe_webui_full(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                              vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode, 
                              # Word timestamps
                              word_timestamps: bool, highlight_words: bool, prepend_punctuations: str, append_punctuations: str,
                              initial_prompt: str, temperature: float, best_of: int, beam_size: int, patience: float, length_penalty: float, suppress_tokens: str, 
                              condition_on_previous_text: bool, fp16: bool, temperature_increment_on_fallback: float, 
                              compression_ratio_threshold: float, logprob_threshold: float, no_speech_threshold: float):
        
        return self.transcribe_webui_full_progress(modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                                vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode,
                                word_timestamps, highlight_words, prepend_punctuations, append_punctuations,
                                initial_prompt, temperature, best_of, beam_size, patience, length_penalty, suppress_tokens,
                                condition_on_previous_text, fp16, temperature_increment_on_fallback,
                                compression_ratio_threshold, logprob_threshold, no_speech_threshold)

    # Entry function for the full tab with progress
    def transcribe_webui_full_progress(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                                        vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode,
                                        # Word timestamps
                                        word_timestamps: bool, highlight_words: bool, prepend_punctuations: str, append_punctuations: str,   
                                        initial_prompt: str, temperature: float, best_of: int, beam_size: int, patience: float, length_penalty: float, suppress_tokens: str, 
                                        condition_on_previous_text: bool, fp16: bool, temperature_increment_on_fallback: float, 
                                        compression_ratio_threshold: float, logprob_threshold: float, no_speech_threshold: float, 
                                        progress=gr.Progress()):

        # Handle temperature_increment_on_fallback
        if temperature_increment_on_fallback is not None:
            temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
        else:
            temperature = [temperature]

        vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode)

        return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions,
                                     initial_prompt=initial_prompt, temperature=temperature, best_of=best_of, beam_size=beam_size, patience=patience, length_penalty=length_penalty, suppress_tokens=suppress_tokens,
                                     condition_on_previous_text=condition_on_previous_text, fp16=fp16,
                                     compression_ratio_threshold=compression_ratio_threshold, logprob_threshold=logprob_threshold, no_speech_threshold=no_speech_threshold, 
                                     word_timestamps=word_timestamps, prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, highlight_words=highlight_words,
                                     progress=progress)

    def transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
                         vadOptions: VadOptions, progress: gr.Progress = None, highlight_words: bool = False, 
                         **decodeOptions: dict):
        try:
            sources = self.__get_source(urlData, multipleFiles, microphoneData)

            try:
                selectedLanguage = languageName.lower() if len(languageName) > 0 else None
                selectedModel = modelName if modelName is not None else "base"

                model = create_whisper_container(whisper_implementation=self.app_config.whisper_implementation, 
                                                 model_name=selectedModel, compute_type=self.app_config.compute_type, 
                                                 cache=self.model_cache, models=self.app_config.models)

                # Result
                download = []
                zip_file_lookup = {}
                text = ""
                vtt = ""

                # Write result
                downloadDirectory = tempfile.mkdtemp()
                source_index = 0

                outputDirectory = self.output_dir if self.output_dir is not None else downloadDirectory

                # Progress
                total_duration = sum([source.get_audio_duration() for source in sources])
                current_progress = 0

                # A listener that will report progress to Gradio
                root_progress_listener = self._create_progress_listener(progress)

                # Execute whisper
                for source in sources:
                    source_prefix = ""
                    source_audio_duration = source.get_audio_duration()

                    if (len(sources) > 1):
                        # Prefix (minimum 2 digits)
                        source_index += 1
                        source_prefix = str(source_index).zfill(2) + "_"
                        print("Transcribing ", source.source_path)

                    scaled_progress_listener = SubTaskProgressListener(root_progress_listener, 
                                                   base_task_total=total_duration,
                                                   sub_task_start=current_progress,
                                                   sub_task_total=source_audio_duration)

                    # Transcribe
                    result = self.transcribe_file(model, source.source_path, selectedLanguage, task, vadOptions, scaled_progress_listener, **decodeOptions)
                    filePrefix = slugify(source_prefix + source.get_short_name(), allow_unicode=True)

                    # Update progress
                    current_progress += source_audio_duration

                    source_download, source_text, source_vtt = self.write_result(result, filePrefix, outputDirectory, highlight_words)

                    if len(sources) > 1:
                        # Add new line separators
                        if (len(source_text) > 0):
                            source_text += os.linesep + os.linesep
                        if (len(source_vtt) > 0):
                            source_vtt += os.linesep + os.linesep

                        # Append file name to source text too
                        source_text = source.get_full_name() + ":" + os.linesep + source_text
                        source_vtt = source.get_full_name() + ":" + os.linesep + source_vtt

                    # Add to result
                    download.extend(source_download)
                    text += source_text
                    vtt += source_vtt

                    if (len(sources) > 1):
                        # Zip files support at least 260 characters, but we'll play it safe and use 200
                        zipFilePrefix = slugify(source_prefix + source.get_short_name(max_length=200), allow_unicode=True)

                        # File names in ZIP file can be longer
                        for source_download_file in source_download:
                            # Get file postfix (after last -)
                            filePostfix = os.path.basename(source_download_file).split("-")[-1]
                            zip_file_name = zipFilePrefix + "-" + filePostfix
                            zip_file_lookup[source_download_file] = zip_file_name

                # Create zip file from all sources
                if len(sources) > 1:
                    downloadAllPath = os.path.join(downloadDirectory, "All_Output-" + datetime.now().strftime("%Y%m%d-%H%M%S") + ".zip")

                    with zipfile.ZipFile(downloadAllPath, 'w', zipfile.ZIP_DEFLATED) as zip:
                        for download_file in download:
                            # Get file name from lookup
                            zip_file_name = zip_file_lookup.get(download_file, os.path.basename(download_file))
                            zip.write(download_file, arcname=zip_file_name)

                    download.insert(0, downloadAllPath)

                return download, text, vtt

            finally:
                # Cleanup source
                if self.deleteUploadedFiles:
                    for source in sources:
                        print("Deleting source file " + source.source_path)

                        try:
                            os.remove(source.source_path)
                        except Exception as e:
                            # Ignore error - it's just a cleanup
                            print("Error deleting source file " + source.source_path + ": " + str(e))
        
        except ExceededMaximumDuration as e:
            return [], ("[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s"), "[ERROR]"

    def transcribe_file(self, model: AbstractWhisperContainer, audio_path: str, language: str, task: str = None, 
                        vadOptions: VadOptions = VadOptions(), 
                        progressListener: ProgressListener = None, **decodeOptions: dict):
        
        initial_prompt = decodeOptions.pop('initial_prompt', None)

        if progressListener is None:
            # Default progress listener
            progressListener = ProgressListener()

        if ('task' in decodeOptions):
            task = decodeOptions.pop('task')

        initial_prompt_mode = vadOptions.vadInitialPromptMode

        # Set default initial prompt mode
        if (initial_prompt_mode is None):
            initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT

        if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or 
            initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
            # Prepend initial prompt
            prompt_strategy = PrependPromptStrategy(initial_prompt, initial_prompt_mode)
        elif (vadOptions.vadInitialPromptMode == VadInitialPromptMode.JSON_PROMPT_MODE):
            # Use a JSON format to specify the prompt for each segment
            prompt_strategy = JsonPromptStrategy(initial_prompt)
        else:
            raise ValueError("Invalid vadInitialPromptMode: " + initial_prompt_mode)

        # Callable for processing an audio file
        whisperCallable = model.create_callback(language, task, prompt_strategy=prompt_strategy, **decodeOptions)

        # The results
        if (vadOptions.vad == 'silero-vad'):
            # Silero VAD where non-speech gaps are transcribed
            process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadOptions)
            result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps, progressListener=progressListener)
        elif (vadOptions.vad == 'silero-vad-skip-gaps'):
            # Silero VAD where non-speech gaps are simply ignored
            skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadOptions)
            result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps, progressListener=progressListener)
        elif (vadOptions.vad == 'silero-vad-expand-into-gaps'):
            # Use Silero VAD where speech-segments are expanded into non-speech gaps
            expand_gaps = self._create_silero_config(NonSpeechStrategy.EXPAND_SEGMENT, vadOptions)
            result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps, progressListener=progressListener)
        elif (vadOptions.vad == 'periodic-vad'):
            # Very simple VAD - mark every 5 minutes as speech. This makes it less likely that Whisper enters an infinite loop, but
            # it may create a break in the middle of a sentence, causing some artifacts.
            periodic_vad = VadPeriodicTranscription()
            period_config = PeriodicTranscriptionConfig(periodic_duration=vadOptions.vadMaxMergeSize, max_prompt_window=vadOptions.vadPromptWindow)
            result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)

        else:
            if (self._has_parallel_devices()):
                # Use a simple period transcription instead, as we need to use the parallel context
                periodic_vad = VadPeriodicTranscription()
                period_config = PeriodicTranscriptionConfig(periodic_duration=math.inf, max_prompt_window=1)

                result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
            else:
                # Default VAD
                result = whisperCallable.invoke(audio_path, 0, None, None, progress_listener=progressListener)

        return result

    def _create_progress_listener(self, progress: gr.Progress):
        if (progress is None):
            # Dummy progress listener
            return ProgressListener()
        
        class ForwardingProgressListener(ProgressListener):
            def __init__(self, progress: gr.Progress):
                self.progress = progress

            def on_progress(self, current: Union[int, float], total: Union[int, float]):
                # From 0 to 1
                self.progress(current / total)

            def on_finished(self):
                self.progress(1)

        return ForwardingProgressListener(progress)

    def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig, 
                    progressListener: ProgressListener = None):
        if (not self._has_parallel_devices()):
            # No parallel devices, so just run the VAD and Whisper in sequence
            return vadModel.transcribe(audio_path, whisperCallable, vadConfig, progressListener=progressListener)

        gpu_devices = self.parallel_device_list

        if (gpu_devices is None or len(gpu_devices) == 0):
            # No GPU devices specified, pass the current environment variable to the first GPU process. This may be NULL.
            gpu_devices = [os.environ.get("CUDA_VISIBLE_DEVICES", None)]

        # Create parallel context if needed
        if (self.gpu_parallel_context is None):
            # Create a context wih processes and automatically clear the pool after 1 hour of inactivity
            self.gpu_parallel_context = ParallelContext(num_processes=len(gpu_devices), auto_cleanup_timeout_seconds=self.vad_process_timeout)
        # We also need a CPU context for the VAD
        if (self.cpu_parallel_context is None):
            self.cpu_parallel_context = ParallelContext(num_processes=self.vad_cpu_cores, auto_cleanup_timeout_seconds=self.vad_process_timeout)

        parallel_vad = ParallelTranscription()
        return parallel_vad.transcribe_parallel(transcription=vadModel, audio=audio_path, whisperCallable=whisperCallable,  
                                                config=vadConfig, cpu_device_count=self.vad_cpu_cores, gpu_devices=gpu_devices, 
                                                cpu_parallel_context=self.cpu_parallel_context, gpu_parallel_context=self.gpu_parallel_context, 
                                                progress_listener=progressListener) 

    def _has_parallel_devices(self):
        return (self.parallel_device_list is not None and len(self.parallel_device_list) > 0) or self.vad_cpu_cores > 1

    def _concat_prompt(self, prompt1, prompt2):
        if (prompt1 is None):
            return prompt2
        elif (prompt2 is None):
            return prompt1
        else:
            return prompt1 + " " + prompt2

    def _create_silero_config(self, non_speech_strategy: NonSpeechStrategy, vadOptions: VadOptions):
        # Use Silero VAD 
        if (self.vad_model is None):
            self.vad_model = VadSileroTranscription()

        config = TranscriptionConfig(non_speech_strategy = non_speech_strategy, 
                max_silent_period=vadOptions.vadMergeWindow, max_merge_size=vadOptions.vadMaxMergeSize, 
                segment_padding_left=vadOptions.vadPadding, segment_padding_right=vadOptions.vadPadding, 
                max_prompt_window=vadOptions.vadPromptWindow)

        return config

    def write_result(self, result: dict, source_name: str, output_dir: str, highlight_words: bool = False):
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)

        text = result["text"]
        language = result["language"]
        languageMaxLineWidth = self.__get_max_line_width(language)

        print("Max line width " + str(languageMaxLineWidth))
        vtt = self.__get_subs(result["segments"], "vtt", languageMaxLineWidth, highlight_words=highlight_words)
        srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth, highlight_words=highlight_words)
        json_result = json.dumps(result, indent=4, ensure_ascii=False)

        output_files = []
        output_files.append(self.__create_file(srt, output_dir, source_name + "-subs.srt"));
        output_files.append(self.__create_file(vtt, output_dir, source_name + "-subs.vtt"));
        output_files.append(self.__create_file(text, output_dir, source_name + "-transcript.txt"));
        output_files.append(self.__create_file(json_result, output_dir, source_name + "-result.json"));

        return output_files, text, vtt

    def clear_cache(self):
        self.model_cache.clear()
        self.vad_model = None

    def __get_source(self, urlData, multipleFiles, microphoneData):
        return get_audio_source_collection(urlData, multipleFiles, microphoneData, self.inputAudioMaxDuration)

    def __get_max_line_width(self, language: str) -> int:
        if (language and language.lower() in ["japanese", "ja", "chinese", "zh"]):
            # Chinese characters and kana are wider, so limit line length to 40 characters
            return 40
        else:
            # TODO: Add more languages
            # 80 latin characters should fit on a 1080p/720p screen
            return 80

    def __get_subs(self, segments: Iterator[dict], format: str, maxLineWidth: int, highlight_words: bool = False) -> str:
        segmentStream = StringIO()

        if format == 'vtt':
            write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
        elif format == 'srt':
            write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
        else:
            raise Exception("Unknown format " + format)

        segmentStream.seek(0)
        return segmentStream.read()

    def __create_file(self, text: str, directory: str, fileName: str) -> str:
        # Write the text to a file
        with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file:
            file.write(text)

        return file.name

    def close(self):
        print("Closing parallel contexts")
        self.clear_cache()

        if (self.gpu_parallel_context is not None):
            self.gpu_parallel_context.close()
        if (self.cpu_parallel_context is not None):
            self.cpu_parallel_context.close()


def create_ui(app_config: ApplicationConfig):
    ui = WhisperTranscriber(app_config.input_audio_max_duration, app_config.vad_process_timeout, app_config.vad_cpu_cores, 
                            app_config.delete_uploaded_files, app_config.output_dir, app_config)

    # Specify a list of devices to use for parallel processing
    ui.set_parallel_devices(app_config.vad_parallel_devices)
    ui.set_auto_parallel(app_config.auto_parallel)

    is_whisper = False

    if app_config.whisper_implementation == "whisper":
        implementation_name = "Whisper"
        is_whisper = True
    elif app_config.whisper_implementation in ["faster-whisper", "faster_whisper"]:
        implementation_name = "Faster Whisper"
    else:
        # Try to convert from camel-case to title-case
        implementation_name = app_config.whisper_implementation.title().replace("_", " ").replace("-", " ")

    ui_description = implementation_name + " is a general-purpose speech recognition model. It is trained on a large dataset of diverse " 
    ui_description += " audio and is also a multi-task model that can perform multilingual speech recognition "
    ui_description += " as well as speech translation and language identification. "

    ui_description += "\n\n\n\nFor longer audio files (>10 minutes) not in English, it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option."

    # Recommend faster-whisper
    if is_whisper:
        ui_description += "\n\n\n\nFor faster inference on GPU, try [faster-whisper](https://huggingface.co/spaces/aadnk/faster-whisper-webui)."

    if app_config.input_audio_max_duration > 0:
        ui_description += "\n\n" + "Max audio file length: " + str(app_config.input_audio_max_duration) + " s"

    ui_article = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)."

    whisper_models = app_config.get_model_names()

    common_inputs = lambda : [
        gr.Dropdown(choices=whisper_models, value=app_config.default_model_name, label="Model"),
        gr.Dropdown(choices=sorted(get_language_names()), label="Language", value=app_config.language),
        gr.Text(label="URL (YouTube, bilibili etc.)"),
        gr.File(label="Upload Files", file_count="multiple"),
        gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
        gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task),
    ]

    common_vad_inputs = lambda : [
        gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=app_config.default_vad, label="VAD"),
        gr.Number(label="VAD - Merge Window (s)", precision=0, value=app_config.vad_merge_window),
        gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=app_config.vad_max_merge_size),
    ]
    
    common_word_timestamps_inputs = lambda : [
        gr.Checkbox(label="Word Timestamps", value=app_config.word_timestamps),
        gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words),
    ]

    # TODO:send current tab result to another tab
    def send_result_to_tab(result):
        # write curent tab output as target tab input
        # input_text = result
        # switch current tab to target tab
        test_postprocess.render()
        # fill the gr.TextArea with input result
        return gr.update(value=result)

    is_queue_mode = app_config.queue_concurrency_count is not None and app_config.queue_concurrency_count > 0    

    simple_transcribe = gr.Interface(fn=ui.transcribe_webui_simple_progress if is_queue_mode else ui.transcribe_webui_simple, 
                                     description=ui_description, article=ui_article, inputs=[
        *common_inputs(),
        *common_vad_inputs(),
        *common_word_timestamps_inputs(),
    ], outputs=[
        gr.File(label="Download"),
        gr.Text(label="Transcription"), 
        gr.Text(label="Segments")
    ])

    # reimplement above simple_transcribe using gr.Blocks() instead of gr.Interface()
    simple_transcribe = gr.Blocks()
    with simple_transcribe:
        gr.Markdown(
            """
            Whisper是OpenAI开源的一个多语言语音识别模型，可以用来将音频转换为文本。\n
            faster-whisper 是使用 CTranslate2 对 OpenAI Whisper 模型的重新实现，CTranslate2 是 Transformer 模型的快速推理引擎。\n
            此实现比 OpenAI Whisper 快 4 倍，同时使用更少的内存，达到相同的精度。\n
            您只需要一个视频网址链接，或实时录制音频，或上传本地音频文件，该App会将音频转录成文本，您还可以对转录得到的文本进行二次编辑。\n
            对于非英语的较长音频文件（>10 分钟），建议您在 VAD 选项中选择 Silero VAD（Voice Activity Detector）。\n
            项目github地址：https://github.com/wwdok/faster-whisper-webui-cn
            """
        )
        with gr.Row():
            with gr.Column():
                # *common_inputs(),
                i1 = gr.Dropdown(choices=whisper_models, value=app_config.default_model_name, label="Model")
                i2 = gr.Dropdown(choices=sorted(get_language_names()), label="Language", value=app_config.language)
                i3 = gr.Text(label="URL (YouTube, bilibili etc.)")
                i4 = gr.File(label="Upload Files", file_count="multiple")
                i5 = gr.Audio(source="microphone", type="filepath", label="Microphone Input")
                i6 = gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task)

                # *common_vad_inputs(),
                i7 = gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=app_config.default_vad, label="VAD")
                i8 = gr.Number(label="VAD - Merge Window (s)", precision=0, value=app_config.vad_merge_window)
                i9 = gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=app_config.vad_max_merge_size)

                # *common_word_timestamps_inputs(),
                i10 = gr.Checkbox(label="Word Timestamps", value=app_config.word_timestamps)
                i11 = gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words)

                submit_btn = gr.Button("开始")
            with gr.Column():
                o1 = gr.File(label="Download")
                o2 = gr.Text(label="Transcription").style(show_copy_button=True)
                o3 = gr.Text(label="Segments")
                # send_btn = gr.Button("发送到后处理标签页")
        submit_btn.click(ui.transcribe_webui_simple_progress if is_queue_mode else ui.transcribe_webui_simple, 
                        inputs=[i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11], 
                        outputs=[o1, o2, o3])
        # send_btn.click(send_result_to_tab, inputs=o2, outputs=input_text)

    full_description = ui_description + "\n\n\n\n" + "Be careful when changing some of the options in the full interface - this can cause the model to crash."

    full_transcribe = gr.Interface(fn=ui.transcribe_webui_full_progress if is_queue_mode else ui.transcribe_webui_full,
                                   description=full_description, article=ui_article, inputs=[
        *common_inputs(),

        *common_vad_inputs(),
        gr.Number(label="VAD - Padding (s)", precision=None, value=app_config.vad_padding),
        gr.Number(label="VAD - Prompt Window (s)", precision=None, value=app_config.vad_prompt_window),
        gr.Dropdown(choices=VAD_INITIAL_PROMPT_MODE_VALUES, label="VAD - Initial Prompt Mode"),
        
        *common_word_timestamps_inputs(),
        gr.Text(label="Word Timestamps - Prepend Punctuations", value=app_config.prepend_punctuations),
        gr.Text(label="Word Timestamps - Append Punctuations", value=app_config.append_punctuations),

        gr.TextArea(label="Initial Prompt"),
        gr.Number(label="Temperature", value=app_config.temperature),
        gr.Number(label="Best Of - Non-zero temperature", value=app_config.best_of, precision=0),
        gr.Number(label="Beam Size - Zero temperature", value=app_config.beam_size, precision=0),
        gr.Number(label="Patience - Zero temperature", value=app_config.patience),
        gr.Number(label="Length Penalty - Any temperature", value=app_config.length_penalty), 
        gr.Text(label="Suppress Tokens - Comma-separated list of token IDs", value=app_config.suppress_tokens),
        gr.Checkbox(label="Condition on previous text", value=app_config.condition_on_previous_text),
        gr.Checkbox(label="FP16", value=app_config.fp16),
        gr.Number(label="Temperature increment on fallback", value=app_config.temperature_increment_on_fallback),
        gr.Number(label="Compression ratio threshold", value=app_config.compression_ratio_threshold),
        gr.Number(label="Logprob threshold", value=app_config.logprob_threshold),
        gr.Number(label="No speech threshold", value=app_config.no_speech_threshold),
    ], outputs=[
        gr.File(label="Download"),
        gr.Text(label="Transcription").style(show_copy_button=True), 
        gr.Text(label="Segments")
    ])

    def switch_punc_method(use_chatgpt, auto_punc):
        if use_chatgpt == True and auto_punc == True:
            return gr.update(), gr.update(), gr.update()
        elif use_chatgpt == True and auto_punc == False:
            return gr.update(visible=True), gr.update(visible=True, interactive=True), gr.update(visible=False)
        elif use_chatgpt == False and auto_punc == True:
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
        else:
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
    
    test_postprocess = gr.Blocks()

    with test_postprocess:
        gr.Markdown(
        """
        后处理Simple或Full标签页输出的Transcription里的文本，也可以单独使用
        """
        )
        with gr.Row():
            with gr.Column():
                input_text = gr.TextArea(label="输入文本", placeholder="在此处粘贴你的待处理文本")
                tokens_count = gr.Markdown(label="Tokens 计数: 0", visible=False)
                use_chatgpt = gr.Checkbox(label="使用ChatGPT自动纠正错别字和添加标点符号", value=False)
                user_token = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", visible=False,
                                        label="输入你的 OpenAI API Key. 你可以从这里(https://platform.openai.com/account/api-keys)获取.\
                                        \n⚠ 注意！使用ChatGPT来处理文本会消耗大量的tokens，免费版用户谨慎使用！")
                apply_correction = gr.Checkbox(label="使用pycorrector纠正错别字", value=False)
                auto_punc = gr.Checkbox(label="使用paddle auto punc自动添加标点符号", value=False)
                separator = gr.Text(label="使用统一的标点符号(比如逗号，或换行\\n)", value="，")
                remove_words = gr.Text(label="去掉的语气助词", value="呢，啊，哦，嗯，嘛，吧，呀，哈，哇，呐，噢，嘞，哎")
                submit_btn = gr.Button("提交")
            with gr.Column():
                output_text = gr.TextArea(label="输出文本", interactive=True).style(show_copy_button=True)
                with gr.Row():
                    src_word = gr.Text(label="替换前的字符")
                    target_word = gr.Text(label="替换后的字符")
                replace_btn = gr.Button("替换")
                copy_btn = gr.Button("复制到剪贴板")
        input_text.change(num_tokens_from_messages, inputs=[input_text], outputs=[tokens_count])
        auto_punc.change(switch_punc_method, inputs=[use_chatgpt, auto_punc], outputs=[tokens_count, user_token, separator])
        use_chatgpt.change(switch_punc_method, inputs=[use_chatgpt, auto_punc], outputs=[tokens_count, user_token, separator])
        user_token.change(on_token_change, inputs=[user_token], outputs=[])
        submit_btn.click(post_processing, inputs=[input_text, use_chatgpt, user_token, apply_correction, auto_punc, separator, remove_words], outputs=output_text)
        replace_btn.click(replace, inputs=[output_text, src_word, target_word], outputs=output_text)
        copy_btn.click(copy_text, inputs=output_text)
    
    demo = gr.TabbedInterface([simple_transcribe, full_transcribe, test_postprocess], tab_names=["Simple", "Full", "Text Postprocess"])

    # Queue up the demo
    if is_queue_mode:
        demo.queue(concurrency_count=app_config.queue_concurrency_count)
        print("Queue mode enabled (concurrency count: " + str(app_config.queue_concurrency_count) + ")")
    else:
        print("Queue mode disabled - progress bars will not be shown.")
   
    demo.launch(share=app_config.share, server_name=app_config.server_name, server_port=app_config.server_port)
    
    # Clean up
    ui.close()

if __name__ == '__main__':
    default_app_config = ApplicationConfig.create_default()
    whisper_models = default_app_config.get_model_names()

    # Environment variable overrides
    default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", default_app_config.whisper_implementation)

    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("--input_audio_max_duration", type=int, default=default_app_config.input_audio_max_duration, \
                        help="Maximum audio file length in seconds, or -1 for no limit.") # 600
    parser.add_argument("--share", type=bool, default=default_app_config.share, \
                        help="True to share the app on HuggingFace.") # False
    parser.add_argument("--server_name", type=str, default=default_app_config.server_name, \
                        help="The host or IP to bind to. If None, bind to localhost.") # None
    parser.add_argument("--server_port", type=int, default=default_app_config.server_port, \
                        help="The port to bind to.") # 7860
    parser.add_argument("--queue_concurrency_count", type=int, default=default_app_config.queue_concurrency_count, \
                        help="The number of concurrent requests to process.") # 1
    parser.add_argument("--default_model_name", type=str, choices=whisper_models, default=default_app_config.default_model_name, \
                        help="The default model name.") # medium
    parser.add_argument("--default_vad", type=str, default=default_app_config.default_vad, \
                        help="The default VAD.") # silero-vad
    parser.add_argument("--vad_initial_prompt_mode", type=str, default=default_app_config.vad_initial_prompt_mode, choices=VAD_INITIAL_PROMPT_MODE_VALUES, \
                        help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment
    parser.add_argument("--vad_parallel_devices", type=str, default=default_app_config.vad_parallel_devices, \
                        help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # ""
    parser.add_argument("--vad_cpu_cores", type=int, default=default_app_config.vad_cpu_cores, \
                        help="The number of CPU cores to use for VAD pre-processing.") # 1
    parser.add_argument("--vad_process_timeout", type=float, default=default_app_config.vad_process_timeout, \
                        help="The number of seconds before inactivate processes are terminated. Use 0 to close processes immediately, or None for no timeout.")
    parser.add_argument("--auto_parallel", type=bool, default=default_app_config.auto_parallel, \
                        help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False
    parser.add_argument("--output_dir", "-o", type=str, default=default_app_config.output_dir, \
                        help="directory to save the outputs")
    parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\
                        help="the Whisper implementation to use")
    parser.add_argument("--compute_type", type=str, default=default_app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \
                        help="the compute type to use for inference")
    parser.add_argument("--threads", type=optional_int, default=0, 
                        help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")

    args = parser.parse_args().__dict__

    updated_config = default_app_config.update(**args)

    if (threads := args.pop("threads")) > 0:
        torch.set_num_threads(threads)

    create_ui(app_config=updated_config)