from datetime import datetime import math from typing import Iterator import argparse from io import StringIO import os import pathlib import tempfile import zipfile import numpy as np import torch from src.modelCache import ModelCache 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 slugify, write_srt, write_vtt from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription from src.whisperContainer import WhisperContainer import requests # Limitations (set to -1 to disable) DEFAULT_INPUT_AUDIO_MAX_DURATION = 600 # seconds # Whether or not to automatically delete all uploaded files, to save disk space DELETE_UPLOADED_FILES = True # 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 LANGUAGES = [ "English", "Chinese", "German", "Spanish", "Russian", "Korean", "French", "Japanese", "Portuguese", "Turkish", "Polish", "Catalan", "Dutch", "Arabic", "Swedish", "Italian", "Indonesian", "Hindi", "Finnish", "Vietnamese", "Hebrew", "Ukrainian", "Greek", "Malay", "Czech", "Romanian", "Danish", "Hungarian", "Tamil", "Norwegian", "Thai", "Urdu", "Croatian", "Bulgarian", "Lithuanian", "Latin", "Maori", "Malayalam", "Welsh", "Slovak", "Telugu", "Persian", "Latvian", "Bengali", "Serbian", "Azerbaijani", "Slovenian", "Kannada", "Estonian", "Macedonian", "Breton", "Basque", "Icelandic", "Armenian", "Nepali", "Mongolian", "Bosnian", "Kazakh", "Albanian", "Swahili", "Galician", "Marathi", "Punjabi", "Sinhala", "Khmer", "Shona", "Yoruba", "Somali", "Afrikaans", "Occitan", "Georgian", "Belarusian", "Tajik", "Sindhi", "Gujarati", "Amharic", "Yiddish", "Lao", "Uzbek", "Faroese", "Haitian Creole", "Pashto", "Turkmen", "Nynorsk", "Maltese", "Sanskrit", "Luxembourgish", "Myanmar", "Tibetan", "Tagalog", "Malagasy", "Assamese", "Tatar", "Hawaiian", "Lingala", "Hausa", "Bashkir", "Javanese", "Sundanese" ] LAN_SIMPLI={ 'afrikaans': 'af','albanian': 'sq','amharic': 'am','arabic': 'ar','armenian': 'hy', 'azerbaijani': 'az','basque': 'eu','belarusian': 'be','bengali': 'bn','bosnian': 'bs', 'bulgarian': 'bg','catalan': 'ca','cebuano': 'ceb','chichewa': 'ny','chinese': 'zh', 'corsican': 'co','croatian': 'hr','czech': 'cs','danish': 'da','dutch': 'nl','english': 'en', 'esperanto': 'eo','estonian': 'et','filipino': 'tl','finnish': 'fi','french': 'fr','frisian': 'fy', 'galician': 'gl','georgian': 'ka','german': 'de','greek': 'el','gujarati': 'gu','haitian creole': 'ht', 'hausa': 'ha','hawaiian': 'haw','hebrew': 'iw','hindi': 'hi','hmong': 'hmn','hungarian': 'hu','icelandic': 'is', 'igbo': 'ig','indonesian': 'id','irish': 'ga','italian': 'it','japanese': 'ja','javanese': 'jw','kannada': 'kn', 'kazakh': 'kk','khmer': 'km','korean': 'ko','kurdish (kurmanji)': 'ku','kyrgyz': 'ky','lao': 'lo','latin': 'la', 'latvian': 'lv','lithuanian': 'lt','luxembourgish': 'lb','macedonian': 'mk','malagasy': 'mg','malay': 'ms', 'malayalam': 'ml','maltese': 'mt','maori': 'mi','marathi': 'mr','mongolian': 'mn','myanmar (burmese)': 'my', 'nepali': 'ne','norwegian': 'no','pashto': 'ps','persian': 'fa','polish': 'pl','portuguese': 'pt','punjabi': 'pa', 'romanian': 'ro','russian': 'ru','samoan': 'sm','scots gaelic': 'gd','serbian': 'sr','sesotho': 'st','shona': 'sn', 'sindhi': 'sd','sinhala': 'si','slovak': 'sk','slovenian': 'sl','somali': 'so','spanish': 'es','sundanese': 'su', 'swahili': 'sw','swedish': 'sv','tajik': 'tg','tamil': 'ta','telugu': 'te','thai': 'th','turkish': 'tr','ukrainian': 'uk', 'urdu': 'ur','uzbek': 'uz','vietnamese': 'vi','welsh': 'cy','xhosa': 'xh','yiddish': 'yi','yoruba': 'yo','zulu': 'zu', 'Filipino': 'fil','Hebrew': 'he',} WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"] class WhisperTranscriber: def __init__(self, input_audio_max_duration: float = DEFAULT_INPUT_AUDIO_MAX_DURATION, vad_process_timeout: float = None, vad_cpu_cores: int = 1, delete_uploaded_files: bool = DELETE_UPLOADED_FILES, output_dir: str = 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 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, vadPadding, vadPromptWindow): return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow) # Entry function for the full tab def transcribe_webui_full(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, 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): # 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] return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, 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) def transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, **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 = WhisperContainer(model_name=selectedModel, cache=self.model_cache) # 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 # Execute whisper for source in sources: source_prefix = "" if (len(sources) > 1): # Prefix (minimum 2 digits) source_index += 1 source_prefix = str(source_index).zfill(2) + "_" print("Transcribing ", source.source_path) # Transcribe result = self.transcribe_file(model, source.source_path, selectedLanguage, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, **decodeOptions) translated_segs=[] lan_text = result['language'] can_trans = True if lan_text != '': if len(lan_text)!=2: try: lan_text = LAN_SIMPLI[lan_text] print('语言改简写',result['language'],lan_text) except: print('不能翻译这种语言',lan_text) can_trans = False else: if not lan_text in LAN_SIMPLI.values(): can_trans = False if lan_text!='' and can_trans == True: text='' text_trans=[] #翻译 if lan_text == 'zh': lan_text = 'zh-tw' for i,x in enumerate(result['segments']): text += x['text'] + '\n' if i>0 and i%50 == 0 : trans = requests.post("https://hf.space/embed/mikeee/gradio-gtr/+/api/predict", json={"data": [text, lan_text, "zh"]}).json()["data"][0] trans = str(trans).split('\n') text_trans.extend(trans) text='' print(int(i/50),'次翻译') if text !='': trans = requests.post("https://hf.space/embed/mikeee/gradio-gtr/+/api/predict", json={"data": [text, lan_text, "zh"]}).json()["data"][0] trans = str(trans).split('\n') text_trans.extend(trans) print('最后一次翻译') from copy import deepcopy translated_segs=deepcopy(result['segments']) #嵌套列表，.copy()不能深拷贝内部列表的东西。 print('原文') print(translated_segs) print('翻译') print(text_trans) print(len(text_trans),len(translated_segs)) try: for i in range(len(translated_segs)): translated_segs[i]['text'] = text_trans[i] except: print('翻译错误') filePrefix = slugify(source_prefix + source.get_short_name(), allow_unicode=True) source_download, source_text, source_vtt = self.write_result(result,translated_segs, filePrefix, outputDirectory) 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: WhisperContainer, audio_path: str, language: str, task: str = None, vad: str = None, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1, **decodeOptions: dict): initial_prompt = decodeOptions.pop('initial_prompt', None) if ('task' in decodeOptions): task = decodeOptions.pop('task') # Callable for processing an audio file whisperCallable = model.create_callback(language, task, initial_prompt, **decodeOptions) # The results if (vad == 'silero-vad'): # Silero VAD where non-speech gaps are transcribed process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow) result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps) elif (vad == 'silero-vad-skip-gaps'): # Silero VAD where non-speech gaps are simply ignored skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow) result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps) elif (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, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow) result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps) elif (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=vadMaxMergeSize, max_prompt_window=vadPromptWindow) result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config) 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) else: # Default VAD result = whisperCallable.invoke(audio_path, 0, None, None) return result def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig): 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) 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) 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, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1): # Use Silero VAD if (self.vad_model is None): self.vad_model = VadSileroTranscription() config = TranscriptionConfig(non_speech_strategy = non_speech_strategy, max_silent_period=vadMergeWindow, max_merge_size=vadMaxMergeSize, segment_padding_left=vadPadding, segment_padding_right=vadPadding, max_prompt_window=vadPromptWindow) return config def write_result(self, result: dict, translated_segs ,source_name: str, output_dir: str): 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) srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth) if len(translated_segs) != 0: translated_srt = self.__get_subs(translated_segs, "srt", languageMaxLineWidth) output_files = [] output_files.append(self.__create_file(srt, output_dir, source_name + "-subs.srt")); if len(translated_segs) != 0: output_files.append(self.__create_file(translated_srt, output_dir, source_name + "-subs-translated.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")); 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) -> str: segmentStream = StringIO() if format == 'vtt': write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth) elif format == 'srt': write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth) 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(input_audio_max_duration, share=False, server_name: str = None, server_port: int = 7860, default_model_name: str = "large-v2", default_vad: str = None, vad_parallel_devices: str = None, vad_process_timeout: float = None, vad_cpu_cores: int = 1, auto_parallel: bool = False, output_dir: str = None): ui = WhisperTranscriber(input_audio_max_duration, vad_process_timeout, vad_cpu_cores, DELETE_UPLOADED_FILES, output_dir) # Specify a list of devices to use for parallel processing ui.set_parallel_devices(vad_parallel_devices) ui.set_auto_parallel(auto_parallel) ui_description = "Whisper 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." if input_audio_max_duration > 0: ui_description += "\n\n" + "Max audio file length: " + str(input_audio_max_duration) + " s" ui_article = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)" simple_inputs = lambda : [ gr.Dropdown(choices=WHISPER_MODELS, value=default_model_name, label="Model"), gr.Dropdown(choices=sorted(LANGUAGES), label="Language"), gr.Text(label="URL (YouTube, etc.)"), gr.Text(label="File",value='/content/drive/MyDrive/videos/'), #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="transcribe"), gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=default_vad, label="VAD"), gr.Number(label="VAD - Merge Window (s)", precision=0, value=5), gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=30), gr.Number(label="VAD - Padding (s)", precision=None, value=1), gr.Number(label="VAD - Prompt Window (s)", precision=None, value=3) ] simple_transcribe = gr.Interface(fn=ui.transcribe_webui_simple, description=ui_description, article=ui_article, inputs=simple_inputs(), outputs=[ gr.File(label="Download"), gr.Text(label="Transcription"), gr.Text(label="Segments") ]) 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, description=full_description, article=ui_article, inputs=[ *simple_inputs(), gr.TextArea(label="Initial Prompt"), gr.Number(label="Temperature", value=0), gr.Number(label="Best Of - Non-zero temperature", value=5, precision=0), gr.Number(label="Beam Size - Zero temperature", value=5, precision=0), gr.Number(label="Patience - Zero temperature", value=None), gr.Number(label="Length Penalty - Any temperature", value=None), gr.Text(label="Suppress Tokens - Comma-separated list of token IDs", value="-1"), gr.Checkbox(label="Condition on previous text", value=True), gr.Checkbox(label="FP16", value=True), gr.Number(label="Temperature increment on fallback", value=0.2), gr.Number(label="Compression ratio threshold", value=2.4), gr.Number(label="Logprob threshold", value=-1.0), gr.Number(label="No speech threshold", value=0.6) ], outputs=[ gr.File(label="Download"), gr.Text(label="Transcription"), gr.Text(label="Segments") ]) demo = gr.TabbedInterface([simple_transcribe, full_transcribe], tab_names=["Simple", "Full"]) demo.launch(share=share, server_name=server_name, server_port=server_port) # Clean up ui.close() if __name__ == '__main__': parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--input_audio_max_duration", type=int, default=DEFAULT_INPUT_AUDIO_MAX_DURATION, help="Maximum audio file length in seconds, or -1 for no limit.") parser.add_argument("--share", type=bool, default=False, help="True to share the app on HuggingFace.") parser.add_argument("--server_name", type=str, default=None, help="The host or IP to bind to. If None, bind to localhost.") parser.add_argument("--server_port", type=int, default=7860, help="The port to bind to.") parser.add_argument("--default_model_name", type=str, choices=WHISPER_MODELS, default="large-v2", help="The default model name.") parser.add_argument("--default_vad", type=str, default="silero-vad", help="The default VAD.") parser.add_argument("--vad_parallel_devices", type=str, default="", 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=1, help="The number of CPU cores to use for VAD pre-processing.") parser.add_argument("--vad_process_timeout", type=float, default="1800", 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=False, 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.") parser.add_argument("--output_dir", "-o", type=str, default=None, help="directory to save the outputs") args = parser.parse_args().__dict__ create_ui(**args)