from datetime import datetime import json import math from typing import Callable, Iterator, Union import argparse 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.diarization.diarization import Diarization from src.diarization.diarizationContainer import DiarizationContainer from src.diarization.transcriptLoader import load_transcript from src.hooks.progressListener import ProgressListener from src.hooks.subTaskProgressListener import SubTaskProgressListener 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 AudioSource, 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, str2bool, write_srt, write_vtt 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 # Support for diarization self.diarization: DiarizationContainer = None # Dictionary with parameters to pass to diarization.run - if None, diarization is not enabled self.diarization_kwargs = None 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.") def set_diarization(self, auth_token: str, enable_daemon_process: bool = True, **kwargs): if self.diarization is None: self.diarization = DiarizationContainer(auth_token=auth_token, enable_daemon_process=enable_daemon_process, auto_cleanup_timeout_seconds=self.app_config.diarization_process_timeout, cache=self.model_cache) # Set parameters self.diarization_kwargs = kwargs def unset_diarization(self): if self.diarization is not None: self.diarization.cleanup() self.diarization_kwargs = None # 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, diarization: bool = False, diarization_speakers: int = 2): return self.transcribe_webui_simple_progress(modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, word_timestamps, highlight_words, diarization, diarization_speakers) # 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, diarization: bool = False, diarization_speakers: int = 2, 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) if diarization: self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers) else: self.unset_diarization() 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, diarization: bool = False, diarization_speakers: int = 2, diarization_min_speakers = 1, diarization_max_speakers = 5): 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, diarization, diarization_speakers, diarization_min_speakers, diarization_max_speakers) # 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, diarization: bool = False, diarization_speakers: int = 2, diarization_min_speakers = 1, diarization_max_speakers = 5, 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) # Set diarization if diarization: self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers, min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers) else: self.unset_diarization() 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) # Perform diarization given a specific input audio file and whisper file def perform_extra(self, languageName, urlData, singleFile, whisper_file: str, highlight_words: bool = False, diarization: bool = False, diarization_speakers: int = 2, diarization_min_speakers = 1, diarization_max_speakers = 5, progress=gr.Progress()): if whisper_file is None: raise ValueError("whisper_file is required") # Set diarization if diarization: self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers, min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers) else: self.unset_diarization() def custom_transcribe_file(source: AudioSource): result = load_transcript(whisper_file.name) # Set language if not set if not "language" in result: result["language"] = languageName # Mark speakers result = self._handle_diarization(source.source_path, result) return result multipleFiles = [singleFile] if singleFile else None # Will return download, text, vtt return self.transcribe_webui("base", "", urlData, multipleFiles, None, None, None, progress=progress,highlight_words=highlight_words, override_transcribe_file=custom_transcribe_file, override_max_sources=1) def transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions: VadOptions, progress: gr.Progress = None, highlight_words: bool = False, override_transcribe_file: Callable[[AudioSource], dict] = None, override_max_sources = None, **decodeOptions: dict): try: sources = self.__get_source(urlData, multipleFiles, microphoneData) if override_max_sources is not None and len(sources) > override_max_sources: raise ValueError("Maximum number of sources is " + str(override_max_sources) + ", but " + str(len(sources)) + " were provided") try: selectedLanguage = languageName.lower() if len(languageName) > 0 else None selectedModel = modelName if modelName is not None else "base" if override_transcribe_file is None: 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) else: model = None # 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 using the override function if specified if override_transcribe_file is None: result = self.transcribe_file(model, source.source_path, selectedLanguage, task, vadOptions, scaled_progress_listener, **decodeOptions) else: result = override_transcribe_file(source) 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) # Diarization result = self._handle_diarization(audio_path, result) return result def _handle_diarization(self, audio_path: str, input: dict): if self.diarization and self.diarization_kwargs: print("Diarizing ", audio_path) diarization_result = list(self.diarization.run(audio_path, **self.diarization_kwargs)) # Print result print("Diarization result: ") for entry in diarization_result: print(f" start={entry.start:.1f}s stop={entry.end:.1f}s speaker_{entry.speaker}") # Add speakers to result input = self.diarization.mark_speakers(diarization_result, input) return input 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"] if "language" in result else None languageMaxLineWidth = self.__get_max_line_width(language) # We always create the JSON file for debugging purposes json_result = json.dumps(result, indent=4, ensure_ascii=False) json_file = self.__create_file(json_result, output_dir, source_name + "-result.json") print("Created JSON file " + json_file) 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) 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(json_file) 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() # Cleanup diarization if (self.diarization is not None): self.diarization.cleanup() self.diarization = None 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, 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), ] has_diarization_libs = Diarization.has_libraries() if not has_diarization_libs: print("Diarization libraries not found - disabling diarization") app_config.diarization = False common_diarization_inputs = lambda : [ gr.Checkbox(label="Diarization", value=app_config.diarization, interactive=has_diarization_libs), gr.Number(label="Diarization - Speakers", precision=0, value=app_config.diarization_speakers, interactive=has_diarization_libs) ] 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(), *common_diarization_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_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), *common_diarization_inputs(), gr.Number(label="Diarization - Min Speakers", precision=0, value=app_config.diarization_min_speakers, interactive=has_diarization_libs), gr.Number(label="Diarization - Max Speakers", precision=0, value=app_config.diarization_max_speakers, interactive=has_diarization_libs), ], outputs=[ gr.File(label="Download"), gr.Text(label="Transcription"), gr.Text(label="Segments") ]) perform_extra_interface = gr.Interface(fn=ui.perform_extra, description="Perform additional processing on a given JSON or SRT file", article=ui_article, inputs=[ gr.Dropdown(choices=sorted(get_language_names()), label="Language", value=app_config.language), gr.Text(label="URL (YouTube, etc.)"), gr.File(label="Upload Audio File", file_count="single"), gr.File(label="Upload JSON/SRT File", file_count="single"), gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words), *common_diarization_inputs(), gr.Number(label="Diarization - Min Speakers", precision=0, value=app_config.diarization_min_speakers, interactive=has_diarization_libs), gr.Number(label="Diarization - Max Speakers", precision=0, value=app_config.diarization_max_speakers, interactive=has_diarization_libs), ], outputs=[ gr.File(label="Download"), gr.Text(label="Transcription"), gr.Text(label="Segments") ]) demo = gr.TabbedInterface([simple_transcribe, full_transcribe, perform_extra_interface], tab_names=["Simple", "Full", "Extra"]) # 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.") # 1800 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") parser.add_argument('--auth_token', type=str, default=default_app_config.auth_token, help='HuggingFace API Token (optional)') parser.add_argument("--diarization", type=str2bool, default=default_app_config.diarization, \ help="whether to perform speaker diarization") parser.add_argument("--diarization_num_speakers", type=int, default=default_app_config.diarization_speakers, help="Number of speakers") parser.add_argument("--diarization_min_speakers", type=int, default=default_app_config.diarization_min_speakers, help="Minimum number of speakers") parser.add_argument("--diarization_max_speakers", type=int, default=default_app_config.diarization_max_speakers, help="Maximum number of speakers") parser.add_argument("--diarization_process_timeout", type=int, default=default_app_config.diarization_process_timeout, \ help="Number of seconds before inactivate diarization processes are terminated. Use 0 to close processes immediately, or None for no timeout.") args = parser.parse_args().__dict__ updated_config = default_app_config.update(**args) if (threads := args.pop("threads")) > 0: torch.set_num_threads(threads) print("Using whisper implementation: " + updated_config.whisper_implementation) create_ui(app_config=updated_config)