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| from datetime import datetime | |
| import json | |
| import math | |
| from typing import 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.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.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, 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), | |
| ] | |
| 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") | |
| ]) | |
| 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"), | |
| gr.Text(label="Segments") | |
| ]) | |
| demo = gr.TabbedInterface([simple_transcribe, full_transcribe], tab_names=["Simple", "Full"]) | |
| # 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") | |
| 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) |