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| 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 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 | |
| # 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" | |
| ] | |
| 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): | |
| 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 | |
| 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 transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow): | |
| 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 | |
| # 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) | |
| filePrefix = slugify(source_prefix + source.get_short_name(), allow_unicode=True) | |
| source_download, source_text, source_vtt = self.write_result(result, filePrefix, downloadDirectory) | |
| 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, 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) | |
| 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")); | |
| 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 = "medium", default_vad: str = None, vad_parallel_devices: str = None, vad_process_timeout: float = None, vad_cpu_cores: int = 1, auto_parallel: bool = False): | |
| ui = WhisperTranscriber(input_audio_max_duration, vad_process_timeout, vad_cpu_cores) | |
| # 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)" | |
| demo = gr.Interface(fn=ui.transcribe_webui, description=ui_description, article=ui_article, inputs=[ | |
| 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.File(label="Upload Files", file_count="multiple"), | |
| gr.Audio(source="microphone", type="filepath", label="Microphone Input"), | |
| gr.Dropdown(choices=["transcribe", "translate"], label="Task"), | |
| 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) | |
| ], outputs=[ | |
| gr.File(label="Download"), | |
| gr.Text(label="Transcription"), | |
| gr.Text(label="Segments") | |
| ]) | |
| 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="medium", 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.") | |
| args = parser.parse_args().__dict__ | |
| create_ui(**args) |