import math from typing import Iterator import argparse from io import StringIO import os import pathlib import tempfile from src.modelCache import ModelCache 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 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" ] 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 transcribe_webui(self, modelName, languageName, urlData, uploadFile, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow): try: source, sourceName = self.__get_source(urlData, uploadFile, 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) # Execute whisper result = self.transcribe_file(model, source, selectedLanguage, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow) # Write result downloadDirectory = tempfile.mkdtemp() filePrefix = slugify(sourceName, allow_unicode=True) download, text, vtt = self.write_result(result, filePrefix, downloadDirectory) return download, text, vtt finally: # Cleanup source if self.deleteUploadedFiles: print("Deleting source file " + source) os.remove(source) 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(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, uploadFile, microphoneData): if urlData: # Download from YouTube source = download_url(urlData, self.inputAudioMaxDuration)[0] else: # File input source = uploadFile if uploadFile is not None else microphoneData if self.inputAudioMaxDuration > 0: # Calculate audio length audioDuration = ffmpeg.probe(source)["format"]["duration"] if float(audioDuration) > self.inputAudioMaxDuration: raise ExceededMaximumDuration(videoDuration=audioDuration, maxDuration=self.inputAudioMaxDuration, message="Video is too long") file_path = pathlib.Path(source) sourceName = file_path.stem[:MAX_FILE_PREFIX_LENGTH] + file_path.suffix return source, sourceName 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): 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): 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_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://huggingface.co/spaces/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=["tiny", "base", "small", "medium", "large"], value=default_model_name, label="Model"), gr.Dropdown(choices=sorted(LANGUAGES), label="Language"), gr.Text(label="URL (YouTube, etc.)"), gr.Audio(source="upload", type="filepath", label="Upload Audio"), 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, 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.") args = parser.parse_args().__dict__ create_ui(**args)