import re from typing import Iterator from io import StringIO import os import pathlib import tempfile # External programs import whisper import ffmpeg # UI import gradio as gr from src.download import ExceededMaximumDuration, downloadUrl from src.utils import slugify, write_srt, write_vtt from src.vad import VadPeriodicTranscription, VadSileroTranscription # 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" ] model_cache = dict() class UI: def __init__(self, inputAudioMaxDuration): self.vad_model = None self.inputAudioMaxDuration = inputAudioMaxDuration def transcribeFile(self, modelName, languageName, urlData, uploadFile, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize): try: source, sourceName = self.getSource(urlData, uploadFile, microphoneData) try: selectedLanguage = languageName.lower() if len(languageName) > 0 else None selectedModel = modelName if modelName is not None else "base" model = model_cache.get(selectedModel, None) if not model: model = whisper.load_model(selectedModel) model_cache[selectedModel] = model # Callable for processing an audio file whisperCallable = lambda audio : model.transcribe(audio, language=selectedLanguage, task=task) # The results if (vad == 'silero-vad'): # Use Silero VAD and include gaps if (self.vad_model is None): self.vad_model = VadSileroTranscription() process_gaps = VadSileroTranscription(transcribe_non_speech = True, max_silent_period=vadMergeWindow, max_merge_size=vadMaxMergeSize, copy=self.vad_model) result = process_gaps.transcribe(source, whisperCallable) elif (vad == 'silero-vad-skip-gaps'): # Use Silero VAD if (self.vad_model is None): self.vad_model = VadSileroTranscription() skip_gaps = VadSileroTranscription(transcribe_non_speech = False, max_silent_period=vadMergeWindow, max_merge_size=vadMaxMergeSize, copy=self.vad_model) result = skip_gaps.transcribe(source, whisperCallable) 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(periodic_duration=vadMaxMergeSize) result = periodic_vad.transcribe(source, whisperCallable) else: # Default VAD result = whisperCallable(source) text = result["text"] language = result["language"] languageMaxLineWidth = getMaxLineWidth(language) print("Max line width " + str(languageMaxLineWidth)) vtt = getSubs(result["segments"], "vtt", languageMaxLineWidth) srt = getSubs(result["segments"], "srt", languageMaxLineWidth) # Files that can be downloaded downloadDirectory = tempfile.mkdtemp() filePrefix = slugify(sourceName, allow_unicode=True) download = [] download.append(createFile(srt, downloadDirectory, filePrefix + "-subs.srt")); download.append(createFile(vtt, downloadDirectory, filePrefix + "-subs.vtt")); download.append(createFile(text, downloadDirectory, filePrefix + "-transcript.txt")); return download, text, vtt finally: # Cleanup source if DELETE_UPLOADED_FILES: 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 getSource(self, urlData, uploadFile, microphoneData): if urlData: # Download from YouTube source = downloadUrl(urlData, self.inputAudioMaxDuration) 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 getMaxLineWidth(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 createFile(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 getSubs(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 createUi(inputAudioMaxDuration, share=False, server_name: str = None): ui = UI(inputAudioMaxDuration) 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), it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option." if inputAudioMaxDuration > 0: ui_description += "\n\n" + "Max audio file length: " + str(inputAudioMaxDuration) + " s" ui_article = "Read the [documentation her](https://huggingface.co/spaces/aadnk/whisper-webui/blob/main/docs/options.md)" demo = gr.Interface(fn=ui.transcribeFile, description=ui_description, article=ui_article, inputs=[ gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", 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", "periodic-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=150) ], outputs=[ gr.File(label="Download"), gr.Text(label="Transcription"), gr.Text(label="Segments") ]) demo.launch(share=share, server_name=server_name) if __name__ == '__main__': createUi(DEFAULT_INPUT_AUDIO_MAX_DURATION)