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, download_url from src.utils import slugify, write_srt, write_vtt from src.vad import NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, 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" ] class WhisperTranscriber: def __init__(self, inputAudioMaxDuration: float = DEFAULT_INPUT_AUDIO_MAX_DURATION, deleteUploadedFiles: bool = DELETE_UPLOADED_FILES): self.model_cache = dict() self.vad_model = None self.inputAudioMaxDuration = inputAudioMaxDuration self.deleteUploadedFiles = deleteUploadedFiles 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 = self.model_cache.get(selectedModel, None) if not model: model = whisper.load_model(selectedModel) self.model_cache[selectedModel] = model # 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: whisper.Whisper, 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 = lambda audio, segment_index, prompt, detected_language : model.transcribe(audio, \ language=language if language else detected_language, task=task, \ initial_prompt=self._concat_prompt(initial_prompt, prompt) if segment_index == 0 else 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.vad_model.transcribe(audio_path, whisperCallable, 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.vad_model.transcribe(audio_path, whisperCallable, 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.vad_model.transcribe(audio_path, whisperCallable, 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() result = periodic_vad.transcribe(audio_path, whisperCallable, PeriodicTranscriptionConfig(periodic_duration=vadMaxMergeSize, max_prompt_window=vadPromptWindow)) else: # Default VAD result = whisperCallable(audio_path, 0, None, None) return result 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 = dict() 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 create_ui(inputAudioMaxDuration, share=False, server_name: str = None): ui = WhisperTranscriber(inputAudioMaxDuration) ui_description = "Whisper是一个语音转文字模型，经过多个语音数据集的训练而成。也可以进行多语言的识别任务和翻译（多种语言翻译成英文）" ui_description += "\n\n\n\n对于时长大于10分钟的非英语音频文件，建议选择VAD选项中的Silero VAD (语音活动检测器)。" if inputAudioMaxDuration > 0: ui_description += "\n\n" + "Max audio file length: " + str(inputAudioMaxDuration) + " s" demo = gr.Interface(fn=ui.transcribe_webui, description=ui_description, 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", "silero-vad-expand-into-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=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) if __name__ == '__main__': create_ui(DEFAULT_INPUT_AUDIO_MAX_DURATION)