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 download import downloadUrl from utils import slugify, write_srt, write_vtt #import os #os.system("pip install git+https://github.com/openai/whisper.git") # Limitations (set to -1 to disable) DEFAULT_INPUT_AUDIO_MAX_DURATION = 600 # seconds 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.inputAudioMaxDuration = inputAudioMaxDuration def transcribeFile(self, modelName, languageName, urlData, uploadFile, microphoneData, task): source, sourceName = getSource(urlData, uploadFile, microphoneData) selectedLanguage = languageName.lower() if len(languageName) > 0 else None selectedModel = modelName if modelName is not None else "base" if self.inputAudioMaxDuration > 0: # Calculate audio length audioDuration = ffmpeg.probe(source)["format"]["duration"] if float(audioDuration) > self.inputAudioMaxDuration: return ("[ERROR]: Maximum audio file length is " + str(self.inputAudioMaxDuration) + "s, file was " + str(audioDuration) + "s"), "[ERROR]" model = model_cache.get(selectedModel, None) if not model: model = whisper.load_model(selectedModel) model_cache[selectedModel] = model # The results result = model.transcribe(source, language=selectedLanguage, task=task) text = result["text"] vtt = getSubs(result["segments"], "vtt") srt = getSubs(result["segments"], "srt") # 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 def getSource(urlData, uploadFile, microphoneData): if urlData: # Download from YouTube source = downloadUrl(urlData) else: # File input source = uploadFile if uploadFile is not None else microphoneData file_path = pathlib.Path(source) sourceName = file_path.stem[:18] + file_path.suffix return source, sourceName 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) -> str: segmentStream = StringIO() if format == 'vtt': write_vtt(segments, file=segmentStream) elif format == 'srt': write_srt(segments, file=segmentStream) else: raise Exception("Unknown format " + format) segmentStream.seek(0) return segmentStream.read() def createUi(inputAudioMaxDuration, share=False): 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. " if inputAudioMaxDuration > 0: ui_description += "\n\n" + "Max audio file length: " + str(inputAudioMaxDuration) + " s" demo = gr.Interface(fn=ui.transcribeFile, 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"), ], outputs=[ gr.File(label="Download"), gr.Text(label="Transcription"), gr.Text(label="Segments") ]) demo.launch(share=share) if __name__ == '__main__': createUi(DEFAULT_INPUT_AUDIO_MAX_DURATION)