from io import StringIO import gradio as gr from utils import write_vtt import whisper import ffmpeg #import os #os.system("pip install git+https://github.com/openai/whisper.git") # Limitations (set to -1 to disable) INPUT_AUDIO_MAX_DURATION = 60 # 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() def greet(modelName, languageName, uploadFile, microphoneData, task): source = uploadFile if uploadFile is not None else microphoneData selectedLanguage = languageName.lower() if len(languageName) > 0 else None selectedModel = modelName if modelName is not None else "base" if INPUT_AUDIO_MAX_DURATION > 0: # Calculate audio length audioDuration = ffmpeg.probe(source)["format"]["duration"] if float(audioDuration) > INPUT_AUDIO_MAX_DURATION: return ("[ERROR]: Maximum audio file length is " + str(INPUT_AUDIO_MAX_DURATION) + "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 result = model.transcribe(source, language=selectedLanguage, task=task) segmentStream = StringIO() write_vtt(result["segments"], file=segmentStream) segmentStream.seek(0) return result["text"], segmentStream.read() 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 INPUT_AUDIO_MAX_DURATION > 0: ui_description += "\n\n" + "Max audio file length: " + str(INPUT_AUDIO_MAX_DURATION) + " s" demo = gr.Interface(fn=greet, description=ui_description, inputs=[ gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", label="Model"), gr.Dropdown(choices=sorted(LANGUAGES), label="Language"), 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.Text(label="Transcription"), gr.Text(label="Segments")]) demo.launch()