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| from io import StringIO | |
| import gradio as gr | |
| from utils import write_vtt | |
| import whisper | |
| #import os | |
| #os.system("pip install git+https://github.com/openai/whisper.git") | |
| 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" | |
| 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() | |
| demo = gr.Interface(fn=greet, description="Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.", 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() |