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from io import StringIO |
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import gradio as gr |
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from utils import write_vtt |
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import whisper |
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LANGUAGES = [ |
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"English", |
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"Chinese", |
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"German", |
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"Spanish", |
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"Russian", |
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"Korean", |
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"French", |
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"Japanese", |
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"Portuguese", |
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"Turkish", |
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"Polish", |
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"Catalan", |
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"Dutch", |
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"Arabic", |
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"Swedish", |
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"Italian", |
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"Indonesian", |
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"Hindi", |
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"Finnish", |
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"Vietnamese", |
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"Hebrew", |
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"Ukrainian", |
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"Greek", |
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"Malay", |
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"Czech", |
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"Romanian", |
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"Danish", |
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"Hungarian", |
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"Tamil", |
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"Norwegian", |
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"Thai", |
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"Urdu", |
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"Croatian", |
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"Bulgarian", |
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"Lithuanian", |
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"Latin", |
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"Maori", |
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"Malayalam", |
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"Welsh", |
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"Slovak", |
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"Telugu", |
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"Persian", |
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"Latvian", |
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"Bengali", |
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"Serbian", |
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"Azerbaijani", |
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"Slovenian", |
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"Kannada", |
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"Estonian", |
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"Macedonian", |
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"Breton", |
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"Basque", |
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"Icelandic", |
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"Armenian", |
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"Nepali", |
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"Mongolian", |
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"Bosnian", |
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"Kazakh", |
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"Albanian", |
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"Swahili", |
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"Galician", |
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"Marathi", |
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"Punjabi", |
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"Sinhala", |
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"Khmer", |
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"Shona", |
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"Yoruba", |
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"Somali", |
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"Afrikaans", |
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"Occitan", |
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"Georgian", |
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"Belarusian", |
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"Tajik", |
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"Sindhi", |
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"Gujarati", |
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"Amharic", |
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"Yiddish", |
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"Lao", |
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"Uzbek", |
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"Faroese", |
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"Haitian Creole", |
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"Pashto", |
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"Turkmen", |
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"Nynorsk", |
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"Maltese", |
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"Sanskrit", |
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"Luxembourgish", |
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"Myanmar", |
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"Tibetan", |
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"Tagalog", |
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"Malagasy", |
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"Assamese", |
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"Tatar", |
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"Hawaiian", |
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"Lingala", |
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"Hausa", |
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"Bashkir", |
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"Javanese", |
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"Sundanese" |
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] |
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model_cache = dict() |
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def greet(modelName, languageName, uploadFile, microphoneData, task): |
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source = uploadFile if uploadFile is not None else microphoneData |
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selectedLanguage = languageName.lower() if len(languageName) > 0 else None |
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selectedModel = modelName if modelName is not None else "base" |
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model = model_cache.get(selectedModel, None) |
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if not model: |
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model = whisper.load_model(selectedModel) |
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model_cache[selectedModel] = model |
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result = model.transcribe(source, language=selectedLanguage, task=task) |
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segmentStream = StringIO() |
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write_vtt(result["segments"], file=segmentStream) |
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segmentStream.seek(0) |
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return result["text"], segmentStream.read() |
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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=[ |
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gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", label="Model"), |
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gr.Dropdown(choices=sorted(LANGUAGES), label="Language"), |
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gr.Audio(source="upload", type="filepath", label="Upload Audio"), |
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gr.Audio(source="microphone", type="filepath", label="Microphone Input"), |
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gr.Dropdown(choices=["transcribe", "translate"], label="Task"), |
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], outputs=[gr.Text(label="Transcription"), gr.Text(label="Segments")]) |
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demo.launch() |
