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| 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) | |
| DEFAULT_INPUT_AUDIO_MAX_DURATION = -1 # 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, 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 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 | |
| result = model.transcribe(source, language=selectedLanguage, task=task) | |
| segmentStream = StringIO() | |
| write_vtt(result["segments"], file=segmentStream) | |
| segmentStream.seek(0) | |
| return result["text"], 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.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(share=share) | |
| if __name__ == '__main__': | |
| createUi(DEFAULT_INPUT_AUDIO_MAX_DURATION) |