app.py

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 = 120 # 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 transcribeFile(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=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()