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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 = 300 # 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) |