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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)
INPUT_AUDIO_MAX_DURATION = 60 # 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 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"
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=greet, 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() |