app.py

import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer

# Create a PretrainedTokenizer object
tokenizer = AutoTokenizer.from_pretrained('wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut')

# Pass the PretrainedTokenizer object as the tokenizer argument
p = pipeline('text-generation', model='wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut', tokenizer=tokenizer)

def transcribe(audio):
    # Pass the audio file to the pipeline and obtain the output
    text = p(audio)["generated_text"]
    return text
    
# Create a GraudioInput object for the audio input
audio_input = gr.Audio(source="microphone", type="filepath")

# Use the GraudioInput object as the value for the inputs argument
gr.Interface(
    fn=transcribe, 
    inputs=audio_input, 
    outputs="text").launch()