app.py

import warnings
import torch
import gradio as gr
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

warnings.filterwarnings("ignore")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model     = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

def transcribe_audio(audio_path):
    try:
        # 'sr' = 'sampling_rate' 
        audio, sr = librosa.load(audio_path, sr=16000)
        
        input_values = processor(audio, return_tensors='pt', sampling_rate=sr).input_values

        with torch.no_grad():
            logits = model(input_values).logits

        predicted_ids = torch.argmax(logits, dim=-1)
        transcriptions = processor.batch_decode(predicted_ids)[0]
        return transcriptions
    except Exception as e:
        return str(e)

demo = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(type='filepath'),
    outputs='text',
    title="Subtitle Generator",
    description="This tool transcribes audio files into text"
)
demo.launch()