app.py

import torch
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import soundfile as sf
import streamlit as st

# Load the processor and model
processor = Wav2Vec2Processor.from_pretrained("openbmb/MiniCPM-o-2_6")
model = Wav2Vec2ForCTC.from_pretrained("openbmb/MiniCPM-o-2_6")

def transcribe_audio(file_path):
    # Load audio file
    audio_input, sample_rate = sf.read(file_path)
    
    # Preprocess the audio
    input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
    
    # Perform inference
    with torch.no_grad():
        logits = model(input_values).logits
    
    # Decode the logits to text
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)
    
    return transcription[0]

uploaded_file = st.file_uploader("Upload an audio", type=["mp3", "wav"])

if uploaded_file is not None:
     transcription = transcribe_audio(uploaded_file)
     st.write(transcription)
    
# if __name__ == "__main__":
#     audio_file_path = "CAR0005.mp3"
#     transcription = transcribe_audio(audio_file_path)
#     print("Transcription:", transcription)