minicpm_2 / app.py
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Update app.py
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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)