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import gradio as gr
import torch
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, pipeline
import soundfile as sf
# Load models
# Transcription model for Moroccan Darija
processor = Wav2Vec2Processor.from_pretrained("achrafkhannoussi/Wav2Vec2-Large-XLSR-53-Moroccan-Darija")
transcription_model = Wav2Vec2ForCTC.from_pretrained("achrafkhannoussi/Wav2Vec2-Large-XLSR-53-Moroccan-Darija")
# Summarization model
summarizer = pipeline("summarization", model="t5-small")
# Function to transcribe audio using Wav2Vec2
def transcribe_audio(audio_path):
# Load and preprocess audio
audio_input, sample_rate = sf.read(audio_path)
inputs = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt", padding=True)
# Get predictions
with torch.no_grad():
logits = transcription_model(**inputs).logits
# Decode predictions
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
return transcription
# Function to transcribe and summarize
def transcribe_and_summarize(audio_file):
# Transcription
transcription = transcribe_audio(audio_file)
# Summarization
summary = summarizer(transcription, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
return transcription, summary
# Gradio Interface
inputs = gr.Audio(type="filepath", label="Upload your audio file")
outputs = [
gr.Textbox(label="Transcription"),
gr.Textbox(label="Summary")
]
app = gr.Interface(
fn=transcribe_and_summarize,
inputs=inputs,
outputs=outputs,
title="Moroccan Darija Audio Transcription and Summarization",
description="Upload an audio file in Moroccan Darija to get its transcription and a summarized version of the content."
)
# Launch the app
if __name__ == "__main__":
app.launch()
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