import gradio as gr
from transformers import pipeline
import cv2
from PIL import Image
import io
import scipy
Afficher plus
app.py
3 Ko

import gradio as gr
from transformers import pipeline
import cv2
from PIL import Image
import io
import scipy
import torch
import time



def video_to_descriptions(video, target_language="en"):
    
    start_time = time.time()
    print("START TIME = ", start_time)

    ImgToText = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
    Summarize = pipeline("summarization", model="tuner007/pegasus_summarizer")
    translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{target_language}")
    audio = pipeline("text-to-speech", model="suno/bark-small")

    voice_preset = f"v2/{target_language}_speaker_1"

    cap = cv2.VideoCapture(video)
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    
    descriptions = []
    frame_count = 0
    
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        

        if frame_count % (fps * 2) == 0:

            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

            pil_img = Image.fromarray(frame_rgb)

            outputs = ImgToText(pil_img)
            description = outputs[0]['generated_text']
            descriptions.append(description)
            print(str(frame_count) + " : " + outputs[0]['generated_text'])
        
        frame_count += 1

    cap.release()

    concatenated_description = " ".join(descriptions)
    summarized_description = Summarize(concatenated_description, max_length=31)[0]["summary_text"]
    print("SUMMARIZATION : " + summarized_description)

    translated_text = translator(summarized_description)[0]["translation_text"]
    print("TRANSLATION : " + translated_text)
    
    audio_file = audio(translated_text)

    output_path = "./bark_out.wav"
    scipy.io.wavfile.write(output_path, data=audio_file["audio"][0], rate=audio_file["sampling_rate"])

    stop_time = time.time()

    print("EXECUTION TIME = ", stop_time - start_time)
    return output_path

language_dropdown = gr.Dropdown(
            ["en", "fr", "de", "es"], label="[MANDATORY] Language", info="The Voice's Language"
        )

iface = gr.Interface(
    fn=video_to_descriptions,
    inputs=[gr.Video(label="Video to Upload", info="The Video"), language_dropdown],
    outputs="audio",
    live=False
)

if __name__ == "__main__":
    iface.launch()