Create app.py

app.py

@@ -0,0 +1,80 @@
+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()