added app.py and requirements.txt

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+import os
+# import sys
+import subprocess
+
+import whisper
+from whisper.utils import write_vtt
+
+model = whisper.load_model("base")
+
+title = "Add multilingual text/caption to your video"
+
+
+def video2mp3(video_file, output_ext="mp3"):
+    filename, ext = os.path.splitext(video_file)
+    subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"],
+                    stdout=subprocess.DEVNULL,
+                    stderr=subprocess.STDOUT)
+    return f"{filename}.{output_ext}"
+
+
+def transcribe(input_video):
+    audio_file = video2mp3(input_video)
+
+    # options = dict(beam_size=5, best_of=5, fp16=False)
+    # translate_options = dict(task="translate", **options)
+    # result = model.transcribe(audio_file, **translate_options)
+
+    result = model.transcribe(audio_file)
+
+    output_dir = ''
+    audio_path = audio_file.split(".")[0]
+
+    with open(os.path.join(output_dir, audio_path + ".vtt"), "w") as vtt:
+        write_vtt(result["segments"], file=vtt)
+
+    subtitle = audio_path + ".vtt"
+    output_video = audio_path + "_subtitled.mp4"
+
+    os.system(f"ffmpeg -i {input_video} -vf subtitles={subtitle} {output_video}")
+
+    return output_video
+
+
+block = gr.Blocks()
+with block:
+    with gr.Group():
+        with gr.Box():
+            with gr.Row().style():
+                inp_video = gr.Video(
+                    label="Input Video",
+                    type="filepath",
+                    mirror_webcam=False
+                )
+                op_video = gr.Video()
+            btn = gr.Button("Generate Subtitle Video")
+
+        btn.click(transcribe, inputs=[inp_video], outputs=[op_video])
+
+block.launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,2 @@
+tensorflow
+git+https://github.com/openai/whisper.git