Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -1,65 +1,92 @@
|
|
1 |
-
import gradio as gr
|
2 |
-
import os
|
3 |
-
import sys
|
4 |
-
import subprocess
|
5 |
-
#from moviepy.editor import VideoFileClip
|
6 |
-
|
7 |
import whisper
|
8 |
from whisper.utils import write_vtt
|
9 |
|
10 |
-
|
11 |
-
|
12 |
-
|
13 |
-
|
14 |
-
def video2mp3(video_file, output_ext="mp3"):
|
15 |
-
filename, ext = os.path.splitext(video_file)
|
16 |
-
subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"],
|
17 |
-
stdout=subprocess.DEVNULL,
|
18 |
-
stderr=subprocess.STDOUT)
|
19 |
-
return f"{filename}.{output_ext}"
|
20 |
-
|
21 |
-
|
22 |
-
def translate(input_video):
|
23 |
-
|
24 |
-
audio_file = video2mp3(input_video)
|
25 |
-
|
26 |
-
options = dict(beam_size=5, best_of=5, fp16 = False)
|
27 |
-
translate_options = dict(task="translate", **options)
|
28 |
-
result = model.transcribe(audio_file,**translate_options)
|
29 |
-
|
30 |
-
output_dir = ''
|
31 |
-
audio_path = audio_file.split(".")[0]
|
32 |
-
|
33 |
-
with open(os.path.join(output_dir, audio_path + ".vtt"), "w") as vtt:
|
34 |
-
write_vtt(result["segments"], file=vtt)
|
35 |
-
|
36 |
-
subtitle = audio_path + ".vtt"
|
37 |
-
output_video = audio_path + "_subtitled.mp4"
|
38 |
-
|
39 |
-
os.system(f"ffmpeg -i {input_video} -vf subtitles={subtitle} {output_video}")
|
40 |
-
|
41 |
-
return output_video
|
42 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
43 |
block = gr.Blocks()
|
44 |
-
with block:
|
45 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
46 |
with gr.Group():
|
47 |
with gr.Box():
|
48 |
-
|
49 |
-
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
-
|
57 |
-
|
58 |
-
|
59 |
-
|
60 |
-
|
61 |
-
|
62 |
-
|
63 |
-
|
64 |
-
|
65 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
|
|
|
|
|
|
|
|
|
|
2 |
import whisper
|
3 |
from whisper.utils import write_vtt
|
4 |
|
5 |
+
from pytube import YouTube
|
6 |
+
import os
|
7 |
+
import sys
|
8 |
+
import subprocess
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
9 |
|
10 |
+
loaded_model = whisper.load_model("base")
|
11 |
+
current_size = 'base'
|
12 |
+
|
13 |
+
def inference(link):
|
14 |
+
|
15 |
+
yt = YouTube(link)
|
16 |
+
|
17 |
+
audio_path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
|
18 |
+
print(f'audio path : {audio_path}')
|
19 |
+
video_path = yt.streams.filter(file_extension='mp4')
|
20 |
+
|
21 |
+
#options = whisper.DecodingOptions(without_timestamps=True)
|
22 |
+
options = dict(beam_size=5, best_of=5, fp16 = False)
|
23 |
+
translate_options = dict(task="inference", **options)
|
24 |
+
results = loaded_model.transcribe(audio_path,**translate_options)
|
25 |
+
|
26 |
+
#output_dir = ''
|
27 |
+
path = audio_path.split(".")[0]
|
28 |
+
print(path)
|
29 |
+
|
30 |
+
with open(path + ".vtt", "w") as vtt:
|
31 |
+
write_vtt(results["segments"], file=vtt)
|
32 |
+
|
33 |
+
subtitle = path + ".vtt"
|
34 |
+
output_video = path + "_subtitled.mp4"
|
35 |
+
|
36 |
+
os.system(f"ffmpeg -i {video_path} -vf subtitles={subtitle} {output_video}")
|
37 |
+
|
38 |
+
return output_video
|
39 |
+
|
40 |
+
def change_model(size):
|
41 |
+
if size == current_size:
|
42 |
+
return
|
43 |
+
loaded_model = whisper.load_model(size)
|
44 |
+
current_size = size
|
45 |
+
|
46 |
+
def populate_metadata(link):
|
47 |
+
yt = YouTube(link)
|
48 |
+
return yt.thumbnail_url, yt.title
|
49 |
+
|
50 |
+
title="Youtube Caption Generator"
|
51 |
+
description="Generate captions of Youtube videos using OpenAI's Whisper"
|
52 |
block = gr.Blocks()
|
|
|
53 |
|
54 |
+
with block:
|
55 |
+
gr.HTML(
|
56 |
+
"""
|
57 |
+
<div style="text-align: center; max-width: 500px; margin: 0 auto;">
|
58 |
+
<div>
|
59 |
+
<h1>Youtube Caption Generator</h1>
|
60 |
+
</div>
|
61 |
+
<p style="margin-bottom: 10px; font-size: 94%">
|
62 |
+
Generate captions of Youtube videos using OpenAI's Whisper
|
63 |
+
</p>
|
64 |
+
</div>
|
65 |
+
"""
|
66 |
+
)
|
67 |
with gr.Group():
|
68 |
with gr.Box():
|
69 |
+
sz = gr.Dropdown(label="Model Size", choices=['base','small', 'medium', 'large'], value='base')
|
70 |
+
|
71 |
+
link = gr.Textbox(label="YouTube Link")
|
72 |
+
|
73 |
+
with gr.Row().style(mobile_collapse=False, equal_height=True):
|
74 |
+
title = gr.Label(label="Video Title", placeholder="Title")
|
75 |
+
img = gr.Image(label="Thumbnail")
|
76 |
+
|
77 |
+
# text = gr.Textbox(
|
78 |
+
# label="Transcription",
|
79 |
+
# placeholder="Transcription Output",
|
80 |
+
# lines=5)
|
81 |
+
|
82 |
+
op_video = gr.Video()
|
83 |
+
|
84 |
+
with gr.Row().style(mobile_collapse=False, equal_height=True):
|
85 |
+
btn = gr.Button("Generate Captions")
|
86 |
+
|
87 |
+
# Events
|
88 |
+
btn.click(inference, inputs=[link], outputs=[op_video])
|
89 |
+
link.change(populate_metadata, inputs=[link], outputs=[img, title])
|
90 |
+
sz.change(change_model, inputs=[sz], outputs=[])
|
91 |
+
|
92 |
+
block.launch(debug=True,enable_queue=True)
|