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.gitignore ADDED
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+ venv/
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+ ui/__pycache__/
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+ modules/__pycache__/
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+ modules/yt_tmp.wav
LICENSE ADDED
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app.py ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from modules.model_Inference import WhisperInference
3
+ from ui.htmls import CSS,MARKDOWN_TITLE,MARKDOWN_NOTE
4
+ from modules.youtube_manager import get_ytmetas
5
+
6
+ def on_change_models(model_size):
7
+ translatable_model = ["large","large-v1","large-v2"]
8
+ if model_size not in translatable_model:
9
+ return gr.Checkbox.update(visible=False,value=False,interactive=False)
10
+ else:
11
+ return gr.Checkbox.update(visible=True,value=False,label="Translate to English?",interactive=True)
12
+
13
+ whisper_inf = WhisperInference()
14
+ block = gr.Blocks(css=CSS).queue(api_open=False)
15
+
16
+ with block:
17
+ with gr.Row():
18
+ with gr.Column():
19
+ gr.Markdown(MARKDOWN_TITLE,elem_id="md_project")
20
+ gr.Markdown(MARKDOWN_NOTE,elem_id="md_note")
21
+ with gr.Tabs():
22
+ with gr.TabItem("File"): # tab1
23
+ with gr.Row():
24
+ input_file = gr.Files(type="file", label="Upload File here",file_types=["audio","video"])
25
+ with gr.Row():
26
+ dd_model = gr.Dropdown(choices=whisper_inf.available_models,value="tiny",label="Model")
27
+ dd_lang = gr.Dropdown(choices=["Automatic Detection"]+whisper_inf.available_langs,value="Automatic Detection",label="Language")
28
+ dd_subformat = gr.Dropdown(["SRT","WebVTT"],value="SRT",label="Subtitle Format")
29
+ with gr.Row():
30
+ cb_translate = gr.Checkbox(value=False,visible=False,label="Translate to English?",interactive=True)
31
+ with gr.Row():
32
+ btn_run = gr.Button("GENERATE SUBTITLE FILE",variant="primary")
33
+ with gr.Row():
34
+ tb_indicator = gr.Textbox(label="Output")
35
+
36
+ btn_run.click(fn=whisper_inf.transcribe_file,inputs=[input_file,dd_model,dd_lang,dd_subformat,cb_translate],outputs=[tb_indicator])
37
+ dd_model.change(fn=on_change_models,inputs=[dd_model],outputs=[cb_translate])
38
+
39
+ with gr.TabItem("Youtube"): # tab2
40
+ with gr.Row():
41
+ tb_youtubelink = gr.Textbox(label="Youtube Link" )
42
+ with gr.Row().style(equal_height=True):
43
+ with gr.Column():
44
+ img_thumbnail = gr.Image(label="Youtube Thumbnail")
45
+ with gr.Column():
46
+ tb_title = gr.Label(label="Youtube Title")
47
+ tb_description = gr.Textbox(label="Youtube Description",max_lines=15)
48
+ with gr.Row():
49
+ dd_model = gr.Dropdown(choices=whisper_inf.available_models,value="tiny",label="Model")
50
+ dd_lang = gr.Dropdown(choices=["Automatic Detection"]+whisper_inf.available_langs,value="Automatic Detection",label="Language")
51
+ dd_subformat = gr.Dropdown(choices=["SRT","WebVTT"],value="SRT",label="Subtitle Format")
52
+ with gr.Row():
53
+ cb_translate = gr.Checkbox(value=False,visible=False,label="Translate to English?",interactive=True)
54
+ with gr.Row():
55
+ btn_run = gr.Button("GENERATE SUBTITLE FILE",variant="primary")
56
+ with gr.Row():
57
+ tb_indicator = gr.Textbox(label="Output")
58
+
59
+ btn_run.click(fn=whisper_inf.transcribe_youtube,inputs=[tb_youtubelink,dd_model,dd_lang,dd_subformat,cb_translate],outputs=[tb_indicator])
60
+ tb_youtubelink.change(get_ytmetas,inputs=[tb_youtubelink],outputs=[img_thumbnail,tb_title,tb_description])
61
+ dd_model.change(fn=on_change_models,inputs=[dd_model],outputs=[cb_translate])
62
+
63
+ with gr.TabItem("Mic"): # tab3
64
+ with gr.Row():
65
+ mic_input = gr.Microphone(label="Record with Mic",type="filepath",interactive=True)
66
+ with gr.Row():
67
+ dd_model = gr.Dropdown(choices=whisper_inf.available_models,value="tiny",label="Model")
68
+ dd_lang = gr.Dropdown(choices=["Automatic Detection"]+whisper_inf.available_langs,value="Automatic Detection",label="Language")
69
+ dd_subformat = gr.Dropdown(["SRT","WebVTT"],value="SRT",label="Subtitle Format")
70
+ with gr.Row():
71
+ cb_translate = gr.Checkbox(value=False,visible=False,label="Translate to English?",interactive=True)
72
+ with gr.Row():
73
+ btn_run = gr.Button("GENERATE SUBTITLE FILE",variant="primary")
74
+ with gr.Row():
75
+ tb_indicator = gr.Textbox(label="Output")
76
+
77
+ btn_run.click(fn=whisper_inf.transcribe_mic,inputs=[mic_input,dd_model,dd_lang,dd_subformat,cb_translate],outputs=[tb_indicator])
78
+ dd_model.change(fn=on_change_models,inputs=[dd_model],outputs=[cb_translate])
79
+
80
+ block.launch()
81
+
models/models will be saved here.txt ADDED
File without changes
modules/__init__.py ADDED
File without changes
modules/model_Inference.py ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import whisper
2
+ from modules.subtitle_manager import get_srt,get_vtt,safe_filename
3
+ from modules.youtube_manager import get_ytdata,get_ytaudio
4
+ import gradio as gr
5
+ import os
6
+
7
+ DEFAULT_MODEL_SIZE="tiny"
8
+
9
+ class WhisperInference():
10
+ def __init__(self):
11
+ print("\nInitializing Model..\n")
12
+ self.current_model_size = DEFAULT_MODEL_SIZE
13
+ self.model = whisper.load_model(name=DEFAULT_MODEL_SIZE,download_root="models")
14
+ self.available_models = ["tiny","tiny.en"]
15
+ self.available_langs = sorted(list(whisper.tokenizer.LANGUAGES.values()))
16
+
17
+ def transcribe_file(self,fileobjs
18
+ ,model_size,lang,subformat,istranslate,
19
+ progress=gr.Progress()):
20
+
21
+ def progress_callback(progress_value):
22
+ progress(progress_value,desc="Transcribing..")
23
+
24
+ if model_size != self.current_model_size:
25
+ progress(0,desc="Initializing Model..")
26
+ self.current_model_size = model_size
27
+ self.model = whisper.load_model(name=model_size,download_root="models")
28
+
29
+ if lang == "Automatic Detection" :
30
+ lang = None
31
+
32
+ progress(0,desc="Loading Audio..")
33
+
34
+ files_info = {}
35
+ for fileobj in fileobjs:
36
+ print(f"\n\n {fileobj.name} \n\n")
37
+
38
+ audio = whisper.load_audio(fileobj.name)
39
+
40
+ translatable_model = ["large","large-v1","large-v2"]
41
+ if istranslate and self.current_model_size in translatable_model:
42
+ result = self.model.transcribe(audio=audio,language=lang,verbose=False,task="translate",progress_callback=progress_callback)
43
+ else :
44
+ result = self.model.transcribe(audio=audio,language=lang,verbose=False,progress_callback=progress_callback)
45
+
46
+ progress(1,desc="Completed!")
47
+
48
+ file_name, file_ext = os.path.splitext(os.path.basename(fileobj.orig_name))
49
+ file_name = file_name[:-9]
50
+ file_name = safe_filename(file_name)
51
+
52
+ if subformat == "SRT":
53
+ subtitle = get_srt(result["segments"])
54
+ elif subformat == "WebVTT":
55
+ subtitle = get_vtt(result["segments"])
56
+
57
+ files_info[file_name] = subtitle
58
+
59
+ total_result = ''
60
+ for file_name,subtitle in files_info.items():
61
+ total_result+='------------------------------------\n'
62
+ total_result+=f'{file_name}\n\n'
63
+ total_result+=f'{subtitle}'
64
+
65
+ return f"\n\n{total_result}"
66
+
67
+ def transcribe_youtube(self,youtubelink
68
+ ,model_size,lang,subformat,istranslate,
69
+ progress=gr.Progress()):
70
+
71
+ def progress_callback(progress_value):
72
+ progress(progress_value,desc="Transcribing..")
73
+
74
+ if model_size != self.current_model_size:
75
+ progress(0,desc="Initializing Model..")
76
+ self.current_model_size = model_size
77
+ self.model = whisper.load_model(name=model_size,download_root="models")
78
+
79
+ if lang == "Automatic Detection" :
80
+ lang = None
81
+
82
+ progress(0,desc="Loading Audio from Youtube..")
83
+ yt = get_ytdata(youtubelink)
84
+ audio = whisper.load_audio(get_ytaudio(yt))
85
+
86
+ translatable_model = ["large","large-v1","large-v2"]
87
+ if istranslate and self.current_model_size in translatable_model:
88
+ result = self.model.transcribe(audio=audio,language=lang,verbose=False,task="translate",progress_callback=progress_callback)
89
+ else :
90
+ result = self.model.transcribe(audio=audio,language=lang,verbose=False,progress_callback=progress_callback)
91
+
92
+ progress(1,desc="Completed!")
93
+
94
+ file_name = safe_filename(yt.title)
95
+
96
+ if subformat == "SRT":
97
+ subtitle = get_srt(result["segments"])
98
+ elif subformat == "WebVTT":
99
+ subtitle = get_vtt(result["segments"])
100
+
101
+ return f"\n\n{subtitle}"
102
+
103
+ def transcribe_mic(self,micaudio
104
+ ,model_size,lang,subformat,istranslate,
105
+ progress=gr.Progress()):
106
+
107
+ def progress_callback(progress_value):
108
+ progress(progress_value,desc="Transcribing..")
109
+
110
+ if model_size != self.current_model_size:
111
+ progress(0,desc="Initializing Model..")
112
+ self.current_model_size = model_size
113
+ self.model = whisper.load_model(name=model_size,download_root="models")
114
+
115
+ if lang == "Automatic Detection" :
116
+ lang = None
117
+
118
+ progress(0,desc="Loading Audio..")
119
+
120
+ translatable_model = ["large","large-v1","large-v2"]
121
+ if istranslate and self.current_model_size in translatable_model:
122
+ result = self.model.transcribe(audio=micaudio,language=lang,verbose=False,task="translate",progress_callback=progress_callback)
123
+ else :
124
+ result = self.model.transcribe(audio=micaudio,language=lang,verbose=False,progress_callback=progress_callback)
125
+
126
+ progress(1,desc="Completed!")
127
+
128
+ if subformat == "SRT":
129
+ subtitle = get_srt(result["segments"])
130
+ elif subformat == "WebVTT":
131
+ subtitle = get_vtt(result["segments"])
132
+
133
+ return f"\n\n{subtitle}"
134
+
135
+
modules/subtitle_manager.py ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+
3
+ def timeformat_srt(time):
4
+ hours = time//3600
5
+ minutes = (time - hours*3600)//60
6
+ seconds = time - hours*3600 - minutes*60
7
+ milliseconds = (time - int(time))*1000
8
+ return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d},{int(milliseconds):03d}"
9
+
10
+ def timeformat_vtt(time):
11
+ hours = time//3600
12
+ minutes = (time - hours*3600)//60
13
+ seconds = time - hours*3600 - minutes*60
14
+ milliseconds = (time - int(time))*1000
15
+ return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d}.{int(milliseconds):03d}"
16
+
17
+ def write_srt(subtitle,output_file):
18
+ with open(output_file, 'w', encoding='utf-8') as f:
19
+ f.write(subtitle)
20
+
21
+ def write_vtt(subtitle,output_file):
22
+ with open(output_file, 'w', encoding='utf-8') as f:
23
+ f.write(subtitle)
24
+
25
+ def get_srt(segments):
26
+ output = ""
27
+ for i, segment in enumerate(segments):
28
+ output += f"{i+1}\n"
29
+ output += f"{timeformat_srt(segment['start'])} --> {timeformat_srt(segment['end'])}\n"
30
+ output += f"{segment['text']}\n\n"
31
+ return output
32
+
33
+ def get_vtt(segments):
34
+ output = "WebVTT\n\n"
35
+ for i, segment in enumerate(segments):
36
+ output += f"{i+1}\n"
37
+ output += f"{timeformat_vtt(segment['start'])} --> {timeformat_vtt(segment['end'])}\n"
38
+ output += f"{segment['text']}\n\n"
39
+ return output
40
+
41
+ def safe_filename(name):
42
+ INVALID_FILENAME_CHARS = r'[<>:"/\\|?*\x00-\x1f]'
43
+ return re.sub(INVALID_FILENAME_CHARS, '_', name)
modules/youtube_manager.py ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pytube import YouTube
2
+
3
+ def get_ytdata(link):
4
+ return YouTube(link)
5
+
6
+ def get_ytmetas(link):
7
+ yt = YouTube(link)
8
+ return yt.thumbnail_url,yt.title,yt.description
9
+
10
+ def get_ytaudio(ytdata:YouTube):
11
+ return ytdata.streams.get_audio_only().download(filename="modules/yt_tmp.wav")
requirements.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ git+https://github.com/jhj0517/jhj0517-whisper.git
2
+ pytube
screenshot.png ADDED
ui/__init__.py ADDED
File without changes
ui/htmls.py ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ CSS = """
2
+ .bmc-button {
3
+ padding: 2px 5px;
4
+ border-radius: 5px;
5
+ background-color: #FF813F;
6
+ color: white;
7
+ box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.3);
8
+ text-decoration: none;
9
+ display: inline-block;
10
+ font-size: 20px;
11
+ margin: 2px;
12
+ cursor: pointer;
13
+ -webkit-transition: background-color 0.3s ease;
14
+ -ms-transition: background-color 0.3s ease;
15
+ transition: background-color 0.3s ease;
16
+ }
17
+ .bmc-button:hover,
18
+ .bmc-button:active,
19
+ .bmc-button:focus {
20
+ background-color: #FF5633;
21
+ }
22
+ .markdown {
23
+ margin-bottom: 0;
24
+ padding-bottom: 0;
25
+ }
26
+ .tabs {
27
+ margin-top: 0;
28
+ padding-top: 0;
29
+ }
30
+
31
+ #md_project a {
32
+ color: black;
33
+ text-decoration: none;
34
+ }
35
+ #md_project a:hover {
36
+ text-decoration: underline;
37
+ }
38
+
39
+ #md_note a {
40
+ color: black;
41
+ }
42
+ #md_note a:hover {
43
+ text-decoration: underline;
44
+ }
45
+ """
46
+
47
+ MARKDOWN_TITLE = """
48
+ ### [Whisper Web-UI](https://github.com/jhj0517/Whsiper-WebUI)
49
+ """
50
+ MARKDOWN_NOTE = """
51
+ ### To run it locally, check [here !](https://github.com/jhj0517/Whsiper-WebUI)
52
+ Note: Only the **"tiny"** and **"tiny.en"** models are supported in this space due to the free CPU provided by Hugging Face.
53
+
54
+ If you want to run every Whisper model, you should do it in a local environment.
55
+ """