-
-
Notifications
You must be signed in to change notification settings - Fork 226
/
Copy pathapp.py
368 lines (329 loc) · 23.3 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
import os
import argparse
import gradio as gr
from gradio_i18n import Translate, gettext as _
import yaml
from modules.utils.paths import (FASTER_WHISPER_MODELS_DIR, DIARIZATION_MODELS_DIR, OUTPUT_DIR, WHISPER_MODELS_DIR,
INSANELY_FAST_WHISPER_MODELS_DIR, NLLB_MODELS_DIR, DEFAULT_PARAMETERS_CONFIG_PATH,
UVR_MODELS_DIR, I18N_YAML_PATH)
from modules.utils.files_manager import load_yaml, MEDIA_EXTENSION
from modules.whisper.whisper_factory import WhisperFactory
from modules.translation.nllb_inference import NLLBInference
from modules.ui.htmls import *
from modules.utils.cli_manager import str2bool
from modules.utils.youtube_manager import get_ytmetas
from modules.translation.deepl_api import DeepLAPI
from modules.whisper.data_classes import *
class App:
def __init__(self, args):
self.args = args
self.app = gr.Blocks(css=CSS, theme=self.args.theme, delete_cache=(60, 3600))
self.whisper_inf = WhisperFactory.create_whisper_inference(
whisper_type=self.args.whisper_type,
whisper_model_dir=self.args.whisper_model_dir,
faster_whisper_model_dir=self.args.faster_whisper_model_dir,
insanely_fast_whisper_model_dir=self.args.insanely_fast_whisper_model_dir,
uvr_model_dir=self.args.uvr_model_dir,
output_dir=self.args.output_dir,
)
self.nllb_inf = NLLBInference(
model_dir=self.args.nllb_model_dir,
output_dir=os.path.join(self.args.output_dir, "translations")
)
self.deepl_api = DeepLAPI(
output_dir=os.path.join(self.args.output_dir, "translations")
)
self.i18n = load_yaml(I18N_YAML_PATH)
self.default_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)
print(f"Use \"{self.args.whisper_type}\" implementation\n"
f"Device \"{self.whisper_inf.device}\" is detected")
def create_pipeline_inputs(self):
whisper_params = self.default_params["whisper"]
vad_params = self.default_params["vad"]
diarization_params = self.default_params["diarization"]
uvr_params = self.default_params["bgm_separation"]
with gr.Row():
dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value=whisper_params["model_size"],
label=_("Model"), allow_custom_value=True)
dd_lang = gr.Dropdown(choices=self.whisper_inf.available_langs + [AUTOMATIC_DETECTION],
value=AUTOMATIC_DETECTION if whisper_params["lang"] == AUTOMATIC_DETECTION.unwrap()
else whisper_params["lang"], label=_("Language"))
dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt", "LRC"], value=whisper_params["file_format"], label=_("File Format"))
with gr.Row():
cb_translate = gr.Checkbox(value=whisper_params["is_translate"], label=_("Translate to English?"),
interactive=True)
with gr.Row():
cb_timestamp = gr.Checkbox(value=whisper_params["add_timestamp"],
label=_("Add a timestamp to the end of the filename"),
interactive=True)
with gr.Accordion(_("Advanced Parameters"), open=False):
whisper_inputs = WhisperParams.to_gradio_inputs(defaults=whisper_params, only_advanced=True,
whisper_type=self.args.whisper_type,
available_compute_types=self.whisper_inf.available_compute_types,
compute_type=self.whisper_inf.current_compute_type)
with gr.Accordion(_("Background Music Remover Filter"), open=False):
uvr_inputs = BGMSeparationParams.to_gradio_input(defaults=uvr_params,
available_models=self.whisper_inf.music_separator.available_models,
available_devices=self.whisper_inf.music_separator.available_devices,
device=self.whisper_inf.music_separator.device)
with gr.Accordion(_("Voice Detection Filter"), open=False):
vad_inputs = VadParams.to_gradio_inputs(defaults=vad_params)
with gr.Accordion(_("Diarization"), open=False):
diarization_inputs = DiarizationParams.to_gradio_inputs(defaults=diarization_params,
available_devices=self.whisper_inf.diarizer.available_device,
device=self.whisper_inf.diarizer.device)
pipeline_inputs = [dd_model, dd_lang, cb_translate] + whisper_inputs + vad_inputs + diarization_inputs + uvr_inputs
return (
pipeline_inputs,
dd_file_format,
cb_timestamp
)
def launch(self):
translation_params = self.default_params["translation"]
deepl_params = translation_params["deepl"]
nllb_params = translation_params["nllb"]
uvr_params = self.default_params["bgm_separation"]
with self.app:
lang = gr.Radio(choices=list(self.i18n.keys()),
label=_("Language"), interactive=True,
visible=False, # Set it by development purpose.
)
with Translate(I18N_YAML_PATH):
with gr.Row():
with gr.Column():
gr.Markdown(MARKDOWN, elem_id="md_project")
with gr.Tabs():
with gr.TabItem(_("File")): # tab1
with gr.Column():
input_file = gr.Files(type="filepath", label=_("Upload File here"), file_types=MEDIA_EXTENSION)
tb_input_folder = gr.Textbox(label="Input Folder Path (Optional)",
info="Optional: Specify the folder path where the input files are located, if you prefer to use local files instead of uploading them."
" Leave this field empty if you do not wish to use a local path.",
visible=self.args.colab,
value="")
cb_include_subdirectory = gr.Checkbox(label="Include Subdirectory Files",
info="When using Input Folder Path above, whether to include all files in the subdirectory or not.",
visible=self.args.colab,
value=False)
cb_save_same_dir = gr.Checkbox(label="Save outputs at same directory",
info="When using Input Folder Path above, whether to save output in the same directory as inputs or not, in addition to the original"
" output directory.",
visible=self.args.colab,
value=True)
pipeline_params, dd_file_format, cb_timestamp = self.create_pipeline_inputs()
with gr.Row():
btn_run = gr.Button(_("GENERATE SUBTITLE FILE"), variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label=_("Output"), scale=5)
files_subtitles = gr.Files(label=_("Downloadable output file"), scale=3, interactive=False)
btn_openfolder = gr.Button('📂', scale=1)
params = [input_file, tb_input_folder, cb_include_subdirectory, cb_save_same_dir,
dd_file_format, cb_timestamp]
params = params + pipeline_params
btn_run.click(fn=self.whisper_inf.transcribe_file,
inputs=params,
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
with gr.TabItem(_("Youtube")): # tab2
with gr.Row():
tb_youtubelink = gr.Textbox(label=_("Youtube Link"))
with gr.Row(equal_height=True):
with gr.Column():
img_thumbnail = gr.Image(label=_("Youtube Thumbnail"))
with gr.Column():
tb_title = gr.Label(label=_("Youtube Title"))
tb_description = gr.Textbox(label=_("Youtube Description"), max_lines=15)
pipeline_params, dd_file_format, cb_timestamp = self.create_pipeline_inputs()
with gr.Row():
btn_run = gr.Button(_("GENERATE SUBTITLE FILE"), variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label=_("Output"), scale=5)
files_subtitles = gr.Files(label=_("Downloadable output file"), scale=3)
btn_openfolder = gr.Button('📂', scale=1)
params = [tb_youtubelink, dd_file_format, cb_timestamp]
btn_run.click(fn=self.whisper_inf.transcribe_youtube,
inputs=params + pipeline_params,
outputs=[tb_indicator, files_subtitles])
tb_youtubelink.change(get_ytmetas, inputs=[tb_youtubelink],
outputs=[img_thumbnail, tb_title, tb_description])
btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
with gr.TabItem(_("Mic")): # tab3
with gr.Row():
mic_input = gr.Microphone(label=_("Record with Mic"), type="filepath", interactive=True,
show_download_button=True)
pipeline_params, dd_file_format, cb_timestamp = self.create_pipeline_inputs()
with gr.Row():
btn_run = gr.Button(_("GENERATE SUBTITLE FILE"), variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label=_("Output"), scale=5)
files_subtitles = gr.Files(label=_("Downloadable output file"), scale=3)
btn_openfolder = gr.Button('📂', scale=1)
params = [mic_input, dd_file_format, cb_timestamp]
btn_run.click(fn=self.whisper_inf.transcribe_mic,
inputs=params + pipeline_params,
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
with gr.TabItem(_("T2T Translation")): # tab 4
with gr.Row():
file_subs = gr.Files(type="filepath", label=_("Upload Subtitle Files to translate here"))
with gr.TabItem(_("DeepL API")): # sub tab1
with gr.Row():
tb_api_key = gr.Textbox(label=_("Your Auth Key (API KEY)"),
value=deepl_params["api_key"])
with gr.Row():
dd_source_lang = gr.Dropdown(label=_("Source Language"),
value=AUTOMATIC_DETECTION if deepl_params["source_lang"] == AUTOMATIC_DETECTION.unwrap()
else deepl_params["source_lang"],
choices=list(self.deepl_api.available_source_langs.keys()))
dd_target_lang = gr.Dropdown(label=_("Target Language"),
value=deepl_params["target_lang"],
choices=list(self.deepl_api.available_target_langs.keys()))
with gr.Row():
cb_is_pro = gr.Checkbox(label=_("Pro User?"), value=deepl_params["is_pro"])
with gr.Row():
cb_timestamp = gr.Checkbox(value=translation_params["add_timestamp"],
label=_("Add a timestamp to the end of the filename"),
interactive=True)
with gr.Row():
btn_run = gr.Button(_("TRANSLATE SUBTITLE FILE"), variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label=_("Output"), scale=5)
files_subtitles = gr.Files(label=_("Downloadable output file"), scale=3)
btn_openfolder = gr.Button('📂', scale=1)
btn_run.click(fn=self.deepl_api.translate_deepl,
inputs=[tb_api_key, file_subs, dd_source_lang, dd_target_lang,
cb_is_pro, cb_timestamp],
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(
fn=lambda: self.open_folder(os.path.join(self.args.output_dir, "translations")),
inputs=None,
outputs=None)
with gr.TabItem(_("NLLB")): # sub tab2
with gr.Row():
dd_model_size = gr.Dropdown(label=_("Model"), value=nllb_params["model_size"],
choices=self.nllb_inf.available_models)
dd_source_lang = gr.Dropdown(label=_("Source Language"),
value=nllb_params["source_lang"],
choices=self.nllb_inf.available_source_langs)
dd_target_lang = gr.Dropdown(label=_("Target Language"),
value=nllb_params["target_lang"],
choices=self.nllb_inf.available_target_langs)
with gr.Row():
nb_max_length = gr.Number(label="Max Length Per Line", value=nllb_params["max_length"],
precision=0)
with gr.Row():
cb_timestamp = gr.Checkbox(value=translation_params["add_timestamp"],
label=_("Add a timestamp to the end of the filename"),
interactive=True)
with gr.Row():
btn_run = gr.Button(_("TRANSLATE SUBTITLE FILE"), variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label=_("Output"), scale=5)
files_subtitles = gr.Files(label=_("Downloadable output file"), scale=3)
btn_openfolder = gr.Button('📂', scale=1)
with gr.Column():
md_vram_table = gr.HTML(NLLB_VRAM_TABLE, elem_id="md_nllb_vram_table")
btn_run.click(fn=self.nllb_inf.translate_file,
inputs=[file_subs, dd_model_size, dd_source_lang, dd_target_lang,
nb_max_length, cb_timestamp],
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(
fn=lambda: self.open_folder(os.path.join(self.args.output_dir, "translations")),
inputs=None,
outputs=None)
with gr.TabItem(_("BGM Separation")):
files_audio = gr.Files(type="filepath", label=_("Upload Audio Files to separate background music"))
dd_uvr_device = gr.Dropdown(label=_("Device"), value=self.whisper_inf.music_separator.device,
choices=self.whisper_inf.music_separator.available_devices)
dd_uvr_model_size = gr.Dropdown(label=_("Model"), value=uvr_params["uvr_model_size"],
choices=self.whisper_inf.music_separator.available_models)
nb_uvr_segment_size = gr.Number(label="Segment Size", value=uvr_params["segment_size"],
precision=0)
cb_uvr_save_file = gr.Checkbox(label=_("Save separated files to output"),
value=True, visible=False)
btn_run = gr.Button(_("SEPARATE BACKGROUND MUSIC"), variant="primary")
with gr.Column():
with gr.Row():
ad_instrumental = gr.Audio(label=_("Instrumental"), scale=8)
btn_open_instrumental_folder = gr.Button('📂', scale=1)
with gr.Row():
ad_vocals = gr.Audio(label=_("Vocals"), scale=8)
btn_open_vocals_folder = gr.Button('📂', scale=1)
btn_run.click(fn=self.whisper_inf.music_separator.separate_files,
inputs=[files_audio, dd_uvr_model_size, dd_uvr_device, nb_uvr_segment_size,
cb_uvr_save_file],
outputs=[ad_instrumental, ad_vocals])
btn_open_instrumental_folder.click(inputs=None,
outputs=None,
fn=lambda: self.open_folder(os.path.join(
self.args.output_dir, "UVR", "instrumental"
)))
btn_open_vocals_folder.click(inputs=None,
outputs=None,
fn=lambda: self.open_folder(os.path.join(
self.args.output_dir, "UVR", "vocals"
)))
# Launch the app with optional gradio settings
args = self.args
self.app.queue(
api_open=args.api_open
).launch(
share=args.share,
server_name=args.server_name,
server_port=args.server_port,
auth=(args.username, args.password) if args.username and args.password else None,
root_path=args.root_path,
inbrowser=args.inbrowser,
ssl_verify=args.ssl_verify,
ssl_keyfile=args.ssl_keyfile,
ssl_keyfile_password=args.ssl_keyfile_password,
ssl_certfile=args.ssl_certfile,
allowed_paths=eval(args.allowed_paths) if args.allowed_paths else None
)
@staticmethod
def open_folder(folder_path: str):
if os.path.exists(folder_path):
os.system(f"start {folder_path}")
else:
os.makedirs(folder_path, exist_ok=True)
print(f"The directory path {folder_path} has newly created.")
parser = argparse.ArgumentParser()
parser.add_argument('--whisper_type', type=str, default=WhisperImpl.FASTER_WHISPER.value,
choices=[item.value for item in WhisperImpl],
help='A type of the whisper implementation (Github repo name)')
parser.add_argument('--share', type=str2bool, default=False, nargs='?', const=True, help='Gradio share value')
parser.add_argument('--server_name', type=str, default=None, help='Gradio server host')
parser.add_argument('--server_port', type=int, default=None, help='Gradio server port')
parser.add_argument('--root_path', type=str, default=None, help='Gradio root path')
parser.add_argument('--username', type=str, default=None, help='Gradio authentication username')
parser.add_argument('--password', type=str, default=None, help='Gradio authentication password')
parser.add_argument('--theme', type=str, default=None, help='Gradio Blocks theme')
parser.add_argument('--colab', type=str2bool, default=False, nargs='?', const=True, help='Is colab user or not')
parser.add_argument('--api_open', type=str2bool, default=False, nargs='?', const=True,
help='Enable api or not in Gradio')
parser.add_argument('--allowed_paths', type=str, default=None, help='Gradio allowed paths')
parser.add_argument('--inbrowser', type=str2bool, default=True, nargs='?', const=True,
help='Whether to automatically start Gradio app or not')
parser.add_argument('--ssl_verify', type=str2bool, default=True, nargs='?', const=True,
help='Whether to verify SSL or not')
parser.add_argument('--ssl_keyfile', type=str, default=None, help='SSL Key file location')
parser.add_argument('--ssl_keyfile_password', type=str, default=None, help='SSL Key file password')
parser.add_argument('--ssl_certfile', type=str, default=None, help='SSL cert file location')
parser.add_argument('--whisper_model_dir', type=str, default=WHISPER_MODELS_DIR,
help='Directory path of the whisper model')
parser.add_argument('--faster_whisper_model_dir', type=str, default=FASTER_WHISPER_MODELS_DIR,
help='Directory path of the faster-whisper model')
parser.add_argument('--insanely_fast_whisper_model_dir', type=str,
default=INSANELY_FAST_WHISPER_MODELS_DIR,
help='Directory path of the insanely-fast-whisper model')
parser.add_argument('--diarization_model_dir', type=str, default=DIARIZATION_MODELS_DIR,
help='Directory path of the diarization model')
parser.add_argument('--nllb_model_dir', type=str, default=NLLB_MODELS_DIR,
help='Directory path of the Facebook NLLB model')
parser.add_argument('--uvr_model_dir', type=str, default=UVR_MODELS_DIR,
help='Directory path of the UVR model')
parser.add_argument('--output_dir', type=str, default=OUTPUT_DIR, help='Directory path of the outputs')
_args = parser.parse_args()
if __name__ == "__main__":
app = App(args=_args)
app.launch()