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[main fix] cli fixed

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alexlnkp
2023-07-25 15:55:04 +07:00
parent 4eae15f862
commit 4bc20f84d3
1 changed files with 102 additions and 101 deletions
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203
infer-web.py
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@@ -124,17 +124,6 @@ else:
default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])
class ToolButton(gr.Button, gr.components.FormComponent):
"""Small button with single emoji as text, fits inside gradio forms"""
def __init__(self, **kwargs):
super().__init__(variant="tool", **kwargs)
def get_block_name(self):
return "button"
hubert_model = None
@@ -227,19 +216,6 @@ def get_fshift_presets():
else:
return ''
def get_audios():
if check_for_name() != '':
audios_path= '"' + os.path.abspath(os.getcwd()) + '/audios/'
if os.path.exists(audios_path):
for file in os.listdir(audios_path):
print(audios_path.join(file) + '"')
return os.path.join(audios_path, file + '"')
return ''
else:
return ''
def vc_single(
sid,
input_audio_path0,
@@ -828,12 +804,12 @@ def change_sr2(sr2, if_f0_3, version19):
if not if_pretrained_generator_exist:
print(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
"doesn't exist, will not use pretrained model",
)
if not if_pretrained_discriminator_exist:
print(
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
"doesn't exist, will not use pretrained model",
)
return (
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
@@ -864,12 +840,12 @@ def change_version19(sr2, if_f0_3, version19):
if not if_pretrained_generator_exist:
print(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
"doesn't exist, will not use pretrained model",
)
if not if_pretrained_discriminator_exist:
print(
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
"doesn't exist, will not use pretrained model",
)
return (
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
@@ -1546,10 +1522,25 @@ def cli_infer(com):
mix = float(com[10])
feature_ratio = float(com[11])
protection_amnt = float(com[12])
#####
protect1 = 0.5
if com[14] == 'False' or com[14] == 'false':
DoFormant = False
Quefrency = 0.0
Timbre = 0.0
with open('formanting.txt', 'w') as fxxxf:
fxxxf.truncate(0)
fxxxf.writelines([str(DoFormant) + '\n', str(Quefrency) + '\n', str(Timbre) + '\n'])
else:
DoFormant = True
Quefrency = float(com[15])
Timbre = float(com[16])
with open('formanting.txt', 'w') as fxxxf:
fxxxf.truncate(0)
fxxxf.writelines([str(DoFormant) + '\n', str(Quefrency) + '\n', str(Timbre) + '\n'])
print("Mangio-RVC-Fork Infer-CLI: Starting the inference...")
vc_data = get_vc(model_name)
vc_data = get_vc(model_name, protection_amnt, protect1)
print(vc_data)
print("Mangio-RVC-Fork Infer-CLI: Performing inference...")
conversion_data = vc_single(
@@ -1704,70 +1695,86 @@ def preset_apply(preset, qfer, tmbr):
def print_page_details():
if cli_current_page == "HOME":
print(" go home : Takes you back to home with a navigation list.")
print(" go infer : Takes you to inference command execution.\n")
print(" go pre-process : Takes you to training step.1) pre-process command execution.")
print(" go extract-feature : Takes you to training step.2) extract-feature command execution.")
print(" go train : Takes you to training step.3) being or continue training command execution.")
print(" go train-feature : Takes you to the train feature index command execution.\n")
print(" go extract-model : Takes you to the extract small model command execution.")
print(
"\n go home : Takes you back to home with a navigation list."
"\n go infer : Takes you to inference command execution."
"\n go pre-process : Takes you to training step.1) pre-process command execution."
"\n go extract-feature : Takes you to training step.2) extract-feature command execution."
"\n go train : Takes you to training step.3) being or continue training command execution."
"\n go train-feature : Takes you to the train feature index command execution."
"\n go extract-model : Takes you to the extract small model command execution."
)
elif cli_current_page == "INFER":
print(" arg 1) model name with .pth in ./weights: mi-test.pth")
print(" arg 2) source audio path: myFolder\\MySource.wav")
print(" arg 3) output file name to be placed in './audio-outputs': MyTest.wav")
print(" arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index")
print(" arg 5) speaker id: 0")
print(" arg 6) transposition: 0")
print(" arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny)")
print(" arg 8) crepe hop length: 160")
print(" arg 9) harvest median filter radius: 3 (0-7)")
print(" arg 10) post resample rate: 0")
print(" arg 11) mix volume envelope: 1")
print(" arg 12) feature index ratio: 0.78 (0-1)")
print(" arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.) \n")
print("Example: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33")
print(
"\n arg 1) model name with .pth in ./weights: mi-test.pth"
"\n arg 2) source audio path: myFolder\\MySource.wav"
"\n arg 3) output file name to be placed in './audio-outputs': MyTest.wav"
"\n arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index"
"\n arg 5) speaker id: 0"
"\n arg 6) transposition: 0"
"\n arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny, rmvpe)"
"\n arg 8) crepe hop length: 160"
"\n arg 9) harvest median filter radius: 3 (0-7)"
"\n arg 10) post resample rate: 0"
"\n arg 11) mix volume envelope: 1"
"\n arg 12) feature index ratio: 0.78 (0-1)"
"\n arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.)"
"\n arg 14) Whether to formant shift the inference audio before conversion: False (if set to false, you can ignore setting the quefrency and timbre values for formanting)"
"\n arg 15)* Quefrency for formanting: 8.0 (no need to set if arg14 is False/false)"
"\n arg 16)* Timbre for formanting: 1.2 (no need to set if arg14 is False/false) \n"
"\nExample: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33 0.45 True 8.0 1.2"
)
elif cli_current_page == "PRE-PROCESS":
print(" arg 1) Model folder name in ./logs: mi-test")
print(" arg 2) Trainset directory: mydataset (or) E:\\my-data-set")
print(" arg 3) Sample rate: 40k (32k, 40k, 48k)")
print(" arg 4) Number of CPU threads to use: 8 \n")
print("Example: mi-test mydataset 40k 24")
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Trainset directory: mydataset (or) E:\\my-data-set"
"\n arg 3) Sample rate: 40k (32k, 40k, 48k)"
"\n arg 4) Number of CPU threads to use: 8 \n"
"\nExample: mi-test mydataset 40k 24"
)
elif cli_current_page == "EXTRACT-FEATURE":
print(" arg 1) Model folder name in ./logs: mi-test")
print(" arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)")
print(" arg 3) Number of CPU threads to use: 8")
print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)")
print(" arg 5) f0 Method: harvest (pm, harvest, dio, crepe)")
print(" arg 6) Crepe hop length: 128")
print(" arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n")
print("Example: mi-test 0 24 1 harvest 128 v2")
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)"
"\n arg 3) Number of CPU threads to use: 8"
"\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
"\n arg 5) f0 Method: harvest (pm, harvest, dio, crepe)"
"\n arg 6) Crepe hop length: 128"
"\n arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n"
"\nExample: mi-test 0 24 1 harvest 128 v2"
)
elif cli_current_page == "TRAIN":
print(" arg 1) Model folder name in ./logs: mi-test")
print(" arg 2) Sample rate: 40k (32k, 40k, 48k)")
print(" arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)")
print(" arg 4) speaker id: 0")
print(" arg 5) Save epoch iteration: 50")
print(" arg 6) Total epochs: 10000")
print(" arg 7) Batch size: 8")
print(" arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)")
print(" arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)")
print(" arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)")
print(" arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)")
print(" arg 12) Model architecture version: v2 (use either v1 or v2)\n")
print("Example: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2")
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Sample rate: 40k (32k, 40k, 48k)"
"\n arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
"\n arg 4) speaker id: 0"
"\n arg 5) Save epoch iteration: 50"
"\n arg 6) Total epochs: 10000"
"\n arg 7) Batch size: 8"
"\n arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)"
"\n arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)"
"\n arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)"
"\n arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)"
"\n arg 12) Model architecture version: v2 (use either v1 or v2)\n"
"\nExample: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2"
)
elif cli_current_page == "TRAIN-FEATURE":
print(" arg 1) Model folder name in ./logs: mi-test")
print(" arg 2) Model architecture version: v2 (use either v1 or v2)\n")
print("Example: mi-test v2")
print(
"\n arg 1) Model folder name in ./logs: mi-test"
"\n arg 2) Model architecture version: v2 (use either v1 or v2)\n"
"\nExample: mi-test v2"
)
elif cli_current_page == "EXTRACT-MODEL":
print(" arg 1) Model Path: logs/mi-test/G_168000.pth")
print(" arg 2) Model save name: MyModel")
print(" arg 3) Sample rate: 40k (32k, 40k, 48k)")
print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)")
print(' arg 5) Model information: "My Model"')
print(" arg 6) Model architecture version: v2 (use either v1 or v2)\n")
print('Example: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2')
print("")
print(
"\n arg 1) Model Path: logs/mi-test/G_168000.pth"
"\n arg 2) Model save name: MyModel"
"\n arg 3) Sample rate: 40k (32k, 40k, 48k)"
"\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)"
'\n arg 5) Model information: "My Model"'
"\n arg 6) Model architecture version: v2 (use either v1 or v2)\n"
'\nExample: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2'
)
def change_page(page):
global cli_current_page
@@ -1809,7 +1816,7 @@ def execute_command(com):
def cli_navigation_loop():
while True:
print("You are currently in '%s':" % cli_current_page)
print("\nYou are currently in '%s':" % cli_current_page)
print_page_details()
command = input("%s: " % cli_current_page)
try:
@@ -1854,11 +1861,11 @@ def match_index(sid0):
if filename.endswith(".index"):
for i in range(len(indexes_list)):
if indexes_list[i] == (os.path.join(("./logs/" + folder), filename).replace('\\','/')):
print('regular index found')
#print('regular index found')
break
else:
if indexes_list[i] == (os.path.join(("./logs/" + folder.lower()), filename).replace('\\','/')):
print('lowered index found')
#print('lowered index found')
parent_dir = "./logs/" + folder.lower()
break
#elif (indexes_list[i]).casefold() == ((os.path.join(("./logs/" + folder), filename).replace('\\','/')).casefold()):
@@ -1887,10 +1894,6 @@ def match_index(sid0):
#print('nothing found')
return ('', '')
def choveraudio():
return ''
def stoptraining(mim):
if int(mim) == 1:
@@ -1976,10 +1979,10 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
input_audio1 = gr.Dropdown(
label=i18n("Auto detect audio path and select from the dropdown:"),
choices=sorted(audio_paths),
value=get_audios(),
value='',
interactive=True,
)
input_audio1.change(fn=choveraudio,inputs=[],outputs=[input_audio0])
input_audio1.change(fn=lambda:'',inputs=[],outputs=[input_audio0])
f0method0 = gr.Radio(
label=i18n(
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
@@ -2081,9 +2084,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
visible=False,
)
formant_refresh_button = gr.Button(value='\U0001f504', visible=False,variant='primary')
#formant_refresh_button = ToolButton( elem_id='1')
#create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets")
qfrency = gr.Slider(
value=Quefrency,
label="Quefrency for formant shifting",
@@ -2417,7 +2418,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length])
but2 = gr.Button(i18n("特征提取"), variant="primary")
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8, interactive=False)
but2.click(
extract_f0_feature,
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length],