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Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,306 @@
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1 |
+
# AGPL: a notification must be added stating that changes have been made to that file.
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2 |
+
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3 |
+
import os
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4 |
+
import shutil
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5 |
+
from pathlib import Path
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6 |
+
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7 |
+
import streamlit as st
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8 |
+
from random import randint
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9 |
+
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10 |
+
from tortoise.api import MODELS_DIR
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11 |
+
from tortoise.inference import (
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12 |
+
infer_on_texts,
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13 |
+
run_and_save_tts,
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14 |
+
split_and_recombine_text,
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15 |
+
)
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16 |
+
from tortoise.utils.diffusion import SAMPLERS
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17 |
+
from app_utils.filepicker import st_file_selector
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18 |
+
from app_utils.conf import TortoiseConfig
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19 |
+
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20 |
+
from app_utils.funcs import (
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21 |
+
timeit,
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22 |
+
load_model,
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23 |
+
list_voices,
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24 |
+
load_voice_conditionings,
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25 |
+
)
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26 |
+
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27 |
+
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28 |
+
LATENT_MODES = [
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29 |
+
"Tortoise original (bad)",
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30 |
+
"average per 4.27s (broken on small files)",
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31 |
+
"average per voice file (broken on small files)",
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32 |
+
]
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+
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34 |
+
def main():
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35 |
+
conf = TortoiseConfig()
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36 |
+
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37 |
+
with st.expander("Create New Voice", expanded=True):
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38 |
+
if "file_uploader_key" not in st.session_state:
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39 |
+
st.session_state["file_uploader_key"] = str(randint(1000, 100000000))
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40 |
+
st.session_state["text_input_key"] = str(randint(1000, 100000000))
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41 |
+
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42 |
+
uploaded_files = st.file_uploader(
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43 |
+
"Upload Audio Samples for a New Voice",
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44 |
+
accept_multiple_files=True,
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+
type=["wav"],
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46 |
+
key=st.session_state["file_uploader_key"]
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47 |
+
)
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48 |
+
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49 |
+
voice_name = st.text_input(
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+
"New Voice Name",
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51 |
+
help="Enter a name for your new voice.",
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+
value="",
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53 |
+
key=st.session_state["text_input_key"]
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+
)
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55 |
+
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+
create_voice_button = st.button(
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57 |
+
"Create Voice",
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+
disabled = ((voice_name.strip() == "") | (len(uploaded_files) == 0))
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59 |
+
)
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60 |
+
if create_voice_button:
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61 |
+
st.write(st.session_state)
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62 |
+
with st.spinner(f"Creating new voice: {voice_name}"):
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63 |
+
new_voice_name = voice_name.strip().replace(" ", "_")
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64 |
+
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65 |
+
voices_dir = f'./tortoise/voices/{new_voice_name}/'
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66 |
+
if os.path.exists(voices_dir):
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67 |
+
shutil.rmtree(voices_dir)
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68 |
+
os.makedirs(voices_dir)
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69 |
+
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70 |
+
for index, uploaded_file in enumerate(uploaded_files):
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71 |
+
bytes_data = uploaded_file.read()
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72 |
+
with open(f"{voices_dir}voice_sample{index}.wav", "wb") as wav_file:
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wav_file.write(bytes_data)
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+
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+
st.session_state["text_input_key"] = str(randint(1000, 100000000))
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+
st.session_state["file_uploader_key"] = str(randint(1000, 100000000))
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+
st.experimental_rerun()
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78 |
+
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79 |
+
text = st.text_area(
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80 |
+
"Text",
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81 |
+
help="Text to speak.",
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82 |
+
value="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.",
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83 |
+
)
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84 |
+
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85 |
+
voices = [v for v in os.listdir("tortoise/voices") if v != "cond_latent_example"]
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86 |
+
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87 |
+
voice = st.selectbox(
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88 |
+
"Voice",
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89 |
+
voices,
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90 |
+
help="Selects the voice to use for generation. See options in voices/ directory (and add your own!) "
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91 |
+
"Use the & character to join two voices together. Use a comma to perform inference on multiple voices.",
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92 |
+
index=0,
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93 |
+
)
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94 |
+
preset = st.selectbox(
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95 |
+
"Preset",
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96 |
+
(
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97 |
+
"single_sample",
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98 |
+
"ultra_fast",
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99 |
+
"very_fast",
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100 |
+
"ultra_fast_old",
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101 |
+
"fast",
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102 |
+
"standard",
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103 |
+
"high_quality",
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104 |
+
),
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105 |
+
help="Which voice preset to use.",
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106 |
+
index=1,
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107 |
+
)
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108 |
+
with st.expander("Advanced"):
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109 |
+
col1, col2 = st.columns(2)
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110 |
+
with col1:
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111 |
+
"""#### Model parameters"""
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112 |
+
candidates = st.number_input(
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113 |
+
"Candidates",
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114 |
+
help="How many output candidates to produce per-voice.",
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115 |
+
value=1,
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116 |
+
)
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117 |
+
latent_averaging_mode = st.radio(
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118 |
+
"Latent averaging mode",
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119 |
+
LATENT_MODES,
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120 |
+
help="How voice samples should be averaged together.",
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121 |
+
index=0,
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122 |
+
)
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123 |
+
sampler = st.radio(
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124 |
+
"Sampler",
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125 |
+
#SAMPLERS,
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126 |
+
["dpm++2m", "p", "ddim"],
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127 |
+
help="Diffusion sampler. Note that dpm++2m is experimental and typically requires more steps.",
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128 |
+
index=1,
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129 |
+
)
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130 |
+
steps = st.number_input(
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131 |
+
"Steps",
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132 |
+
help="Override the steps used for diffusion (default depends on preset)",
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133 |
+
value=10,
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134 |
+
)
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135 |
+
seed = st.number_input(
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136 |
+
"Seed",
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137 |
+
help="Random seed which can be used to reproduce results.",
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138 |
+
value=-1,
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139 |
+
)
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140 |
+
if seed == -1:
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141 |
+
seed = None
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142 |
+
voice_fixer = st.checkbox(
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143 |
+
"Voice fixer",
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144 |
+
help="Use `voicefixer` to improve audio quality. This is a post-processing step which can be applied to any output.",
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145 |
+
value=True,
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146 |
+
)
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147 |
+
"""#### Directories"""
|
148 |
+
output_path = st.text_input(
|
149 |
+
"Output Path", help="Where to store outputs.", value="results/"
|
150 |
+
)
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151 |
+
|
152 |
+
with col2:
|
153 |
+
"""#### Optimizations"""
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154 |
+
high_vram = not st.checkbox(
|
155 |
+
"Low VRAM",
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156 |
+
help="Re-enable default offloading behaviour of tortoise",
|
157 |
+
value=True,
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158 |
+
)
|
159 |
+
half = st.checkbox(
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160 |
+
"Half-Precision",
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161 |
+
help="Enable autocast to half precision for autoregressive model",
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162 |
+
value=False,
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163 |
+
)
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164 |
+
kv_cache = st.checkbox(
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165 |
+
"Key-Value Cache",
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166 |
+
help="Enable kv_cache usage, leading to drastic speedups but worse memory usage",
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167 |
+
value=True,
|
168 |
+
)
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169 |
+
cond_free = st.checkbox(
|
170 |
+
"Conditioning Free",
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171 |
+
help="Force conditioning free diffusion",
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172 |
+
value=True,
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173 |
+
)
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174 |
+
no_cond_free = st.checkbox(
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175 |
+
"Force Not Conditioning Free",
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176 |
+
help="Force disable conditioning free diffusion",
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177 |
+
value=False,
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178 |
+
)
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179 |
+
|
180 |
+
"""#### Text Splitting"""
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181 |
+
min_chars_to_split = st.number_input(
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182 |
+
"Min Chars to Split",
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183 |
+
help="Minimum number of characters to split text on",
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184 |
+
min_value=50,
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185 |
+
value=200,
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186 |
+
step=1,
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187 |
+
)
|
188 |
+
|
189 |
+
"""#### Debug"""
|
190 |
+
produce_debug_state = st.checkbox(
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191 |
+
"Produce Debug State",
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192 |
+
help="Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.",
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193 |
+
value=True,
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194 |
+
)
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195 |
+
|
196 |
+
ar_checkpoint = "."
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197 |
+
diff_checkpoint = "."
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198 |
+
if st.button("Update Basic Settings"):
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199 |
+
conf.update(
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200 |
+
EXTRA_VOICES_DIR=extra_voices_dir,
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201 |
+
LOW_VRAM=not high_vram,
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202 |
+
AR_CHECKPOINT=ar_checkpoint,
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203 |
+
DIFF_CHECKPOINT=diff_checkpoint,
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204 |
+
)
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205 |
+
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206 |
+
ar_checkpoint = None
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207 |
+
diff_checkpoint = None
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208 |
+
tts = load_model(MODELS_DIR, high_vram, kv_cache, ar_checkpoint, diff_checkpoint)
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209 |
+
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210 |
+
if st.button("Start"):
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211 |
+
assert latent_averaging_mode
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212 |
+
assert preset
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213 |
+
assert voice
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214 |
+
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215 |
+
def show_generation(fp, filename: str):
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216 |
+
"""
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217 |
+
audio_buffer = BytesIO()
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218 |
+
save_gen_with_voicefix(g, audio_buffer, squeeze=False)
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219 |
+
torchaudio.save(audio_buffer, g, 24000, format='wav')
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220 |
+
"""
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221 |
+
st.audio(str(fp), format="audio/wav")
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222 |
+
st.download_button(
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223 |
+
"Download sample",
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224 |
+
str(fp),
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225 |
+
file_name=filename, # this doesn't actually seem to work lol
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226 |
+
)
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227 |
+
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228 |
+
with st.spinner(
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229 |
+
f"Generating {candidates} candidates for voice {voice} (seed={seed}). You can see progress in the terminal"
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230 |
+
):
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231 |
+
os.makedirs(output_path, exist_ok=True)
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232 |
+
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233 |
+
selected_voices = voice.split(",")
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234 |
+
for k, selected_voice in enumerate(selected_voices):
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235 |
+
if "&" in selected_voice:
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236 |
+
voice_sel = selected_voice.split("&")
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237 |
+
else:
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238 |
+
voice_sel = [selected_voice]
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239 |
+
voice_samples, conditioning_latents = load_voice_conditionings(
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240 |
+
voice_sel, []
|
241 |
+
)
|
242 |
+
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243 |
+
voice_path = Path(os.path.join(output_path, selected_voice))
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244 |
+
|
245 |
+
with timeit(
|
246 |
+
f"Generating {candidates} candidates for voice {selected_voice} (seed={seed})"
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247 |
+
):
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248 |
+
nullable_kwargs = {
|
249 |
+
k: v
|
250 |
+
for k, v in zip(
|
251 |
+
["sampler", "diffusion_iterations", "cond_free"],
|
252 |
+
[sampler, steps, cond_free],
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253 |
+
)
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254 |
+
if v is not None
|
255 |
+
}
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256 |
+
|
257 |
+
def call_tts(text: str):
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258 |
+
return tts.tts_with_preset(
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259 |
+
text,
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260 |
+
k=candidates,
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261 |
+
voice_samples=voice_samples,
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262 |
+
conditioning_latents=conditioning_latents,
|
263 |
+
preset=preset,
|
264 |
+
use_deterministic_seed=seed,
|
265 |
+
return_deterministic_state=True,
|
266 |
+
cvvp_amount=0.0,
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267 |
+
half=half,
|
268 |
+
latent_averaging_mode=LATENT_MODES.index(
|
269 |
+
latent_averaging_mode
|
270 |
+
),
|
271 |
+
**nullable_kwargs,
|
272 |
+
)
|
273 |
+
|
274 |
+
if len(text) < min_chars_to_split:
|
275 |
+
filepaths = run_and_save_tts(
|
276 |
+
call_tts,
|
277 |
+
text,
|
278 |
+
voice_path,
|
279 |
+
return_deterministic_state=True,
|
280 |
+
return_filepaths=True,
|
281 |
+
voicefixer=voice_fixer,
|
282 |
+
)
|
283 |
+
for i, fp in enumerate(filepaths):
|
284 |
+
show_generation(fp, f"{selected_voice}-text-{i}.wav")
|
285 |
+
else:
|
286 |
+
desired_length = int(min_chars_to_split)
|
287 |
+
texts = split_and_recombine_text(
|
288 |
+
text, desired_length, desired_length + 100
|
289 |
+
)
|
290 |
+
filepaths = infer_on_texts(
|
291 |
+
call_tts,
|
292 |
+
texts,
|
293 |
+
voice_path,
|
294 |
+
return_deterministic_state=True,
|
295 |
+
return_filepaths=True,
|
296 |
+
lines_to_regen=set(range(len(texts))),
|
297 |
+
voicefixer=voice_fixer,
|
298 |
+
)
|
299 |
+
for i, fp in enumerate(filepaths):
|
300 |
+
show_generation(fp, f"{selected_voice}-text-{i}.wav")
|
301 |
+
if produce_debug_state:
|
302 |
+
"""Debug states can be found in the output directory"""
|
303 |
+
|
304 |
+
|
305 |
+
if __name__ == "__main__":
|
306 |
+
main()
|