Spaces:
Runtime error
Runtime error
zhzluke96
commited on
Commit
·
84cfd61
1
Parent(s):
22884c9
update
Browse files- modules/devices.py +8 -0
- modules/generate_audio.py +4 -0
- modules/normalization.py +38 -10
- modules/utils/audio.py +10 -0
- webui.py +27 -13
modules/devices.py
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
def torch_gc():
|
| 5 |
+
if torch.cuda.is_available():
|
| 6 |
+
with torch.cuda.device("cuda"):
|
| 7 |
+
torch.cuda.empty_cache()
|
| 8 |
+
torch.cuda.ipc_collect()
|
modules/generate_audio.py
CHANGED
|
@@ -8,6 +8,8 @@ from modules import models, config
|
|
| 8 |
|
| 9 |
import logging
|
| 10 |
|
|
|
|
|
|
|
| 11 |
logger = logging.getLogger(__name__)
|
| 12 |
|
| 13 |
|
|
@@ -96,6 +98,8 @@ def generate_audio_batch(
|
|
| 96 |
|
| 97 |
sample_rate = 24000
|
| 98 |
|
|
|
|
|
|
|
| 99 |
return [(sample_rate, np.array(wav).flatten().astype(np.float32)) for wav in wavs]
|
| 100 |
|
| 101 |
|
|
|
|
| 8 |
|
| 9 |
import logging
|
| 10 |
|
| 11 |
+
from modules import devices
|
| 12 |
+
|
| 13 |
logger = logging.getLogger(__name__)
|
| 14 |
|
| 15 |
|
|
|
|
| 98 |
|
| 99 |
sample_rate = 24000
|
| 100 |
|
| 101 |
+
devices.torch_gc()
|
| 102 |
+
|
| 103 |
return [(sample_rate, np.array(wav).flatten().astype(np.float32)) for wav in wavs]
|
| 104 |
|
| 105 |
|
modules/normalization.py
CHANGED
|
@@ -75,13 +75,15 @@ character_map = {
|
|
| 75 |
"“": " ",
|
| 76 |
"’": " ",
|
| 77 |
"”": " ",
|
|
|
|
|
|
|
| 78 |
":": ",",
|
| 79 |
";": ",",
|
| 80 |
"!": ".",
|
| 81 |
"(": ",",
|
| 82 |
")": ",",
|
| 83 |
-
|
| 84 |
-
|
| 85 |
">": ",",
|
| 86 |
"<": ",",
|
| 87 |
"-": ",",
|
|
@@ -110,13 +112,6 @@ def apply_emoji_map(text):
|
|
| 110 |
return emojiswitch.demojize(text, delimiters=("", ""), lang="zh")
|
| 111 |
|
| 112 |
|
| 113 |
-
@pre_normalize()
|
| 114 |
-
def apply_markdown_to_text(text):
|
| 115 |
-
if is_markdown(text):
|
| 116 |
-
text = markdown_to_text(text)
|
| 117 |
-
return text
|
| 118 |
-
|
| 119 |
-
|
| 120 |
@post_normalize()
|
| 121 |
def insert_spaces_between_uppercase(s):
|
| 122 |
# 使用正则表达式在每个相邻的大写字母之间插入空格
|
|
@@ -127,6 +122,29 @@ def insert_spaces_between_uppercase(s):
|
|
| 127 |
)
|
| 128 |
|
| 129 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 130 |
def ensure_suffix(a: str, b: str, c: str):
|
| 131 |
a = a.strip()
|
| 132 |
if not a.endswith(b):
|
|
@@ -171,6 +189,7 @@ def sentence_normalize(sentence_text: str):
|
|
| 171 |
sentences = tx.normalize(part)
|
| 172 |
dest_text = ""
|
| 173 |
for sentence in sentences:
|
|
|
|
| 174 |
dest_text += sentence
|
| 175 |
return dest_text
|
| 176 |
|
|
@@ -197,7 +216,6 @@ def text_normalize(text, is_end=False):
|
|
| 197 |
lines = [line for line in lines if line]
|
| 198 |
lines = [sentence_normalize(line) for line in lines]
|
| 199 |
content = "\n".join(lines)
|
| 200 |
-
content = apply_post_normalize(content)
|
| 201 |
return content
|
| 202 |
|
| 203 |
|
|
@@ -216,6 +234,16 @@ console.log('1')
|
|
| 216 |
|
| 217 |
*一条文本*
|
| 218 |
""",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 219 |
]
|
| 220 |
|
| 221 |
for i, test_case in enumerate(test_cases):
|
|
|
|
| 75 |
"“": " ",
|
| 76 |
"’": " ",
|
| 77 |
"”": " ",
|
| 78 |
+
'"': " ",
|
| 79 |
+
"'": " ",
|
| 80 |
":": ",",
|
| 81 |
";": ",",
|
| 82 |
"!": ".",
|
| 83 |
"(": ",",
|
| 84 |
")": ",",
|
| 85 |
+
"[": ",",
|
| 86 |
+
"]": ",",
|
| 87 |
">": ",",
|
| 88 |
"<": ",",
|
| 89 |
"-": ",",
|
|
|
|
| 112 |
return emojiswitch.demojize(text, delimiters=("", ""), lang="zh")
|
| 113 |
|
| 114 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 115 |
@post_normalize()
|
| 116 |
def insert_spaces_between_uppercase(s):
|
| 117 |
# 使用正则表达式在每个相邻的大写字母之间插入空格
|
|
|
|
| 122 |
)
|
| 123 |
|
| 124 |
|
| 125 |
+
@pre_normalize()
|
| 126 |
+
def apply_markdown_to_text(text):
|
| 127 |
+
if is_markdown(text):
|
| 128 |
+
text = markdown_to_text(text)
|
| 129 |
+
return text
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
# 将 "xxx" => \nxxx\n
|
| 133 |
+
# 将 'xxx' => \nxxx\n
|
| 134 |
+
@pre_normalize()
|
| 135 |
+
def replace_quotes(text):
|
| 136 |
+
repl = r"\n\1\n"
|
| 137 |
+
patterns = [
|
| 138 |
+
['"', '"'],
|
| 139 |
+
["'", "'"],
|
| 140 |
+
["“", "”"],
|
| 141 |
+
["‘", "’"],
|
| 142 |
+
]
|
| 143 |
+
for p in patterns:
|
| 144 |
+
text = re.sub(rf"({p[0]}[^{p[0]}{p[1]}]+?{p[1]})", repl, text)
|
| 145 |
+
return text
|
| 146 |
+
|
| 147 |
+
|
| 148 |
def ensure_suffix(a: str, b: str, c: str):
|
| 149 |
a = a.strip()
|
| 150 |
if not a.endswith(b):
|
|
|
|
| 189 |
sentences = tx.normalize(part)
|
| 190 |
dest_text = ""
|
| 191 |
for sentence in sentences:
|
| 192 |
+
sentence = apply_post_normalize(sentence)
|
| 193 |
dest_text += sentence
|
| 194 |
return dest_text
|
| 195 |
|
|
|
|
| 216 |
lines = [line for line in lines if line]
|
| 217 |
lines = [sentence_normalize(line) for line in lines]
|
| 218 |
content = "\n".join(lines)
|
|
|
|
| 219 |
return content
|
| 220 |
|
| 221 |
|
|
|
|
| 234 |
|
| 235 |
*一条文本*
|
| 236 |
""",
|
| 237 |
+
"""
|
| 238 |
+
在沙漠、岩石、雪地上行走了很长的时间以后,小王子终于发现了一条大路。所有的大路都是通往人住的地方的。
|
| 239 |
+
“你们好。”小王子说。
|
| 240 |
+
这是一个玫瑰盛开的花园。
|
| 241 |
+
“你好。”玫瑰花说道。
|
| 242 |
+
小王子瞅着这些花,它们全都和他的那朵花一样。
|
| 243 |
+
“你们是什么花?”小王子惊奇地问。
|
| 244 |
+
“我们是玫瑰花。”花儿们说道。
|
| 245 |
+
“啊!”小王子说……。
|
| 246 |
+
""",
|
| 247 |
]
|
| 248 |
|
| 249 |
for i, test_case in enumerate(test_cases):
|
modules/utils/audio.py
CHANGED
|
@@ -5,6 +5,16 @@ import pyrubberband as pyrb
|
|
| 5 |
import numpy as np
|
| 6 |
from io import BytesIO
|
| 7 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
|
| 9 |
def audiosegment_to_librosawav(audiosegment):
|
| 10 |
channel_sounds = audiosegment.split_to_mono()
|
|
|
|
| 5 |
import numpy as np
|
| 6 |
from io import BytesIO
|
| 7 |
|
| 8 |
+
INT16_MAX = np.iinfo(np.int16).max
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def audio_to_int16(audio_data):
|
| 12 |
+
if audio_data.dtype == np.float32:
|
| 13 |
+
audio_data = (audio_data * INT16_MAX).astype(np.int16)
|
| 14 |
+
if audio_data.dtype == np.float16:
|
| 15 |
+
audio_data = (audio_data * INT16_MAX).astype(np.int16)
|
| 16 |
+
return audio_data
|
| 17 |
+
|
| 18 |
|
| 19 |
def audiosegment_to_librosawav(audiosegment):
|
| 20 |
channel_sounds = audiosegment.split_to_mono()
|
webui.py
CHANGED
|
@@ -1,4 +1,16 @@
|
|
| 1 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
import os
|
| 3 |
import logging
|
| 4 |
|
|
@@ -29,7 +41,7 @@ from modules.api.utils import calc_spk_style
|
|
| 29 |
from modules.normalization import text_normalize
|
| 30 |
from modules import refiner, config
|
| 31 |
|
| 32 |
-
from modules.utils import env
|
| 33 |
from modules.SentenceSplitter import SentenceSplitter
|
| 34 |
|
| 35 |
torch._dynamo.config.cache_size_limit = 64
|
|
@@ -40,7 +52,7 @@ webui_config = {
|
|
| 40 |
"tts_max": 1000,
|
| 41 |
"ssml_max": 5000,
|
| 42 |
"spliter_threshold": 100,
|
| 43 |
-
"max_batch_size":
|
| 44 |
}
|
| 45 |
|
| 46 |
|
|
@@ -65,7 +77,7 @@ def segments_length_limit(segments, total_max: int):
|
|
| 65 |
|
| 66 |
@torch.inference_mode()
|
| 67 |
@spaces.GPU
|
| 68 |
-
def synthesize_ssml(ssml: str, batch_size=
|
| 69 |
try:
|
| 70 |
batch_size = int(batch_size)
|
| 71 |
except Exception:
|
|
@@ -92,7 +104,10 @@ def synthesize_ssml(ssml: str, batch_size=8):
|
|
| 92 |
|
| 93 |
buffer.seek(0)
|
| 94 |
|
| 95 |
-
|
|
|
|
|
|
|
|
|
|
| 96 |
|
| 97 |
|
| 98 |
@torch.inference_mode()
|
|
@@ -110,12 +125,12 @@ def tts_generate(
|
|
| 110 |
prefix,
|
| 111 |
style,
|
| 112 |
disable_normalize=False,
|
| 113 |
-
batch_size=
|
| 114 |
):
|
| 115 |
try:
|
| 116 |
batch_size = int(batch_size)
|
| 117 |
except Exception:
|
| 118 |
-
batch_size =
|
| 119 |
|
| 120 |
max_len = webui_config["tts_max"]
|
| 121 |
text = text.strip()[0:max_len]
|
|
@@ -157,8 +172,6 @@ def tts_generate(
|
|
| 157 |
prompt2=prompt2,
|
| 158 |
prefix=prefix,
|
| 159 |
)
|
| 160 |
-
|
| 161 |
-
return sample_rate, audio_data
|
| 162 |
else:
|
| 163 |
spliter = SentenceSplitter(webui_config["spliter_threshold"])
|
| 164 |
sentences = spliter.parse(text)
|
|
@@ -178,7 +191,8 @@ def tts_generate(
|
|
| 178 |
sample_rate = audio_data_batch[0][0]
|
| 179 |
audio_data = np.concatenate([data for _, data in audio_data_batch])
|
| 180 |
|
| 181 |
-
|
|
|
|
| 182 |
|
| 183 |
|
| 184 |
@torch.inference_mode()
|
|
@@ -366,7 +380,7 @@ def create_tts_interface():
|
|
| 366 |
batch_size_input = gr.Slider(
|
| 367 |
1,
|
| 368 |
webui_config["max_batch_size"],
|
| 369 |
-
value=
|
| 370 |
step=1,
|
| 371 |
label="Batch Size",
|
| 372 |
)
|
|
@@ -593,7 +607,7 @@ def create_ssml_interface():
|
|
| 593 |
# batch size
|
| 594 |
batch_size_input = gr.Slider(
|
| 595 |
label="Batch Size",
|
| 596 |
-
value=
|
| 597 |
minimum=1,
|
| 598 |
maximum=webui_config["max_batch_size"],
|
| 599 |
step=1,
|
|
@@ -892,7 +906,7 @@ if __name__ == "__main__":
|
|
| 892 |
|
| 893 |
webui_config["tts_max"] = env.get_env_or_arg(args, "tts_max_len", 1000, int)
|
| 894 |
webui_config["ssml_max"] = env.get_env_or_arg(args, "ssml_max_len", 5000, int)
|
| 895 |
-
webui_config["max_batch_size"] = env.get_env_or_arg(args, "max_batch_size",
|
| 896 |
|
| 897 |
demo = create_interface()
|
| 898 |
|
|
|
|
| 1 |
+
try:
|
| 2 |
+
import spaces
|
| 3 |
+
except:
|
| 4 |
+
|
| 5 |
+
class NoneSpaces:
|
| 6 |
+
def __init__(self):
|
| 7 |
+
pass
|
| 8 |
+
|
| 9 |
+
def GPU(self, fn):
|
| 10 |
+
return fn
|
| 11 |
+
|
| 12 |
+
spaces = NoneSpaces()
|
| 13 |
+
|
| 14 |
import os
|
| 15 |
import logging
|
| 16 |
|
|
|
|
| 41 |
from modules.normalization import text_normalize
|
| 42 |
from modules import refiner, config
|
| 43 |
|
| 44 |
+
from modules.utils import env, audio
|
| 45 |
from modules.SentenceSplitter import SentenceSplitter
|
| 46 |
|
| 47 |
torch._dynamo.config.cache_size_limit = 64
|
|
|
|
| 52 |
"tts_max": 1000,
|
| 53 |
"ssml_max": 5000,
|
| 54 |
"spliter_threshold": 100,
|
| 55 |
+
"max_batch_size": 8,
|
| 56 |
}
|
| 57 |
|
| 58 |
|
|
|
|
| 77 |
|
| 78 |
@torch.inference_mode()
|
| 79 |
@spaces.GPU
|
| 80 |
+
def synthesize_ssml(ssml: str, batch_size=4):
|
| 81 |
try:
|
| 82 |
batch_size = int(batch_size)
|
| 83 |
except Exception:
|
|
|
|
| 104 |
|
| 105 |
buffer.seek(0)
|
| 106 |
|
| 107 |
+
audio_data = buffer.read()
|
| 108 |
+
audio_data = audio.audio_to_int16(audio_data)
|
| 109 |
+
|
| 110 |
+
return audio_data
|
| 111 |
|
| 112 |
|
| 113 |
@torch.inference_mode()
|
|
|
|
| 125 |
prefix,
|
| 126 |
style,
|
| 127 |
disable_normalize=False,
|
| 128 |
+
batch_size=4,
|
| 129 |
):
|
| 130 |
try:
|
| 131 |
batch_size = int(batch_size)
|
| 132 |
except Exception:
|
| 133 |
+
batch_size = 4
|
| 134 |
|
| 135 |
max_len = webui_config["tts_max"]
|
| 136 |
text = text.strip()[0:max_len]
|
|
|
|
| 172 |
prompt2=prompt2,
|
| 173 |
prefix=prefix,
|
| 174 |
)
|
|
|
|
|
|
|
| 175 |
else:
|
| 176 |
spliter = SentenceSplitter(webui_config["spliter_threshold"])
|
| 177 |
sentences = spliter.parse(text)
|
|
|
|
| 191 |
sample_rate = audio_data_batch[0][0]
|
| 192 |
audio_data = np.concatenate([data for _, data in audio_data_batch])
|
| 193 |
|
| 194 |
+
audio_data = audio.audio_to_int16(audio_data)
|
| 195 |
+
return sample_rate, audio_data
|
| 196 |
|
| 197 |
|
| 198 |
@torch.inference_mode()
|
|
|
|
| 380 |
batch_size_input = gr.Slider(
|
| 381 |
1,
|
| 382 |
webui_config["max_batch_size"],
|
| 383 |
+
value=4,
|
| 384 |
step=1,
|
| 385 |
label="Batch Size",
|
| 386 |
)
|
|
|
|
| 607 |
# batch size
|
| 608 |
batch_size_input = gr.Slider(
|
| 609 |
label="Batch Size",
|
| 610 |
+
value=4,
|
| 611 |
minimum=1,
|
| 612 |
maximum=webui_config["max_batch_size"],
|
| 613 |
step=1,
|
|
|
|
| 906 |
|
| 907 |
webui_config["tts_max"] = env.get_env_or_arg(args, "tts_max_len", 1000, int)
|
| 908 |
webui_config["ssml_max"] = env.get_env_or_arg(args, "ssml_max_len", 5000, int)
|
| 909 |
+
webui_config["max_batch_size"] = env.get_env_or_arg(args, "max_batch_size", 8, int)
|
| 910 |
|
| 911 |
demo = create_interface()
|
| 912 |
|