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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 @@
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import torch
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device("cuda"):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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modules/generate_audio.py
CHANGED
@@ -8,6 +8,8 @@ from modules import models, config
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import logging
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logger = logging.getLogger(__name__)
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@@ -96,6 +98,8 @@ def generate_audio_batch(
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sample_rate = 24000
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return [(sample_rate, np.array(wav).flatten().astype(np.float32)) for wav in wavs]
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import logging
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from modules import devices
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logger = logging.getLogger(__name__)
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sample_rate = 24000
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devices.torch_gc()
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return [(sample_rate, np.array(wav).flatten().astype(np.float32)) for wav in wavs]
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modules/normalization.py
CHANGED
@@ -75,13 +75,15 @@ character_map = {
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"“": " ",
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"’": " ",
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"”": " ",
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":": ",",
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";": ",",
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"!": ".",
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"(": ",",
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")": ",",
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-
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-
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">": ",",
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"<": ",",
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"-": ",",
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@@ -110,13 +112,6 @@ def apply_emoji_map(text):
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return emojiswitch.demojize(text, delimiters=("", ""), lang="zh")
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@pre_normalize()
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def apply_markdown_to_text(text):
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if is_markdown(text):
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text = markdown_to_text(text)
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return text
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@post_normalize()
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def insert_spaces_between_uppercase(s):
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# 使用正则表达式在每个相邻的大写字母之间插入空格
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@@ -127,6 +122,29 @@ def insert_spaces_between_uppercase(s):
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)
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def ensure_suffix(a: str, b: str, c: str):
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a = a.strip()
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if not a.endswith(b):
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@@ -171,6 +189,7 @@ def sentence_normalize(sentence_text: str):
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sentences = tx.normalize(part)
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dest_text = ""
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for sentence in sentences:
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dest_text += sentence
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return dest_text
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@@ -197,7 +216,6 @@ def text_normalize(text, is_end=False):
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lines = [line for line in lines if line]
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lines = [sentence_normalize(line) for line in lines]
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content = "\n".join(lines)
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content = apply_post_normalize(content)
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return content
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@@ -216,6 +234,16 @@ console.log('1')
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*一条文本*
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""",
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]
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for i, test_case in enumerate(test_cases):
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"“": " ",
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"’": " ",
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"”": " ",
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'"': " ",
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"'": " ",
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":": ",",
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";": ",",
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"!": ".",
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"(": ",",
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")": ",",
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"[": ",",
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"]": ",",
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">": ",",
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"<": ",",
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"-": ",",
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return emojiswitch.demojize(text, delimiters=("", ""), lang="zh")
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@post_normalize()
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def insert_spaces_between_uppercase(s):
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# 使用正则表达式在每个相邻的大写字母之间插入空格
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)
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@pre_normalize()
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def apply_markdown_to_text(text):
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if is_markdown(text):
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text = markdown_to_text(text)
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return text
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# 将 "xxx" => \nxxx\n
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# 将 'xxx' => \nxxx\n
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@pre_normalize()
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def replace_quotes(text):
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repl = r"\n\1\n"
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patterns = [
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['"', '"'],
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["'", "'"],
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["“", "”"],
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["‘", "’"],
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]
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for p in patterns:
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text = re.sub(rf"({p[0]}[^{p[0]}{p[1]}]+?{p[1]})", repl, text)
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return text
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def ensure_suffix(a: str, b: str, c: str):
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a = a.strip()
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if not a.endswith(b):
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sentences = tx.normalize(part)
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dest_text = ""
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for sentence in sentences:
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sentence = apply_post_normalize(sentence)
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dest_text += sentence
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return dest_text
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lines = [line for line in lines if line]
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lines = [sentence_normalize(line) for line in lines]
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content = "\n".join(lines)
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return content
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*一条文本*
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""",
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"""
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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。
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“你们好。”小王子说。
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这是一个玫瑰盛开的花园。
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“你好。”玫瑰花说道。
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小王子瞅着这些花,它们全都和他的那朵花一样。
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“你们是什么花?”小王子惊奇地问。
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“我们是玫瑰花。”花儿们说道。
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“啊!”小王子说……。
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""",
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]
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for i, test_case in enumerate(test_cases):
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modules/utils/audio.py
CHANGED
@@ -5,6 +5,16 @@ import pyrubberband as pyrb
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import numpy as np
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from io import BytesIO
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def audiosegment_to_librosawav(audiosegment):
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channel_sounds = audiosegment.split_to_mono()
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import numpy as np
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from io import BytesIO
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INT16_MAX = np.iinfo(np.int16).max
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def audio_to_int16(audio_data):
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if audio_data.dtype == np.float32:
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audio_data = (audio_data * INT16_MAX).astype(np.int16)
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if audio_data.dtype == np.float16:
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audio_data = (audio_data * INT16_MAX).astype(np.int16)
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return audio_data
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def audiosegment_to_librosawav(audiosegment):
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channel_sounds = audiosegment.split_to_mono()
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webui.py
CHANGED
@@ -1,4 +1,16 @@
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import os
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import logging
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@@ -29,7 +41,7 @@ from modules.api.utils import calc_spk_style
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from modules.normalization import text_normalize
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from modules import refiner, config
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from modules.utils import env
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from modules.SentenceSplitter import SentenceSplitter
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torch._dynamo.config.cache_size_limit = 64
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"tts_max": 1000,
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"ssml_max": 5000,
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"spliter_threshold": 100,
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"max_batch_size":
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}
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@torch.inference_mode()
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@spaces.GPU
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def synthesize_ssml(ssml: str, batch_size=
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try:
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batch_size = int(batch_size)
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except Exception:
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buffer.seek(0)
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@torch.inference_mode()
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prefix,
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style,
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disable_normalize=False,
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batch_size=
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):
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try:
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batch_size = int(batch_size)
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except Exception:
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batch_size =
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max_len = webui_config["tts_max"]
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text = text.strip()[0:max_len]
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prompt2=prompt2,
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prefix=prefix,
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)
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return sample_rate, audio_data
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else:
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spliter = SentenceSplitter(webui_config["spliter_threshold"])
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sentences = spliter.parse(text)
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sample_rate = audio_data_batch[0][0]
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audio_data = np.concatenate([data for _, data in audio_data_batch])
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@torch.inference_mode()
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batch_size_input = gr.Slider(
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1,
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webui_config["max_batch_size"],
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value=
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step=1,
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label="Batch Size",
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)
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# batch size
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batch_size_input = gr.Slider(
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label="Batch Size",
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value=
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minimum=1,
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maximum=webui_config["max_batch_size"],
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step=1,
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webui_config["tts_max"] = env.get_env_or_arg(args, "tts_max_len", 1000, int)
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webui_config["ssml_max"] = env.get_env_or_arg(args, "ssml_max_len", 5000, int)
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webui_config["max_batch_size"] = env.get_env_or_arg(args, "max_batch_size",
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demo = create_interface()
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try:
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import spaces
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except:
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class NoneSpaces:
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def __init__(self):
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pass
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def GPU(self, fn):
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return fn
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spaces = NoneSpaces()
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import os
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import logging
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from modules.normalization import text_normalize
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from modules import refiner, config
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from modules.utils import env, audio
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from modules.SentenceSplitter import SentenceSplitter
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torch._dynamo.config.cache_size_limit = 64
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"tts_max": 1000,
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"ssml_max": 5000,
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"spliter_threshold": 100,
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"max_batch_size": 8,
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}
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@torch.inference_mode()
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@spaces.GPU
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def synthesize_ssml(ssml: str, batch_size=4):
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try:
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batch_size = int(batch_size)
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except Exception:
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buffer.seek(0)
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audio_data = buffer.read()
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audio_data = audio.audio_to_int16(audio_data)
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return audio_data
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@torch.inference_mode()
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prefix,
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style,
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disable_normalize=False,
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batch_size=4,
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):
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try:
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batch_size = int(batch_size)
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except Exception:
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batch_size = 4
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max_len = webui_config["tts_max"]
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text = text.strip()[0:max_len]
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prompt2=prompt2,
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prefix=prefix,
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)
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else:
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spliter = SentenceSplitter(webui_config["spliter_threshold"])
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sentences = spliter.parse(text)
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sample_rate = audio_data_batch[0][0]
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audio_data = np.concatenate([data for _, data in audio_data_batch])
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audio_data = audio.audio_to_int16(audio_data)
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return sample_rate, audio_data
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@torch.inference_mode()
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batch_size_input = gr.Slider(
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1,
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webui_config["max_batch_size"],
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value=4,
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step=1,
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label="Batch Size",
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)
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# batch size
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batch_size_input = gr.Slider(
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label="Batch Size",
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value=4,
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minimum=1,
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maximum=webui_config["max_batch_size"],
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step=1,
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webui_config["tts_max"] = env.get_env_or_arg(args, "tts_max_len", 1000, int)
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webui_config["ssml_max"] = env.get_env_or_arg(args, "ssml_max_len", 5000, int)
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webui_config["max_batch_size"] = env.get_env_or_arg(args, "max_batch_size", 8, int)
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demo = create_interface()
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