File size: 7,607 Bytes
0b85fb9 |
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 |
from collections.abc import Sequence
from typing import Any
import numpy as np
import onnxruntime
from numpy.typing import NDArray
from pyopenjtalk import OpenJTalk
from style_bert_vits2.constants import Languages
from style_bert_vits2.models.hyper_parameters import HyperParameters
from style_bert_vits2.nlp import (
clean_text_with_given_phone_tone,
cleaned_text_to_sequence,
extract_bert_feature_onnx,
)
from style_bert_vits2.utils import get_onnx_device_options
def __intersperse(lst: list[Any], item: Any) -> list[Any]:
"""
リストの要素の間に特定のアイテムを挿入する
style_bert_vits2.models.commons.intersperse と同一実装
style_bert_vits2.models.commons モジュールは PyTorch に依存しているため、ONNX 推論時は import できない
Args:
lst (list[Any]): 元のリスト
item (Any): 挿入するアイテム
Returns:
list[Any]: 新しいリスト
"""
result = [item] * (len(lst) * 2 + 1)
result[1::2] = lst
return result
def get_text_onnx(
text: str,
language_str: Languages,
hps: HyperParameters,
onnx_providers: Sequence[str | tuple[str, dict[str, Any]]],
assist_text: str | None = None,
assist_text_weight: float = 0.7,
given_phone: list[str] | None = None,
given_tone: list[int] | None = None,
jtalk: OpenJTalk | None = None,
) -> tuple[
NDArray[Any], NDArray[Any], NDArray[Any], NDArray[Any], NDArray[Any], NDArray[Any]
]:
use_jp_extra = hps.version.endswith("JP-Extra")
norm_text, phone, tone, word2ph, sep_text, _, _ = clean_text_with_given_phone_tone(
text,
language_str,
given_phone=given_phone,
given_tone=given_tone,
use_jp_extra=use_jp_extra,
# 推論時のみ呼び出されるので、raise_yomi_error は False に設定
raise_yomi_error=False,
jtalk=jtalk,
)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if hps.data.add_blank:
phone = __intersperse(phone, 0)
tone = __intersperse(tone, 0)
language = __intersperse(language, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
bert_ori = extract_bert_feature_onnx(
norm_text,
word2ph,
language_str,
onnx_providers,
assist_text,
assist_text_weight,
sep_text, # clean_text_with_given_phone_tone() の中間生成物を再利用して効率向上を図る
)
del word2ph
assert bert_ori.shape[-1] == len(phone), phone
if language_str == Languages.ZH:
bert = bert_ori
ja_bert = np.zeros((1024, len(phone)), dtype=np.float32)
en_bert = np.zeros((1024, len(phone)), dtype=np.float32)
elif language_str == Languages.JP:
bert = np.zeros((1024, len(phone)), dtype=np.float32)
ja_bert = bert_ori
en_bert = np.zeros((1024, len(phone)), dtype=np.float32)
elif language_str == Languages.EN:
bert = np.zeros((1024, len(phone)), dtype=np.float32)
ja_bert = np.zeros((1024, len(phone)), dtype=np.float32)
en_bert = bert_ori
else:
raise ValueError("language_str should be ZH, JP or EN")
assert bert.shape[-1] == len(phone), (
f"Bert seq len {bert.shape[-1]} != {len(phone)}"
)
phone = np.array(phone, dtype=np.int64)
tone = np.array(tone, dtype=np.int64)
language = np.array(language, dtype=np.int64)
return bert, ja_bert, en_bert, phone, tone, language
def infer_onnx(
text: str,
style_vec: NDArray[Any],
sdp_ratio: float,
noise_scale: float,
noise_scale_w: float,
length_scale: float,
sid: int, # In the original Bert-VITS2, its speaker_name: str, but here it's id
language: Languages,
hps: HyperParameters,
onnx_session: onnxruntime.InferenceSession,
onnx_providers: Sequence[str | tuple[str, dict[str, Any]]],
skip_start: bool = False,
skip_end: bool = False,
assist_text: str | None = None,
assist_text_weight: float = 0.7,
given_phone: list[str] | None = None,
given_tone: list[int] | None = None,
jtalk: OpenJTalk | None = None,
) -> NDArray[np.float32]:
is_jp_extra = hps.version.endswith("JP-Extra")
bert, ja_bert, en_bert, phones, tones, lang_ids = get_text_onnx(
text,
language,
hps,
onnx_providers=onnx_providers,
assist_text=assist_text,
assist_text_weight=assist_text_weight,
given_phone=given_phone,
given_tone=given_tone,
jtalk=jtalk,
)
if skip_start:
phones = phones[3:]
tones = tones[3:]
lang_ids = lang_ids[3:]
bert = bert[:, 3:]
ja_bert = ja_bert[:, 3:]
en_bert = en_bert[:, 3:]
if skip_end:
phones = phones[:-2]
tones = tones[:-2]
lang_ids = lang_ids[:-2]
bert = bert[:, :-2]
ja_bert = ja_bert[:, :-2]
en_bert = en_bert[:, :-2]
x_tst = np.expand_dims(phones, axis=0)
tones = np.expand_dims(tones, axis=0)
lang_ids = np.expand_dims(lang_ids, axis=0)
bert = np.expand_dims(bert, axis=0)
ja_bert = np.expand_dims(ja_bert, axis=0)
en_bert = np.expand_dims(en_bert, axis=0)
x_tst_lengths = np.array([phones.shape[0]], dtype=np.int64)
style_vec_tensor = np.expand_dims(style_vec, axis=0)
del phones
sid_tensor = np.array([sid], dtype=np.int64)
input_names = [input.name for input in onnx_session.get_inputs()]
output_name = onnx_session.get_outputs()[0].name
if is_jp_extra:
input_tensor = [
x_tst,
x_tst_lengths,
sid_tensor,
tones,
lang_ids,
ja_bert,
style_vec_tensor,
np.array(length_scale, dtype=np.float32),
np.array(sdp_ratio, dtype=np.float32),
np.array(noise_scale, dtype=np.float32),
np.array(noise_scale_w, dtype=np.float32),
]
else:
input_tensor = [
x_tst,
x_tst_lengths,
sid_tensor,
tones,
lang_ids,
bert,
ja_bert,
en_bert,
style_vec_tensor,
np.array(length_scale, dtype=np.float32),
np.array(sdp_ratio, dtype=np.float32),
np.array(noise_scale, dtype=np.float32),
np.array(noise_scale_w, dtype=np.float32),
]
# 入力テンソルの転送に使用するデバイス種別, デバイス ID, 実行オプションを取得
device_type, device_id, run_options = get_onnx_device_options(onnx_session, onnx_providers) # fmt: skip
# 推論デバイスに入力テンソルを割り当て
## GPU 推論の場合、device_type + device_id に対応する GPU デバイスに入力テンソルが割り当てられる
io_binding = onnx_session.io_binding()
for name, value in zip(input_names, input_tensor):
gpu_tensor = onnxruntime.OrtValue.ortvalue_from_numpy(
value, device_type, device_id
)
io_binding.bind_ortvalue_input(name, gpu_tensor)
# 推論の実行
io_binding.bind_output(output_name, device_type)
onnx_session.run_with_iobinding(io_binding, run_options=run_options)
output = io_binding.get_outputs()
audio = output[0].numpy()[0, 0]
del (
x_tst,
tones,
lang_ids,
bert,
x_tst_lengths,
sid_tensor,
ja_bert,
en_bert,
style_vec,
) # , emo
return audio
|