This commit is contained in:
Googlefan
2025-02-22 08:00:17 +00:00
parent 14d631eeaa
commit 506ee4d883
60 changed files with 927 additions and 517 deletions

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@@ -162,5 +162,5 @@ jobs:
push: true push: true
tags: | tags: |
ghcr.io/${{ github.repository }}:${{ matrix.tag }} ghcr.io/${{ github.repository }}:${{ matrix.tag }}
file: docker/${{ matrix.tag }}.Dockerfile file: scripts/docker/${{ matrix.tag }}.Dockerfile
platforms: ${{ matrix.platform }} platforms: ${{ matrix.platform }}

3
.gitignore vendored
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@@ -5,4 +5,5 @@ venv/
.env .env
output.wav output.wav
node_modules node_modules
dist/ dist/
*.csv

815
Cargo.lock generated

File diff suppressed because it is too large Load Diff

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@@ -1,15 +1,16 @@
[workspace] [workspace]
resolver = "2" resolver = "2"
members = ["sbv2_api", "sbv2_core", "sbv2_bindings", "sbv2_wasm"] members = ["./crates/sbv2_api", "./crates/sbv2_core", "./crates/sbv2_bindings", "./crates/sbv2_wasm"]
[workspace.dependencies] [workspace.dependencies]
anyhow = "1.0.86" anyhow = "1.0.96"
dotenvy = "0.15.7" dotenvy = "0.15.7"
env_logger = "0.11.5" env_logger = "0.11.6"
ndarray = "0.16.1" ndarray = "0.16.1"
once_cell = "1.19.0" once_cell = "1.20.3"
[profile.release] [profile.release]
lto = true
debug = false
strip = true strip = true
opt-level = "z"
lto = true
codegen-units = 1

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@@ -17,7 +17,7 @@ JP-Extra しか対応していません。(基本的に対応する予定もあ
## 変換方法 ## 変換方法
[こちら](https://github.com/tuna2134/sbv2-api/tree/main/convert)を参照してください。 [こちら](https://github.com/tuna2134/sbv2-api/tree/main/scripts/convert)を参照してください。
## Todo ## Todo
@@ -119,3 +119,4 @@ curl http://localhost:3000/models
- [litagin02/Style-Bert-VITS2](https://github.com/litagin02/Style-Bert-VITS2) - このコードの書くにあたり、ベースとなる部分を参考にさせていただきました。 - [litagin02/Style-Bert-VITS2](https://github.com/litagin02/Style-Bert-VITS2) - このコードの書くにあたり、ベースとなる部分を参考にさせていただきました。
- [Googlefan](https://github.com/Googlefan256) - 彼にモデルを ONNX ヘ変換および効率化をする方法を教わりました。 - [Googlefan](https://github.com/Googlefan256) - 彼にモデルを ONNX ヘ変換および効率化をする方法を教わりました。
- [Aivis Project](https://github.com/Aivis-Project/AivisSpeech-Engine) - 辞書部分

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@@ -1,14 +0,0 @@
悪徳貴族として名高いヴェレット家の長男――オウガ・ヴェレットは転生者である。
ブラック企業に勤め、過労死した彼には一つの夢があった。
「可愛いハーレム作って、美味い物を食べる。領民の税金で楽して好き放題な生活を送ってみせる!」
素晴らしき異世界ライフを夢見た彼は実現へ向けて、努力を始めた。
ハーレムを築くためにいじめられてる平民の子を助けて恩を売ってやったり。
労働力を手に入れるために多くの孤児を雇って教育したり。
反乱を起きても鎮圧できるように魔法学院へ通って魔法を極める。
「クックック……! 順調、順調! 未来は明るいなぁ!」
――オウガはまだ知らない。
楽な生活を送るためにしてきたことが評価され、世間から『聖者』様として呼ばれる未来を。

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@@ -107,7 +107,7 @@ impl TTSModel {
/// style_vector : StyleVector /// style_vector : StyleVector
/// スタイルベクトル /// スタイルベクトル
fn get_style_vector( fn get_style_vector(
&self, &mut self,
ident: String, ident: String,
style_id: i32, style_id: i32,
weight: f32, weight: f32,
@@ -144,7 +144,7 @@ impl TTSModel {
style_id: i32, style_id: i32,
sdp_ratio: f32, sdp_ratio: f32,
length_scale: f32, length_scale: f32,
) -> anyhow::Result<Bound<PyBytes>> { ) -> anyhow::Result<Bound<'p, PyBytes>> {
let data = self.model.easy_synthesize( let data = self.model.easy_synthesize(
ident.as_str(), ident.as_str(),
&text, &text,

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@@ -13,17 +13,17 @@ anyhow.workspace = true
dotenvy.workspace = true dotenvy.workspace = true
env_logger.workspace = true env_logger.workspace = true
hound = "3.5.1" hound = "3.5.1"
jpreprocess = { version = "0.10.0", features = ["naist-jdic"] } jpreprocess = { version = "0.12.0", features = ["naist-jdic"] }
ndarray.workspace = true ndarray.workspace = true
num_cpus = "1.16.0" num_cpus = "1.16.0"
once_cell.workspace = true once_cell.workspace = true
ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.8", optional = true } ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.9", optional = true }
regex = "1.10.6" regex = "1.10.6"
serde = { version = "1.0.210", features = ["derive"] } serde = { version = "1.0.210", features = ["derive"] }
serde_json = "1.0.128" serde_json = "1.0.128"
tar = "0.4.41" tar = "0.4.41"
thiserror = "1.0.63" thiserror = "2.0.11"
tokenizers = { version = "0.20.0", default-features = false } tokenizers = { version = "0.21.0", default-features = false }
zstd = "0.13.2" zstd = "0.13.2"
[features] [features]
@@ -35,4 +35,4 @@ directml = ["ort/directml", "std"]
tensorrt = ["ort/tensorrt", "std"] tensorrt = ["ort/tensorrt", "std"]
coreml = ["ort/coreml", "std"] coreml = ["ort/coreml", "std"]
default = ["std"] default = ["std"]
no_std = ["tokenizers/unstable_wasm"] no_std = ["tokenizers/unstable_wasm"]

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@@ -1,17 +1,18 @@
use crate::error::Result; use crate::error::Result;
use ndarray::{Array2, Ix2}; use ndarray::{Array2, Ix2};
use ort::Session; use ort::session::Session;
use ort::value::TensorRef;
pub fn predict( pub fn predict(
session: &Session, session: &mut Session,
token_ids: Vec<i64>, token_ids: Vec<i64>,
attention_masks: Vec<i64>, attention_masks: Vec<i64>,
) -> Result<Array2<f32>> { ) -> Result<Array2<f32>> {
let outputs = session.run( let outputs = session.run(
ort::inputs! { ort::inputs! {
"input_ids" => Array2::from_shape_vec((1, token_ids.len()), token_ids).unwrap(), "input_ids" => TensorRef::from_array_view((vec![1, token_ids.len() as i64], token_ids.as_slice()))?,
"attention_mask" => Array2::from_shape_vec((1, attention_masks.len()), attention_masks).unwrap(), "attention_mask" => TensorRef::from_array_view((vec![1, attention_masks.len() as i64], attention_masks.as_slice()))?,
}? }
)?; )?;
let output = outputs["output"] let output = outputs["output"]

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@@ -6,6 +6,8 @@ pub enum Error {
TokenizerError(#[from] tokenizers::Error), TokenizerError(#[from] tokenizers::Error),
#[error("JPreprocess error: {0}")] #[error("JPreprocess error: {0}")]
JPreprocessError(#[from] jpreprocess::error::JPreprocessError), JPreprocessError(#[from] jpreprocess::error::JPreprocessError),
#[error("Lindera error: {0}")]
LinderaError(String),
#[cfg(feature = "std")] #[cfg(feature = "std")]
#[error("ONNX error: {0}")] #[error("ONNX error: {0}")]
OrtError(#[from] ort::Error), OrtError(#[from] ort::Error),

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@@ -1,21 +1,21 @@
use crate::error::{Error, Result}; use crate::error::{Error, Result};
use crate::mora::{MORA_KATA_TO_MORA_PHONEMES, VOWELS}; use crate::mora::{MORA_KATA_TO_MORA_PHONEMES, VOWELS};
use crate::norm::{replace_punctuation, PUNCTUATIONS}; use crate::norm::{replace_punctuation, PUNCTUATIONS};
use jpreprocess::*; use jpreprocess::{kind, DefaultTokenizer, JPreprocess, SystemDictionaryConfig, UserDictionary};
use once_cell::sync::Lazy; use once_cell::sync::Lazy;
use regex::Regex; use regex::Regex;
use std::cmp::Reverse; use std::cmp::Reverse;
use std::collections::HashSet; use std::collections::HashSet;
use std::sync::Arc; use std::sync::Arc;
type JPreprocessType = JPreprocess<DefaultFetcher>; type JPreprocessType = JPreprocess<DefaultTokenizer>;
fn initialize_jtalk() -> Result<JPreprocessType> { fn initialize_jtalk() -> Result<JPreprocessType> {
let config = JPreprocessConfig { let sdic =
dictionary: SystemDictionaryConfig::Bundled(kind::JPreprocessDictionaryKind::NaistJdic), SystemDictionaryConfig::Bundled(kind::JPreprocessDictionaryKind::NaistJdic).load()?;
user_dictionary: None, let u = UserDictionary::load(include_bytes!("./dic/all.dic/all.bin"))
}; .map_err(|e| Error::LinderaError(e.to_string()))?;
let jpreprocess = JPreprocess::from_config(config)?; let jpreprocess = JPreprocess::with_dictionaries(sdic, Some(u));
Ok(jpreprocess) Ok(jpreprocess)
} }

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@@ -6,7 +6,7 @@ fn main_inner() -> anyhow::Result<()> {
use sbv2_core::tts; use sbv2_core::tts;
dotenvy::dotenv_override().ok(); dotenvy::dotenv_override().ok();
env_logger::init(); env_logger::init();
let text = fs::read_to_string("content.txt")?; let text = "今日の天気は快晴です。";
let ident = "aaa"; let ident = "aaa";
let mut tts_holder = tts::TTSModelHolder::new( let mut tts_holder = tts::TTSModelHolder::new(
&fs::read(env::var("BERT_MODEL_PATH")?)?, &fs::read(env::var("BERT_MODEL_PATH")?)?,

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@@ -0,0 +1,101 @@
use crate::error::Result;
use ndarray::{array, Array1, Array2, Array3, Axis, Ix3};
use ort::session::{builder::GraphOptimizationLevel, Session};
#[allow(clippy::vec_init_then_push, unused_variables)]
pub fn load_model<P: AsRef<[u8]>>(model_file: P, bert: bool) -> Result<Session> {
let mut exp = Vec::new();
#[cfg(feature = "tensorrt")]
{
if bert {
exp.push(
ort::execution_providers::TensorRTExecutionProvider::default()
.with_fp16(true)
.with_profile_min_shapes("input_ids:1x1,attention_mask:1x1")
.with_profile_max_shapes("input_ids:1x100,attention_mask:1x100")
.with_profile_opt_shapes("input_ids:1x25,attention_mask:1x25")
.build(),
);
}
}
#[cfg(feature = "cuda")]
{
#[allow(unused_mut)]
let mut cuda = ort::execution_providers::CUDAExecutionProvider::default()
.with_conv_algorithm_search(
ort::execution_providers::CUDAExecutionProviderCuDNNConvAlgoSearch::Default,
);
#[cfg(feature = "cuda_tf32")]
{
cuda = cuda.with_tf32(true);
}
exp.push(cuda.build());
}
#[cfg(feature = "directml")]
{
exp.push(ort::execution_providers::DirectMLExecutionProvider::default().build());
}
#[cfg(feature = "coreml")]
{
exp.push(ort::execution_providers::CoreMLExecutionProvider::default().build());
}
exp.push(ort::execution_providers::CPUExecutionProvider::default().build());
Ok(Session::builder()?
.with_execution_providers(exp)?
.with_optimization_level(GraphOptimizationLevel::Level3)?
.with_intra_threads(num_cpus::get_physical())?
.with_parallel_execution(true)?
.with_inter_threads(num_cpus::get_physical())?
.commit_from_memory(model_file.as_ref())?)
}
#[allow(clippy::too_many_arguments)]
pub fn synthesize(
session: &mut Session,
bert_ori: Array2<f32>,
x_tst: Array1<i64>,
tones: Array1<i64>,
lang_ids: Array1<i64>,
style_vector: Array1<f32>,
sdp_ratio: f32,
length_scale: f32,
) -> Result<Array3<f32>> {
let bert_ori = bert_ori.insert_axis(Axis(0));
let bert_ori = bert_ori.as_standard_layout();
let bert = ort::value::TensorRef::from_array_view(&bert_ori)?;
let mut x_tst_lengths = array![x_tst.shape()[0] as i64];
let x_tst_lengths = ort::value::TensorRef::from_array_view(&mut x_tst_lengths)?;
let mut x_tst = x_tst.insert_axis(Axis(0));
let x_tst = ort::value::TensorRef::from_array_view(&mut x_tst)?;
let mut lang_ids = lang_ids.insert_axis(Axis(0));
let lang_ids = ort::value::TensorRef::from_array_view(&mut lang_ids)?;
let mut tones = tones.insert_axis(Axis(0));
let tones = ort::value::TensorRef::from_array_view(&mut tones)?;
let mut style_vector = style_vector.insert_axis(Axis(0));
let style_vector = ort::value::TensorRef::from_array_view(&mut style_vector)?;
let sid = vec![1_i64];
let sid = ort::value::TensorRef::from_array_view((vec![1_i64], sid.as_slice()))?;
let sdp_ratio = vec![sdp_ratio];
let sdp_ratio = ort::value::TensorRef::from_array_view((vec![1_i64], sdp_ratio.as_slice()))?;
let length_scale = vec![length_scale];
let length_scale =
ort::value::TensorRef::from_array_view((vec![1_i64], length_scale.as_slice()))?;
let outputs = session.run(ort::inputs! {
"x_tst" => x_tst,
"x_tst_lengths" => x_tst_lengths,
"sid" => sid,
"tones" => tones,
"language" => lang_ids,
"bert" => bert,
"style_vec" => style_vector,
"sdp_ratio" => sdp_ratio,
"length_scale" => length_scale,
})?;
let audio_array = outputs["output"]
.try_extract_tensor::<f32>()?
.into_dimensionality::<Ix3>()?
.to_owned();
Ok(audio_array)
}

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@@ -1,7 +1,7 @@
use crate::error::{Error, Result}; use crate::error::{Error, Result};
use crate::{jtalk, model, style, tokenizer, tts_util}; use crate::{jtalk, model, style, tokenizer, tts_util};
use ndarray::{concatenate, Array1, Array2, Array3, Axis}; use ndarray::{concatenate, Array1, Array2, Array3, Axis};
use ort::Session; use ort::session::Session;
use tokenizers::Tokenizer; use tokenizers::Tokenizer;
#[derive(PartialEq, Eq, Clone)] #[derive(PartialEq, Eq, Clone)]
@@ -147,7 +147,7 @@ impl TTSModelHolder {
/// This function is for low-level usage, use `easy_synthesize` for high-level usage. /// This function is for low-level usage, use `easy_synthesize` for high-level usage.
#[allow(clippy::type_complexity)] #[allow(clippy::type_complexity)]
pub fn parse_text( pub fn parse_text(
&self, &mut self,
text: &str, text: &str,
) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> { ) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> {
crate::tts_util::parse_text_blocking( crate::tts_util::parse_text_blocking(
@@ -155,15 +155,15 @@ impl TTSModelHolder {
&self.jtalk, &self.jtalk,
&self.tokenizer, &self.tokenizer,
|token_ids, attention_masks| { |token_ids, attention_masks| {
crate::bert::predict(&self.bert, token_ids, attention_masks) crate::bert::predict(&mut self.bert, token_ids, attention_masks)
}, },
) )
} }
fn find_model<I: Into<TTSIdent>>(&self, ident: I) -> Result<&TTSModel> { fn find_model<I: Into<TTSIdent>>(&mut self, ident: I) -> Result<&mut TTSModel> {
let ident = ident.into(); let ident = ident.into();
self.models self.models
.iter() .iter_mut()
.find(|m| m.ident == ident) .find(|m| m.ident == ident)
.ok_or(Error::ModelNotFoundError(ident.to_string())) .ok_or(Error::ModelNotFoundError(ident.to_string()))
} }
@@ -209,7 +209,7 @@ impl TTSModelHolder {
/// # Note /// # Note
/// This function is for low-level usage, use `easy_synthesize` for high-level usage. /// This function is for low-level usage, use `easy_synthesize` for high-level usage.
pub fn get_style_vector<I: Into<TTSIdent>>( pub fn get_style_vector<I: Into<TTSIdent>>(
&self, &mut self,
ident: I, ident: I,
style_id: i32, style_id: i32,
weight: f32, weight: f32,
@@ -232,11 +232,6 @@ impl TTSModelHolder {
options: SynthesizeOptions, options: SynthesizeOptions,
) -> Result<Vec<u8>> { ) -> Result<Vec<u8>> {
self.find_and_load_model(ident)?; self.find_and_load_model(ident)?;
let vits2 = &self
.find_model(ident)?
.vits2
.as_ref()
.ok_or(Error::ModelNotFoundError(ident.into().to_string()))?;
let style_vector = self.get_style_vector(ident, style_id, options.style_weight)?; let style_vector = self.get_style_vector(ident, style_id, options.style_weight)?;
let audio_array = if options.split_sentences { let audio_array = if options.split_sentences {
let texts: Vec<&str> = text.split('\n').collect(); let texts: Vec<&str> = text.split('\n').collect();
@@ -246,8 +241,14 @@ impl TTSModelHolder {
continue; continue;
} }
let (bert_ori, phones, tones, lang_ids) = self.parse_text(t)?; let (bert_ori, phones, tones, lang_ids) = self.parse_text(t)?;
let vits2 = self
.find_model(ident)?
.vits2
.as_mut()
.ok_or(Error::ModelNotFoundError(ident.into().to_string()))?;
let audio = model::synthesize( let audio = model::synthesize(
&vits2, vits2,
bert_ori.to_owned(), bert_ori.to_owned(),
phones, phones,
tones, tones,
@@ -267,8 +268,14 @@ impl TTSModelHolder {
)? )?
} else { } else {
let (bert_ori, phones, tones, lang_ids) = self.parse_text(text)?; let (bert_ori, phones, tones, lang_ids) = self.parse_text(text)?;
let vits2 = self
.find_model(ident)?
.vits2
.as_mut()
.ok_or(Error::ModelNotFoundError(ident.into().to_string()))?;
model::synthesize( model::synthesize(
&vits2, vits2,
bert_ori.to_owned(), bert_ori.to_owned(),
phones, phones,
tones, tones,
@@ -298,13 +305,13 @@ impl TTSModelHolder {
length_scale: f32, length_scale: f32,
) -> Result<Vec<u8>> { ) -> Result<Vec<u8>> {
self.find_and_load_model(ident)?; self.find_and_load_model(ident)?;
let vits2 = &self let vits2 = self
.find_model(ident)? .find_model(ident)?
.vits2 .vits2
.as_ref() .as_mut()
.ok_or(Error::ModelNotFoundError(ident.into().to_string()))?; .ok_or(Error::ModelNotFoundError(ident.into().to_string()))?;
let audio_array = model::synthesize( let audio_array = model::synthesize(
&vits2, vits2,
bert_ori.to_owned(), bert_ori.to_owned(),
phones, phones,
tones, tones,

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@@ -13,7 +13,3 @@ once_cell.workspace = true
js-sys = "0.3.70" js-sys = "0.3.70"
ndarray.workspace = true ndarray.workspace = true
wasm-bindgen-futures = "0.4.43" wasm-bindgen-futures = "0.4.43"
[profile.release]
lto = true
opt-level = "s"

5
crates/sbv2_wasm/build.sh Executable file
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@@ -0,0 +1,5 @@
wasm-pack build --target web sbv2_wasm
wasm-opt -O3 -o ./sbv2_wasm/pkg/sbv2_wasm_bg.wasm ./sbv2_wasm/pkg/sbv2_wasm_bg.wasm
wasm-strip ./sbv2_wasm/pkg/sbv2_wasm_bg.wasm
mkdir -p ./sbv2_wasm/dist
cp ./sbv2_wasm/sbv2_wasm/pkg/sbv2_wasm_bg.wasm ./sbv2_wasm/dist/sbv2_wasm_bg.wasm

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@@ -13,13 +13,17 @@
"author": "tuna2134", "author": "tuna2134",
"license": "MIT", "license": "MIT",
"devDependencies": { "devDependencies": {
"@biomejs/biome": "^1.9.2", "@biomejs/biome": "^1.9.4",
"@types/node": "^22.7.4", "@types/node": "^22.13.5",
"esbuild": "^0.24.0", "esbuild": "^0.25.0",
"typescript": "^5.6.2" "typescript": "^5.7.3"
}, },
"dependencies": { "dependencies": {
"onnxruntime-web": "^1.19.2" "onnxruntime-web": "^1.20.1"
}, },
"files": ["dist/*", "package.json", "README.md"] "files": [
"dist/*",
"package.json",
"README.md"
]
} }

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@@ -9,21 +9,21 @@ importers:
.: .:
dependencies: dependencies:
onnxruntime-web: onnxruntime-web:
specifier: ^1.19.2 specifier: ^1.20.1
version: 1.20.0 version: 1.20.1
devDependencies: devDependencies:
'@biomejs/biome': '@biomejs/biome':
specifier: ^1.9.2 specifier: ^1.9.4
version: 1.9.4 version: 1.9.4
'@types/node': '@types/node':
specifier: ^22.7.4 specifier: ^22.13.5
version: 22.8.0 version: 22.13.5
esbuild: esbuild:
specifier: ^0.24.0 specifier: ^0.25.0
version: 0.24.0 version: 0.25.0
typescript: typescript:
specifier: ^5.6.2 specifier: ^5.7.3
version: 5.6.3 version: 5.7.3
packages: packages:
@@ -80,146 +80,152 @@ packages:
cpu: [x64] cpu: [x64]
os: [win32] os: [win32]
'@esbuild/aix-ppc64@0.24.0': '@esbuild/aix-ppc64@0.25.0':
resolution: {integrity: sha512-WtKdFM7ls47zkKHFVzMz8opM7LkcsIp9amDUBIAWirg70RM71WRSjdILPsY5Uv1D42ZpUfaPILDlfactHgsRkw==} resolution: {integrity: sha512-O7vun9Sf8DFjH2UtqK8Ku3LkquL9SZL8OLY1T5NZkA34+wG3OQF7cl4Ql8vdNzM6fzBbYfLaiRLIOZ+2FOCgBQ==}
engines: {node: '>=18'} engines: {node: '>=18'}
cpu: [ppc64] cpu: [ppc64]
os: [aix] os: [aix]
'@esbuild/android-arm64@0.24.0': '@esbuild/android-arm64@0.25.0':
resolution: {integrity: sha512-Vsm497xFM7tTIPYK9bNTYJyF/lsP590Qc1WxJdlB6ljCbdZKU9SY8i7+Iin4kyhV/KV5J2rOKsBQbB77Ab7L/w==} resolution: {integrity: sha512-grvv8WncGjDSyUBjN9yHXNt+cq0snxXbDxy5pJtzMKGmmpPxeAmAhWxXI+01lU5rwZomDgD3kJwulEnhTRUd6g==}
engines: {node: '>=18'} engines: {node: '>=18'}
cpu: [arm64] cpu: [arm64]
os: [android] os: [android]
'@esbuild/android-arm@0.24.0': '@esbuild/android-arm@0.25.0':
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'@esbuild/linux-x64@0.24.0': '@esbuild/linux-x64@0.25.0':
optional: true optional: true
'@esbuild/netbsd-x64@0.24.0': '@esbuild/netbsd-arm64@0.25.0':
optional: true optional: true
'@esbuild/openbsd-arm64@0.24.0': '@esbuild/netbsd-x64@0.25.0':
optional: true optional: true
'@esbuild/openbsd-x64@0.24.0': '@esbuild/openbsd-arm64@0.25.0':
optional: true optional: true
'@esbuild/sunos-x64@0.24.0': '@esbuild/openbsd-x64@0.25.0':
optional: true optional: true
'@esbuild/win32-arm64@0.24.0': '@esbuild/sunos-x64@0.25.0':
optional: true optional: true
'@esbuild/win32-ia32@0.24.0': '@esbuild/win32-arm64@0.25.0':
optional: true optional: true
'@esbuild/win32-x64@0.24.0': '@esbuild/win32-ia32@0.25.0':
optional: true
'@esbuild/win32-x64@0.25.0':
optional: true optional: true
'@protobufjs/aspromise@1.1.2': {} '@protobufjs/aspromise@1.1.2': {}
@@ -424,51 +433,52 @@ snapshots:
'@protobufjs/utf8@1.1.0': {} '@protobufjs/utf8@1.1.0': {}
'@types/node@22.8.0': '@types/node@22.13.5':
dependencies: dependencies:
undici-types: 6.19.8 undici-types: 6.20.0
esbuild@0.24.0: esbuild@0.25.0:
optionalDependencies: optionalDependencies:
'@esbuild/aix-ppc64': 0.24.0 '@esbuild/aix-ppc64': 0.25.0
'@esbuild/android-arm': 0.24.0 '@esbuild/android-arm': 0.25.0
'@esbuild/android-arm64': 0.24.0 '@esbuild/android-arm64': 0.25.0
'@esbuild/android-x64': 0.24.0 '@esbuild/android-x64': 0.25.0
'@esbuild/darwin-arm64': 0.24.0 '@esbuild/darwin-arm64': 0.25.0
'@esbuild/darwin-x64': 0.24.0 '@esbuild/darwin-x64': 0.25.0
'@esbuild/freebsd-arm64': 0.24.0 '@esbuild/freebsd-arm64': 0.25.0
'@esbuild/freebsd-x64': 0.24.0 '@esbuild/freebsd-x64': 0.25.0
'@esbuild/linux-arm': 0.24.0 '@esbuild/linux-arm': 0.25.0
'@esbuild/linux-arm64': 0.24.0 '@esbuild/linux-arm64': 0.25.0
'@esbuild/linux-ia32': 0.24.0 '@esbuild/linux-ia32': 0.25.0
'@esbuild/linux-loong64': 0.24.0 '@esbuild/linux-loong64': 0.25.0
'@esbuild/linux-mips64el': 0.24.0 '@esbuild/linux-mips64el': 0.25.0
'@esbuild/linux-ppc64': 0.24.0 '@esbuild/linux-ppc64': 0.25.0
'@esbuild/linux-riscv64': 0.24.0 '@esbuild/linux-riscv64': 0.25.0
'@esbuild/linux-s390x': 0.24.0 '@esbuild/linux-s390x': 0.25.0
'@esbuild/linux-x64': 0.24.0 '@esbuild/linux-x64': 0.25.0
'@esbuild/netbsd-x64': 0.24.0 '@esbuild/netbsd-arm64': 0.25.0
'@esbuild/openbsd-arm64': 0.24.0 '@esbuild/netbsd-x64': 0.25.0
'@esbuild/openbsd-x64': 0.24.0 '@esbuild/openbsd-arm64': 0.25.0
'@esbuild/sunos-x64': 0.24.0 '@esbuild/openbsd-x64': 0.25.0
'@esbuild/win32-arm64': 0.24.0 '@esbuild/sunos-x64': 0.25.0
'@esbuild/win32-ia32': 0.24.0 '@esbuild/win32-arm64': 0.25.0
'@esbuild/win32-x64': 0.24.0 '@esbuild/win32-ia32': 0.25.0
'@esbuild/win32-x64': 0.25.0
flatbuffers@1.12.0: {} flatbuffers@1.12.0: {}
guid-typescript@1.0.9: {} guid-typescript@1.0.9: {}
long@5.2.3: {} long@5.3.1: {}
onnxruntime-common@1.20.0: {} onnxruntime-common@1.20.1: {}
onnxruntime-web@1.20.0: onnxruntime-web@1.20.1:
dependencies: dependencies:
flatbuffers: 1.12.0 flatbuffers: 1.12.0
guid-typescript: 1.0.9 guid-typescript: 1.0.9
long: 5.2.3 long: 5.3.1
onnxruntime-common: 1.20.0 onnxruntime-common: 1.20.1
platform: 1.3.6 platform: 1.3.6
protobufjs: 7.4.0 protobufjs: 7.4.0
@@ -486,9 +496,9 @@ snapshots:
'@protobufjs/path': 1.1.2 '@protobufjs/path': 1.1.2
'@protobufjs/pool': 1.1.0 '@protobufjs/pool': 1.1.0
'@protobufjs/utf8': 1.1.0 '@protobufjs/utf8': 1.1.0
'@types/node': 22.8.0 '@types/node': 22.13.5
long: 5.2.3 long: 5.3.1
typescript@5.6.3: {} typescript@5.7.3: {}
undici-types@6.19.8: {} undici-types@6.20.0: {}

View File

@@ -1,85 +0,0 @@
use crate::error::Result;
use ndarray::{array, Array1, Array2, Array3, Axis, Ix3};
use ort::{GraphOptimizationLevel, Session};
#[allow(clippy::vec_init_then_push, unused_variables)]
pub fn load_model<P: AsRef<[u8]>>(model_file: P, bert: bool) -> Result<Session> {
let mut exp = Vec::new();
#[cfg(feature = "tensorrt")]
{
if bert {
exp.push(
ort::TensorRTExecutionProvider::default()
.with_fp16(true)
.with_profile_min_shapes("input_ids:1x1,attention_mask:1x1")
.with_profile_max_shapes("input_ids:1x100,attention_mask:1x100")
.with_profile_opt_shapes("input_ids:1x25,attention_mask:1x25")
.build(),
);
}
}
#[cfg(feature = "cuda")]
{
#[allow(unused_mut)]
let mut cuda = ort::CUDAExecutionProvider::default()
.with_conv_algorithm_search(ort::CUDAExecutionProviderCuDNNConvAlgoSearch::Default);
#[cfg(feature = "cuda_tf32")]
{
cuda = cuda.with_tf32(true);
}
exp.push(cuda.build());
}
#[cfg(feature = "directml")]
{
exp.push(ort::DirectMLExecutionProvider::default().build());
}
#[cfg(feature = "coreml")]
{
exp.push(ort::CoreMLExecutionProvider::default().build());
}
exp.push(ort::CPUExecutionProvider::default().build());
Ok(Session::builder()?
.with_execution_providers(exp)?
.with_optimization_level(GraphOptimizationLevel::Level3)?
.with_intra_threads(num_cpus::get_physical())?
.with_parallel_execution(true)?
.with_inter_threads(num_cpus::get_physical())?
.commit_from_memory(model_file.as_ref())?)
}
#[allow(clippy::too_many_arguments)]
pub fn synthesize(
session: &Session,
bert_ori: Array2<f32>,
x_tst: Array1<i64>,
tones: Array1<i64>,
lang_ids: Array1<i64>,
style_vector: Array1<f32>,
sdp_ratio: f32,
length_scale: f32,
) -> Result<Array3<f32>> {
let bert = bert_ori.insert_axis(Axis(0));
let x_tst_lengths: Array1<i64> = array![x_tst.shape()[0] as i64];
let x_tst = x_tst.insert_axis(Axis(0));
let lang_ids = lang_ids.insert_axis(Axis(0));
let tones = tones.insert_axis(Axis(0));
let style_vector = style_vector.insert_axis(Axis(0));
let outputs = session.run(ort::inputs! {
"x_tst" => x_tst,
"x_tst_lengths" => x_tst_lengths,
"sid" => array![0_i64],
"tones" => tones,
"language" => lang_ids,
"bert" => bert,
"style_vec" => style_vector,
"sdp_ratio" => array![sdp_ratio],
"length_scale" => array![length_scale],
}?)?;
let audio_array = outputs["output"]
.try_extract_tensor::<f32>()?
.into_dimensionality::<Ix3>()?
.to_owned();
Ok(audio_array)
}

View File

@@ -1,4 +0,0 @@
wasm-pack build --target web sbv2_wasm
wasm-opt -O3 -o sbv2_wasm/pkg/sbv2_wasm_bg.wasm sbv2_wasm/pkg/sbv2_wasm_bg.wasm
mkdir -p sbv2_wasm/dist
cp sbv2_wasm/sbv2_wasm/pkg/sbv2_wasm_bg.wasm sbv2_wasm/dist/sbv2_wasm_bg.wasm

5
scripts/.gitignore vendored Normal file
View File

@@ -0,0 +1,5 @@
*.json
venv/
tmp/
*.safetensors
*.npy

14
scripts/make_dict.sh Executable file
View File

@@ -0,0 +1,14 @@
#!/bin/bash
set -e
git clone https://github.com/Aivis-Project/AivisSpeech-Engine ./scripts/tmp --filter=blob:none -n
cd ./scripts/tmp
git checkout 168b2a1144afe300b0490d9a6dd773ec6e927667 -- resources/dictionaries/*.csv
cd ../..
rm -rf ./crates/sbv2_core/src/dic
cp -r ./scripts/tmp/resources/dictionaries ./crates/sbv2_core/src/dic
rm -rf ./scripts/tmp
for file in ./crates/sbv2_core/src/dic/0*.csv; do
/usr/bin/cat "$file"
echo
done > ./crates/sbv2_core/src/all.csv
lindera build ./crates/sbv2_core/src/all.csv ./crates/sbv2_core/src/dic/all.dic -u -k ipadic

View File

@@ -16,7 +16,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# 必要なパッケージのインストール\n", "# 必要なパッケージのインストール\n",
"!pip install sbv2_bindings\n", "%pip install sbv2_bindings\n",
"\n", "\n",
"# 必要なモジュールのインポート\n", "# 必要なモジュールのインポート\n",
"import os\n", "import os\n",

View File

@@ -3,10 +3,10 @@ from sbv2_bindings import TTSModel
def main(): def main():
print("Loading models...") print("Loading models...")
model = TTSModel.from_path("../models/debert.onnx", "../models/tokenizer.json") model = TTSModel.from_path("./models/debert.onnx", "./models/tokenizer.json")
print("Models loaded!") print("Models loaded!")
model.load_sbv2file_from_path("amitaro", "../models/amitaro.sbv2") model.load_sbv2file_from_path("amitaro", "./models/amitaro.sbv2")
print("All setup is done!") print("All setup is done!")
style_vector = model.get_style_vector("amitaro", 0, 1.0) style_vector = model.get_style_vector("amitaro", 0, 1.0)