fix

add vibrato feature
2025-12-24 08:19:56 +00:00 · 2024-10-08 09:48:04 +00:00 · 2024-10-08 09:30:16 +00:00
69 changed files with 1735 additions and 2629 deletions
--- a/.env.sample
+++ b/.env.sample
@@ -3,5 +3,4 @@ MODEL_PATH=models/tsukuyomi.sbv2
 MODELS_PATH=models
 TOKENIZER_PATH=models/tokenizer.json
 ADDR=localhost:3000
-RUST_LOG=warn
-HOLDER_MAX_LOADED_MODElS=20
+RUST_LOG=warn
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,3 +0,0 @@
-# These are supported funding model platforms
-
-github: [tuna2134]
--- a/.github/workflows/CI.Dockerfile
+++ b/.github/workflows/CI.Dockerfile
@@ -1,4 +0,0 @@
-FROM ubuntu:latest
-RUN apt update && apt install openssl libssl-dev curl pkg-config software-properties-common -y && add-apt-repository ppa:deadsnakes/ppa && apt update && apt install python3.7 python3.8 python3.9 python3.10 python3.11 python3.12 python3.13 python3-pip python3 -y
-ENV PIP_BREAK_SYSTEM_PACKAGES=1
-RUN mkdir -p /root/.cache/sbv2 && curl https://huggingface.co/neody/sbv2-api-assets/resolve/main/dic/all.bin -o /root/.cache/sbv2/all.bin -L
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -1,35 +1,39 @@
+# This file is autogenerated by maturin v1.7.1
+# To update, run
+#
+#    maturin generate-ci github
+#
 name: CI

 on:
  push:
    branches:
      - main
+      - master
    tags:
      - '*'
  pull_request:
  workflow_dispatch:

 permissions:
-  contents: write
+  contents: read
  id-token: write
-  packages: write

 jobs:
-  python-linux:
+  linux:
    runs-on: ${{ matrix.platform.runner }}
    strategy:
      matrix:
        platform:
          - runner: ubuntu-latest
            target: x86_64
-          - runner: ubuntu-24.04-arm
+          - runner: ubuntu-latest
            target: aarch64
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: 3.x
-      - run: docker build . -f .github/workflows/CI.Dockerfile --tag ci
      - name: Build wheels
        uses: PyO3/maturin-action@v1
        with:
@@ -37,15 +41,14 @@ jobs:
          args: --release --out dist --find-interpreter
          sccache: 'true'
          manylinux: auto
-          container: ci
-          working-directory: ./crates/sbv2_bindings
+          working-directory: sbv2_bindings
      - name: Upload wheels
        uses: actions/upload-artifact@v4
        with:
          name: wheels-linux-${{ matrix.platform.target }}
-          path: ./crates/sbv2_bindings/dist
+          path: sbv2_bindings/dist

-  python-windows:
+  windows:
    runs-on: ${{ matrix.platform.runner }}
    strategy:
      matrix:
@@ -64,18 +67,20 @@ jobs:
          target: ${{ matrix.platform.target }}
          args: --release --out dist --find-interpreter
          sccache: 'true'
-          working-directory: ./crates/sbv2_bindings
+          working-directory: sbv2_bindings
      - name: Upload wheels
        uses: actions/upload-artifact@v4
        with:
          name: wheels-windows-${{ matrix.platform.target }}
-          path: ./crates/sbv2_bindings/dist
+          path: sbv2_bindings/dist

-  python-macos:
+  macos:
    runs-on: ${{ matrix.platform.runner }}
    strategy:
      matrix:
        platform:
+          - runner: macos-12
+            target: x86_64
          - runner: macos-14
            target: aarch64
    steps:
@@ -89,14 +94,14 @@ jobs:
          target: ${{ matrix.platform.target }}
          args: --release --out dist --find-interpreter
          sccache: 'true'
-          working-directory: ./crates/sbv2_bindings
+          working-directory: sbv2_bindings
      - name: Upload wheels
        uses: actions/upload-artifact@v4
        with:
          name: wheels-macos-${{ matrix.platform.target }}
-          path: ./crates/sbv2_bindings/dist
+          path: sbv2_bindings/dist

-  python-sdist:
+  sdist:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
@@ -105,116 +110,57 @@ jobs:
        with:
          command: sdist
          args: --out dist
-          working-directory: ./crates/sbv2_bindings
+          working-directory: sbv2_bindings
      - name: Upload sdist
        uses: actions/upload-artifact@v4
        with:
          name: wheels-sdist
-          path: ./crates/sbv2_bindings/dist
-  python-wheel:
-    name: Wheel Upload
-    runs-on: ubuntu-latest
-    needs: [python-linux, python-windows, python-macos, python-sdist]
-    env:
-      GH_TOKEN: ${{ github.token }}
-    steps:
-      - uses: actions/checkout@v4
-      - run: gh run download ${{ github.run_id }} -p wheels-*
-      - name: release
-        run: |
-          gh release create commit-${GITHUB_SHA:0:8} --prerelease wheels-*/*
+          path: sbv2_bindings/dist

-  python-release:
+  release:
    name: Release
    runs-on: ubuntu-latest
    if: "startsWith(github.ref, 'refs/tags/')"
-    needs: [python-linux, python-windows, python-macos, python-sdist]
+    needs: [linux, windows, macos, sdist]
    environment: release
-    env:
-      GH_TOKEN: ${{ github.token }}
    steps:
-      - uses: actions/checkout@v4
-      - run: gh run download ${{ github.run_id }} -p wheels-*
+      - uses: actions/download-artifact@v4
      - name: Publish to PyPI
        uses: PyO3/maturin-action@v1
        with:
          command: upload
          args: --non-interactive --skip-existing wheels-*/*

-  docker:
-    runs-on: ${{ matrix.machine.runner }}
+  push-docker:
+    runs-on: ubuntu-latest
+    if: "startsWith(github.ref, 'refs/tags/')"
+    permissions:
+      contents: read
+      packages: write
    strategy:
-      fail-fast: false
      matrix:
-        machine:
-          - platform: amd64
-            runner: ubuntu-latest
-          - platform: arm64
-            runner: ubuntu-24.04-arm
        tag: [cpu, cuda]
+        platform:
+        - linux/amd64
+        - linux/arm64
    steps:
-      - name: Prepare
-        run: |
-          platform=${{ matrix.machine.platform }}
-          echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV
-
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: |
-            ghcr.io/${{ github.repository }}
-
-      - name: Login to GHCR
-        uses: docker/login-action@v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.repository_owner }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
+      - uses: actions/checkout@v4
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3
-
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push by digest
-        id: build
-        uses: docker/build-push-action@v6
-        with:
-          labels: ${{ steps.meta.outputs.labels }}
-          file: ./scripts/docker/${{ matrix.tag }}.Dockerfile
-          push: true
-          tags: |
-            ghcr.io/${{ github.repository }}:latest-${{ matrix.tag }}-${{ matrix.machine.platform }}
-
-  docker-merge:
-    runs-on: ubuntu-latest
-    needs:
-      - docker
-    steps:
-      - name: Download digests
-        uses: actions/download-artifact@v4
-        with:
-          path: ${{ runner.temp }}/digests
-          pattern: digests-*
-          merge-multiple: true
-
-      - name: Login to GHCR
+      - name: Login to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
-          username: ${{ github.repository_owner }}
+          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Merge
-        run: |
-          docker buildx imagetools create -t ghcr.io/${{ github.repository }}:cuda \
-              ghcr.io/${{ github.repository }}:latest-cuda-amd64 \
-              ghcr.io/${{ github.repository }}:latest-cuda-arm64
-          docker buildx imagetools create -t ghcr.io/${{ github.repository }}:cpu \
-              ghcr.io/${{ github.repository }}:latest-cpu-amd64 \
-              ghcr.io/${{ github.repository }}:latest-cpu-arm64
+      - name: Build and push image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: |
+            ghcr.io/${{ github.repository }}:${{ matrix.tag }}
+          file: docker/${{ matrix.tag }}.Dockerfile
+          platforms: ${{ matrix.platform }}
--- a/.gitignore
+++ b/.gitignore
@@ -1,10 +1,8 @@
-target/
+target
 models/
 !models/.gitkeep
 venv/
 .env
-*.wav
-node_modules/
-dist/
-*.csv
-*.bin
+output.wav
+node_modules
+dist/
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -1,25 +1,15 @@
 [workspace]
 resolver = "2"
-members = ["./crates/sbv2_api", "./crates/sbv2_core", "./crates/sbv2_bindings", "./crates/sbv2_wasm"]
-
-[workspace.package]
-version = "0.2.0-alpha6"
-edition = "2021"
-description = "Style-Bert-VITSの推論ライブラリ"
-license = "MIT"
-readme = "./README.md"
-repository = "https://github.com/tuna2134/sbv2-api"
-documentation = "https://docs.rs/sbv2_core"
+members = ["sbv2_api", "sbv2_core", "sbv2_bindings", "sbv2_wasm"]

 [workspace.dependencies]
-anyhow = "1.0.96"
+anyhow = "1.0.86"
 dotenvy = "0.15.7"
-env_logger = "0.11.6"
+env_logger = "0.11.5"
 ndarray = "0.16.1"
-once_cell = "1.20.3"
+once_cell = "1.19.0"

 [profile.release]
-strip = true
-opt-level = "z"
 lto = true
-codegen-units = 1
+debug = false
+strip = true
--- a/README.md
+++ b/README.md
@@ -3,9 +3,6 @@
 ## 注意：本バージョンはアルファ版です。
 安定版を利用したい場合は[こちら](https://github.com/tuna2134/sbv2-api/tree/v0.1.x)をご覧ください。

-## 注意: オプションの辞書はLGPLです。
-オプションの辞書を使用する場合、バイナリの内部の辞書部分について、LGPLが適用されます。
-
 ## プログラミングに詳しくない方向け

 [こちら](https://github.com/tuna2134/sbv2-gui?tab=readme-ov-file)を参照してください。
@@ -20,7 +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

-[こちら](https://github.com/tuna2134/sbv2-api/tree/main/scripts/convert)を参照してください。
+[こちら](https://github.com/tuna2134/sbv2-api/tree/main/convert)を参照してください。

 ## Todo

@@ -32,27 +29,23 @@ JP-Extra しか対応していません。(基本的に対応する予定もあ
 - [x] GPU 対応(CUDA)
 - [x] GPU 対応(DirectML)
 - [x] GPU 対応(CoreML)
- [x] WASM 変換
+- [ ] WASM 変換(依存ライブラリの関係により現在は不可)
 - [x] arm64のdockerサポート
- [x] aivis形式のサポート
 - [ ] MeCabを利用する

 ## 構造説明

- `crates/sbv2_api` - 推論用 REST API
- `crates/sbv2_core` - 推論コア部分
- `scripts/docker` - docker ビルドスクリプト
- `scripts/convert` - onnx, sbv2フォーマットへの変換スクリプト
+- `sbv2_api` - 推論用 REST API
+- `sbv2_core` - 推論コア部分
+- `docker` - docker ビルドスクリプト
+- `convert` - onnx, sbv2フォーマットへの変換スクリプト

 ## プログラミングある程度できる人向けREST API起動方法

 ### models をインストール

-https://huggingface.co/neody/sbv2-api-assets/tree/main/deberta
-から`tokenizer.json`,`debert.onnx`
-https://huggingface.co/neody/sbv2-api-assets/tree/main/model
-から`tsukuyomi.sbv2`
-を models フォルダに配置
+https://huggingface.co/googlefan/sbv2_onnx_models/tree/main
+の`tokenizer.json`,`debert.onnx`,`tsukuyomi.sbv2`を models フォルダに配置

 ### .env ファイルの作成

@@ -121,10 +114,8 @@ curl http://localhost:3000/models
 - `ADDR` `localhost:3000`などのようにサーバー起動アドレスをコントロールできます。
 - `MODELS_PATH` sbv2モデルの存在するフォルダを指定できます。
 - `RUST_LOG` おなじみlog levelです。
- `HOLDER_MAX_LOADED_MODElS` RAMにロードされるモデルの最大数を指定します。

 ## 謝辞

- [litagin02/Style-Bert-VITS2](https://github.com/litagin02/Style-Bert-VITS2) - このコードを書くにあたり、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。
- [Aivis Project](https://github.com/Aivis-Project/AivisSpeech-Engine) - 辞書部分
--- a/content.txt
+++ b/content.txt
@@ -0,0 +1,14 @@
+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。
--- a/scripts/convert/README.md
+++ b/scripts/convert/README.md
--- a/scripts/convert/convert_deberta.py
+++ b/scripts/convert/convert_deberta.py
@@ -5,7 +5,6 @@ from transformers import AutoModelForMaskedLM, AutoTokenizer
 import torch
 from torch import nn
 from argparse import ArgumentParser
-import os

 parser = ArgumentParser()
 parser.add_argument("--model", default="ku-nlp/deberta-v2-large-japanese-char-wwm")
@@ -16,7 +15,7 @@ bert_models.load_tokenizer(Languages.JP, model_name)
 tokenizer = bert_models.load_tokenizer(Languages.JP)
 converter = BertConverter(tokenizer)
 tokenizer = converter.converted()
-tokenizer.save("../../models/tokenizer.json")
+tokenizer.save("../models/tokenizer.json")


 class ORTDeberta(nn.Module):
@@ -43,10 +42,9 @@ inputs = AutoTokenizer.from_pretrained(model_name)(
 torch.onnx.export(
    model,
    (inputs["input_ids"], inputs["token_type_ids"], inputs["attention_mask"]),
-    "../../models/deberta.onnx",
+    "../models/deberta.onnx",
    input_names=["input_ids", "token_type_ids", "attention_mask"],
    output_names=["output"],
    verbose=True,
    dynamic_axes={"input_ids": {1: "batch_size"}, "attention_mask": {1: "batch_size"}},
 )
-os.system("onnxsim ../../models/deberta.onnx ../../models/deberta.onnx")
--- a/scripts/convert/convert_model.py
+++ b/scripts/convert/convert_model.py
@@ -36,7 +36,7 @@ data = array.tolist()
 hyper_parameters = HyperParameters.load_from_json(config_file)
 out_name = hyper_parameters.model_name

-with open(f"../../models/style_vectors_{out_name}.json", "w") as f:
+with open(f"../models/style_vectors_{out_name}.json", "w") as f:
    json.dump(
        {
            "data": data,
@@ -94,7 +94,7 @@ model = get_net_g(
 )


-def forward(x, x_len, sid, tone, lang, bert, style, length_scale, sdp_ratio, noise_scale, noise_scale_w):
+def forward(x, x_len, sid, tone, lang, bert, style, length_scale, sdp_ratio):
    return model.infer(
        x,
        x_len,
@@ -105,8 +105,6 @@ def forward(x, x_len, sid, tone, lang, bert, style, length_scale, sdp_ratio, noi
        style,
        sdp_ratio=sdp_ratio,
        length_scale=length_scale,
-        noise_scale=noise_scale,
-        noise_scale_w=noise_scale_w,
    )


@@ -124,10 +122,8 @@ torch.onnx.export(
        style_vec_tensor,
        torch.tensor(1.0),
        torch.tensor(0.0),
-        torch.tensor(0.6777),
-        torch.tensor(0.8),
    ),
-    f"../../models/model_{out_name}.onnx",
+    f"../models/model_{out_name}.onnx",
    verbose=True,
    dynamic_axes={
        "x_tst": {0: "batch_size", 1: "x_tst_max_length"},
@@ -148,16 +144,14 @@ torch.onnx.export(
        "style_vec",
        "length_scale",
        "sdp_ratio",
-        "noise_scale",
-        "noise_scale_w"
    ],
    output_names=["output"],
 )
-os.system(f"onnxsim ../../models/model_{out_name}.onnx ../../models/model_{out_name}.onnx")
-onnxfile = open(f"../../models/model_{out_name}.onnx", "rb").read()
-stylefile = open(f"../../models/style_vectors_{out_name}.json", "rb").read()
+os.system(f"onnxsim ../models/model_{out_name}.onnx ../models/model_{out_name}.onnx")
+onnxfile = open(f"../models/model_{out_name}.onnx", "rb").read()
+stylefile = open(f"../models/style_vectors_{out_name}.json", "rb").read()
 version = bytes("1", "utf8")
-with taropen(f"../../models/tmp_{out_name}.sbv2tar", "w") as w:
+with taropen(f"../models/tmp_{out_name}.sbv2tar", "w") as w:

    def add_tar(f, b):
        t = TarInfo(f)
@@ -167,9 +161,9 @@ with taropen(f"../../models/tmp_{out_name}.sbv2tar", "w") as w:
    add_tar("version.txt", version)
    add_tar("model.onnx", onnxfile)
    add_tar("style_vectors.json", stylefile)
-open(f"../../models/{out_name}.sbv2", "wb").write(
+open(f"../models/{out_name}.sbv2", "wb").write(
    ZstdCompressor(threads=-1, level=22).compress(
-        open(f"../../models/tmp_{out_name}.sbv2tar", "rb").read()
+        open(f"../models/tmp_{out_name}.sbv2tar", "rb").read()
    )
 )
-os.unlink(f"../../models/tmp_{out_name}.sbv2tar")
+os.unlink(f"../models/tmp_{out_name}.sbv2tar")
--- a/scripts/convert/requirements.txt
+++ b/scripts/convert/requirements.txt
--- a/crates/sbv2_bindings/Cargo.toml
+++ b/crates/sbv2_bindings/Cargo.toml
@@ -1,24 +0,0 @@
-[package]
-name = "sbv2_bindings"
-version.workspace = true
-edition.workspace = true
-description.workspace = true
-readme.workspace = true
-repository.workspace = true
-documentation.workspace = true
-license.workspace = true
-
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
-[lib]
-name = "sbv2_bindings"
-crate-type = ["cdylib"]
-
-[dependencies]
-anyhow.workspace = true
-ndarray.workspace = true
-pyo3 = { version = "0.23.0", features = ["anyhow"] }
-sbv2_core = { path = "../sbv2_core", features = ["std"], default-features = false }
-
-[features]
-agpl_dict = ["sbv2_core/agpl_dict"]
-default = ["agpl_dict"]
--- a/crates/sbv2_core/build.rs
+++ b/crates/sbv2_core/build.rs
@@ -1,25 +0,0 @@
-use dirs::home_dir;
-use std::env;
-use std::fs;
-use std::io::copy;
-use std::path::PathBuf;
-
-fn main() -> Result<(), Box<dyn std::error::Error>> {
-    let static_path = home_dir().unwrap().join(".cache/sbv2/all.bin");
-    let out_path = PathBuf::from(&env::var("OUT_DIR").unwrap()).join("all.bin");
-    println!("cargo:rerun-if-changed=build.rs");
-    if static_path.exists() {
-        if fs::hard_link(&static_path, &out_path).is_err() {
-            fs::copy(static_path, out_path).unwrap();
-        };
-    } else {
-        println!("cargo:warning=Downloading dictionary file...");
-        let mut response =
-            ureq::get("https://huggingface.co/neody/sbv2-api-assets/resolve/main/dic/all.bin")
-                .call()?;
-        let mut response = response.body_mut().as_reader();
-        let mut file = fs::File::create(&out_path)?;
-        copy(&mut response, &mut file)?;
-    }
-    Ok(())
-}
--- a/crates/sbv2_core/src/bert.rs
+++ b/crates/sbv2_core/src/bert.rs
@@ -1,24 +0,0 @@
-use crate::error::Result;
-use ndarray::{Array2, Ix2};
-use ort::session::Session;
-use ort::value::TensorRef;
-
-pub fn predict(
-    session: &mut Session,
-    token_ids: Vec<i64>,
-    attention_masks: Vec<i64>,
-) -> Result<Array2<f32>> {
-    let outputs = session.run(
-        ort::inputs! {
-            "input_ids" => TensorRef::from_array_view((vec![1, token_ids.len() as i64], token_ids.as_slice()))?,
-            "attention_mask" => TensorRef::from_array_view((vec![1, attention_masks.len() as i64], attention_masks.as_slice()))?,
-        }
-    )?;
-
-    let output = outputs["output"]
-        .try_extract_tensor::<f32>()?
-        .into_dimensionality::<Ix2>()?
-        .to_owned();
-
-    Ok(output)
-}
--- a/crates/sbv2_core/src/main.rs
+++ b/crates/sbv2_core/src/main.rs
@@ -1,49 +0,0 @@
-use std::env;
-use std::fs;
-
-#[cfg(feature = "std")]
-fn main_inner() -> anyhow::Result<()> {
-    use sbv2_core::tts;
-    dotenvy::dotenv_override().ok();
-    env_logger::init();
-    let text = "今日の天気は快晴です。";
-    let ident = "aaa";
-    let mut tts_holder = tts::TTSModelHolder::new(
-        &fs::read(env::var("BERT_MODEL_PATH")?)?,
-        &fs::read(env::var("TOKENIZER_PATH")?)?,
-        env::var("HOLDER_MAX_LOADED_MODElS")
-            .ok()
-            .and_then(|x| x.parse().ok()),
-    )?;
-    let mp = env::var("MODEL_PATH")?;
-    let b = fs::read(&mp)?;
-    #[cfg(not(feature = "aivmx"))]
-    {
-        tts_holder.load_sbv2file(ident, b)?;
-    }
-    #[cfg(feature = "aivmx")]
-    {
-        if mp.ends_with(".sbv2") {
-            tts_holder.load_sbv2file(ident, b)?;
-        } else {
-            tts_holder.load_aivmx(ident, b)?;
-        }
-    }
-
-    let audio =
-        tts_holder.easy_synthesize(ident, &text, 0, 0, tts::SynthesizeOptions::default())?;
-    fs::write("output.wav", audio)?;
-
-    Ok(())
-}
-
-#[cfg(not(feature = "std"))]
-fn main_inner() -> anyhow::Result<()> {
-    Ok(())
-}
-
-fn main() {
-    if let Err(e) = main_inner() {
-        println!("Error: {e}");
-    }
-}
--- a/crates/sbv2_core/src/model.rs
+++ b/crates/sbv2_core/src/model.rs
@@ -1,111 +0,0 @@
-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::cuda::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>,
-    mut spk_ids: Array1<i64>,
-    tones: Array1<i64>,
-    lang_ids: Array1<i64>,
-    style_vector: Array1<f32>,
-    sdp_ratio: f32,
-    length_scale: f32,
-    noise_scale: f32,
-    noise_scale_w: 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 = ort::value::TensorRef::from_array_view(&mut spk_ids)?;
-    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 noise_scale = vec![noise_scale];
-    let noise_scale =
-        ort::value::TensorRef::from_array_view((vec![1_i64], noise_scale.as_slice()))?;
-    let noise_scale_w = vec![noise_scale_w];
-    let noise_scale_w =
-        ort::value::TensorRef::from_array_view((vec![1_i64], noise_scale_w.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,
-        "noise_scale" => noise_scale,
-        "noise_scale_w" => noise_scale_w,
-    })?;
-
-    let audio_array = outputs["output"]
-        .try_extract_tensor::<f32>()?
-        .into_dimensionality::<Ix3>()?
-        .to_owned();
-
-    Ok(audio_array)
-}
--- a/crates/sbv2_wasm/build.sh
+++ b/crates/sbv2_wasm/build.sh
@@ -1,8 +0,0 @@
-#!/bin/sh
-wasm-pack build --target web ./crates/sbv2_wasm --release
-wasm-opt -O3 -o ./crates/sbv2_wasm/pkg/sbv2_wasm_bg.wasm ./crates/sbv2_wasm/pkg/sbv2_wasm_bg.wasm
-wasm-strip ./crates/sbv2_wasm/pkg/sbv2_wasm_bg.wasm
-mkdir -p ./crates/sbv2_wasm/dist
-cp ./crates/sbv2_wasm/pkg/sbv2_wasm_bg.wasm ./crates/sbv2_wasm/dist/sbv2_wasm_bg.wasm
-cd ./crates/sbv2_wasm
-pnpm build
--- a/crates/sbv2_wasm/pnpm-lock.yaml
+++ b/crates/sbv2_wasm/pnpm-lock.yaml
@@ -1,504 +0,0 @@
-lockfileVersion: '9.0'
-
-settings:
-  autoInstallPeers: true
-  excludeLinksFromLockfile: false
-
-importers:
-
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-        version: 1.20.1
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-        version: 0.25.0
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-        version: 5.7.3
-
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--- a/scripts/docker/cpu.Dockerfile
+++ b/scripts/docker/cpu.Dockerfile
--- a/scripts/docker/cuda.Dockerfile
+++ b/scripts/docker/cuda.Dockerfile
--- a/scripts/docker/run_cpu.sh
+++ b/scripts/docker/run_cpu.sh
--- a/scripts/docker/run_cuda.sh
+++ b/scripts/docker/run_cuda.sh
--- a/crates/sbv2_api/Cargo.toml
+++ b/crates/sbv2_api/Cargo.toml
@@ -1,24 +1,19 @@
 [package]
 name = "sbv2_api"
-version.workspace = true
-edition.workspace = true
-description.workspace = true
-readme.workspace = true
-repository.workspace = true
-documentation.workspace = true
-license.workspace = true
+version = "0.2.0-alpha"
+edition = "2021"

 [dependencies]
 anyhow.workspace = true
-axum = "0.8.0"
+axum = "0.7.5"
 dotenvy.workspace = true
 env_logger.workspace = true
 log = "0.4.22"
-sbv2_core = { version = "0.2.0-alpha6", path = "../sbv2_core", features = ["aivmx"] }
+sbv2_core = { version = "0.2.0-alpha", path = "../sbv2_core" }
 serde = { version = "1.0.210", features = ["derive"] }
 tokio = { version = "1.40.0", features = ["full"] }
-utoipa = { version = "5.0.0", features = ["axum_extras"] }
-utoipa-scalar = { version = "0.3.0", features = ["axum"] }
+utoipa = { version = "4.2.3", features = ["axum_extras"] }
+utoipa-scalar = { version = "0.1.0", features = ["axum"] }

 [features]
 coreml = ["sbv2_core/coreml"]
--- a/crates/sbv2_api/build.rs
+++ b/crates/sbv2_api/build.rs
--- a/crates/sbv2_api/src/error.rs
+++ b/crates/sbv2_api/src/error.rs
--- a/crates/sbv2_api/src/main.rs
+++ b/crates/sbv2_api/src/main.rs
@@ -40,14 +40,6 @@ fn length_default() -> f32 {
    1.0
 }

-fn style_id_default() -> i32 {
-    0
-}
-
-fn speaker_id_default() -> i64 {
-    0
-}
-
 #[derive(Deserialize, ToSchema)]
 struct SynthesizeRequest {
    text: String,
@@ -56,10 +48,6 @@ struct SynthesizeRequest {
    sdp_ratio: f32,
    #[serde(default = "length_default")]
    length_scale: f32,
-    #[serde(default = "style_id_default")]
-    style_id: i32,
-    #[serde(default = "speaker_id_default")]
-    speaker_id: i64,
 }

 #[utoipa::path(
@@ -77,18 +65,15 @@ async fn synthesize(
        ident,
        sdp_ratio,
        length_scale,
-        style_id,
-        speaker_id,
    }): Json<SynthesizeRequest>,
 ) -> AppResult<impl IntoResponse> {
    log::debug!("processing request: text={text}, ident={ident}, sdp_ratio={sdp_ratio}, length_scale={length_scale}");
    let buffer = {
-        let mut tts_model = state.tts_model.lock().await;
+        let tts_model = state.tts_model.lock().await;
        tts_model.easy_synthesize(
            &ident,
            &text,
-            style_id,
-            speaker_id,
+            0,
            SynthesizeOptions {
                sdp_ratio,
                length_scale,
@@ -109,9 +94,6 @@ impl AppState {
        let mut tts_model = TTSModelHolder::new(
            &fs::read(env::var("BERT_MODEL_PATH")?).await?,
            &fs::read(env::var("TOKENIZER_PATH")?).await?,
-            env::var("HOLDER_MAX_LOADED_MODElS")
-                .ok()
-                .and_then(|x| x.parse().ok()),
        )?;
        let models = env::var("MODELS_PATH").unwrap_or("models".to_string());
        let mut f = fs::read_dir(&models).await?;
@@ -140,20 +122,6 @@ impl AppState {
                    log::warn!("Error loading {entry}: {e}");
                };
                log::info!("Loaded: {entry}");
-            } else if name.ends_with(".aivmx") {
-                let entry = &name[..name.len() - 6];
-                log::info!("Try loading: {entry}");
-                let aivmx_bytes = match fs::read(format!("{models}/{entry}.aivmx")).await {
-                    Ok(b) => b,
-                    Err(e) => {
-                        log::warn!("Error loading aivmx bytes from file {entry}: {e}");
-                        continue;
-                    }
-                };
-                if let Err(e) = tts_model.load_aivmx(entry, aivmx_bytes) {
-                    log::error!("Error loading {entry}: {e}");
-                }
-                log::info!("Loaded: {entry}");
            }
        }
        for entry in entries {
--- a/sbv2_bindings/Cargo.toml
+++ b/sbv2_bindings/Cargo.toml
@@ -0,0 +1,15 @@
+[package]
+name = "sbv2_bindings"
+version = "0.2.0-alpha1"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+[lib]
+name = "sbv2_bindings"
+crate-type = ["cdylib"]
+
+[dependencies]
+anyhow.workspace = true
+ndarray.workspace = true
+pyo3 = { version = "0.22.0", features = ["anyhow"] }
+sbv2_core = { version = "0.2.0-alpha", path = "../sbv2_core" }
--- a/sbv2_bindings/examples/basic.py
+++ b/sbv2_bindings/examples/basic.py
@@ -3,16 +3,16 @@ from sbv2_bindings import TTSModel

 def main():
    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!")

-    model.load_sbv2file_from_path("amitaro", "./models/amitaro.sbv2")
+    model.load_sbv2file_from_path("amitaro", "../models/amitaro.sbv2")
    print("All setup is done!")

    style_vector = model.get_style_vector("amitaro", 0, 1.0)
    with open("output.wav", "wb") as f:
        f.write(
-            model.synthesize("おはようございます。", "amitaro", 0, 0, 0.0, 0.5)
+            model.synthesize("おはようございます。", "amitaro", style_vector, 0.0, 0.5)
        )


--- a/crates/sbv2_bindings/pyproject.toml
+++ b/crates/sbv2_bindings/pyproject.toml
--- a/crates/sbv2_bindings/src/lib.rs
+++ b/crates/sbv2_bindings/src/lib.rs
--- a/crates/sbv2_bindings/src/sbv2.rs
+++ b/crates/sbv2_bindings/src/sbv2.rs
@@ -23,15 +23,10 @@ pub struct TTSModel {

 #[pymethods]
 impl TTSModel {
-    #[pyo3(signature = (bert_model_bytes, tokenizer_bytes, max_loaded_models=None))]
    #[new]
-    fn new(
-        bert_model_bytes: Vec<u8>,
-        tokenizer_bytes: Vec<u8>,
-        max_loaded_models: Option<usize>,
-    ) -> anyhow::Result<Self> {
+    fn new(bert_model_bytes: Vec<u8>, tokenizer_bytes: Vec<u8>) -> anyhow::Result<Self> {
        Ok(Self {
-            model: TTSModelHolder::new(bert_model_bytes, tokenizer_bytes, max_loaded_models)?,
+            model: TTSModelHolder::new(bert_model_bytes, tokenizer_bytes)?,
        })
    }

@@ -43,21 +38,10 @@ impl TTSModel {
    ///     BERTモデルのパス
    /// tokenizer_path : str
    ///     トークナイザーのパス
-    /// max_loaded_models: int | None
-    ///     同時にVRAMに存在するモデルの数
-    #[pyo3(signature = (bert_model_path, tokenizer_path, max_loaded_models=None))]
    #[staticmethod]
-    fn from_path(
-        bert_model_path: String,
-        tokenizer_path: String,
-        max_loaded_models: Option<usize>,
-    ) -> anyhow::Result<Self> {
+    fn from_path(bert_model_path: String, tokenizer_path: String) -> anyhow::Result<Self> {
        Ok(Self {
-            model: TTSModelHolder::new(
-                fs::read(bert_model_path)?,
-                fs::read(tokenizer_path)?,
-                max_loaded_models,
-            )?,
+            model: TTSModelHolder::new(fs::read(bert_model_path)?, fs::read(tokenizer_path)?)?,
        })
    }

@@ -107,7 +91,7 @@ impl TTSModel {
    /// style_vector : StyleVector
    ///     スタイルベクトル
    fn get_style_vector(
-        &mut self,
+        &self,
        ident: String,
        style_id: i32,
        weight: f32,
@@ -137,27 +121,25 @@ impl TTSModel {
    /// voice_data : bytes
    ///     音声データ
    fn synthesize<'p>(
-        &'p mut self,
+        &'p self,
        py: Python<'p>,
        text: String,
        ident: String,
        style_id: i32,
-        speaker_id: i64,
        sdp_ratio: f32,
        length_scale: f32,
-    ) -> anyhow::Result<Bound<'p, PyBytes>> {
+    ) -> anyhow::Result<Bound<PyBytes>> {
        let data = self.model.easy_synthesize(
            ident.as_str(),
            &text,
            style_id,
-            speaker_id,
            SynthesizeOptions {
                sdp_ratio,
                length_scale,
                ..Default::default()
            },
        )?;
-        Ok(PyBytes::new(py, &data))
+        Ok(PyBytes::new_bound(py, &data))
    }

    fn unload(&mut self, ident: String) -> bool {
--- a/crates/sbv2_bindings/src/style.rs
+++ b/crates/sbv2_bindings/src/style.rs
--- a/crates/sbv2_core/Cargo.toml
+++ b/crates/sbv2_core/Cargo.toml
@@ -1,47 +1,40 @@
 [package]
 name = "sbv2_core"
-version.workspace = true
-edition.workspace = true
-description.workspace = true
-readme.workspace = true
-repository.workspace = true
-documentation.workspace = true
-license.workspace = true
+description = "Style-Bert-VITSの推論ライブラリ"
+version = "0.2.0-alpha1"
+edition = "2021"
+license = "MIT"
+readme = "../README.md"
+repository = "https://github.com/tuna2134/sbv2-api"
+documentation = "https://docs.rs/sbv2_core"

 [dependencies]
 anyhow.workspace = true
-base64 = { version = "0.22.1", optional = true }
 dotenvy.workspace = true
 env_logger.workspace = true
 hound = "3.5.1"
-jpreprocess = { version = "0.12.0", features = ["naist-jdic"] }
+jpreprocess = { version = "0.10.0", features = ["naist-jdic"] }
 ndarray.workspace = true
-npyz = { version = "0.8.3", optional = true }
 num_cpus = "1.16.0"
 once_cell.workspace = true
-ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.9", optional = true }
+ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.6", optional = true }
 regex = "1.10.6"
 serde = { version = "1.0.210", features = ["derive"] }
 serde_json = "1.0.128"
 tar = "0.4.41"
-thiserror = "2.0.11"
-tokenizers = { version = "0.21.0", default-features = false }
+thiserror = "1.0.63"
+tokenizers = { version = "0.20.0", default-features = false }
+vibrato = { version = "0.5.1", optional = true }
 zstd = "0.13.2"

 [features]
 cuda = ["ort/cuda", "std"]
 cuda_tf32 = ["std", "cuda"]
-agpl_dict = []
 std = ["dep:ort", "tokenizers/progressbar", "tokenizers/onig", "tokenizers/esaxx_fast"]
 dynamic = ["ort/load-dynamic", "std"]
 directml = ["ort/directml", "std"]
 tensorrt = ["ort/tensorrt", "std"]
 coreml = ["ort/coreml", "std"]
-default = ["std", "agpl_dict"]
+default = ["std"]
 no_std = ["tokenizers/unstable_wasm"]
-aivmx = ["npyz", "base64"]
-base64 = ["dep:base64"]
-
-[build-dependencies]
-dirs = "6.0.0"
-ureq = "3.0.6"
+mecab = ["vibrato"]
--- a/crates/sbv2_core/mora_convert.py
+++ b/crates/sbv2_core/mora_convert.py
--- a/sbv2_core/src/bert.rs
+++ b/sbv2_core/src/bert.rs
@@ -0,0 +1,23 @@
+use crate::error::Result;
+use ndarray::Array2;
+use ort::Session;
+
+pub fn predict(
+    session: &Session,
+    token_ids: Vec<i64>,
+    attention_masks: Vec<i64>,
+) -> Result<Array2<f32>> {
+    let outputs = session.run(
+        ort::inputs! {
+            "input_ids" => Array2::from_shape_vec((1, token_ids.len()), token_ids).unwrap(),
+            "attention_mask" => Array2::from_shape_vec((1, attention_masks.len()), attention_masks).unwrap(),
+        }?
+    )?;
+
+    let output = outputs.get("output").unwrap();
+
+    let content = output.try_extract_tensor::<f32>()?.to_owned();
+    let (data, _) = content.clone().into_raw_vec_and_offset();
+
+    Ok(Array2::from_shape_vec((content.shape()[0], content.shape()[1]), data).unwrap())
+}
--- a/crates/sbv2_core/src/error.rs
+++ b/crates/sbv2_core/src/error.rs
@@ -6,8 +6,6 @@ pub enum Error {
    TokenizerError(#[from] tokenizers::Error),
    #[error("JPreprocess error: {0}")]
    JPreprocessError(#[from] jpreprocess::error::JPreprocessError),
-    #[error("Lindera error: {0}")]
-    LinderaError(String),
    #[cfg(feature = "std")]
    #[error("ONNX error: {0}")]
    OrtError(#[from] ort::Error),
@@ -23,9 +21,6 @@ pub enum Error {
    HoundError(#[from] hound::Error),
    #[error("model not found error")]
    ModelNotFoundError(String),
-    #[cfg(feature = "base64")]
-    #[error("base64 error")]
-    Base64Error(#[from] base64::DecodeError),
    #[error("other")]
    OtherError(String),
 }
--- a/crates/sbv2_core/src/jtalk.rs
+++ b/crates/sbv2_core/src/jtalk.rs
@@ -1,32 +1,21 @@
 use crate::error::{Error, Result};
 use crate::mora::{MORA_KATA_TO_MORA_PHONEMES, VOWELS};
 use crate::norm::{replace_punctuation, PUNCTUATIONS};
-use jpreprocess::{kind, DefaultTokenizer, JPreprocess, SystemDictionaryConfig, UserDictionary};
+use jpreprocess::*;
 use once_cell::sync::Lazy;
 use regex::Regex;
 use std::cmp::Reverse;
 use std::collections::HashSet;
 use std::sync::Arc;

-type JPreprocessType = JPreprocess<DefaultTokenizer>;
-
-#[cfg(feature = "agpl_dict")]
-fn agpl_dict() -> Result<Option<UserDictionary>> {
-    Ok(Some(
-        UserDictionary::load(include_bytes!(concat!(env!("OUT_DIR"), "/all.bin")))
-            .map_err(|e| Error::LinderaError(e.to_string()))?,
-    ))
-}
-
-#[cfg(not(feature = "agpl_dict"))]
-fn agpl_dict() -> Result<Option<UserDictionary>> {
-    Ok(None)
-}
+type JPreprocessType = JPreprocess<DefaultFetcher>;

 fn initialize_jtalk() -> Result<JPreprocessType> {
-    let sdic =
-        SystemDictionaryConfig::Bundled(kind::JPreprocessDictionaryKind::NaistJdic).load()?;
-    let jpreprocess = JPreprocess::with_dictionaries(sdic, agpl_dict()?);
+    let config = JPreprocessConfig {
+        dictionary: SystemDictionaryConfig::Bundled(kind::JPreprocessDictionaryKind::NaistJdic),
+        user_dictionary: None,
+    };
+    let jpreprocess = JPreprocess::from_config(config)?;
    Ok(jpreprocess)
 }

--- a/crates/sbv2_core/src/lib.rs
+++ b/crates/sbv2_core/src/lib.rs
@@ -14,3 +14,6 @@ pub mod tokenizer;
 pub mod tts;
 pub mod tts_util;
 pub mod utils;
+
+#[cfg(feature = "mecab")]
+pub mod mecab;
--- a/sbv2_core/src/main.rs
+++ b/sbv2_core/src/main.rs
@@ -0,0 +1,31 @@
+use std::env;
+use std::fs;
+
+#[cfg(feature = "std")]
+fn main_inner() -> anyhow::Result<()> {
+    use sbv2_core::tts;
+    dotenvy::dotenv_override().ok();
+    env_logger::init();
+    let text = fs::read_to_string("content.txt")?;
+    let ident = "aaa";
+    let mut tts_holder = tts::TTSModelHolder::new(
+        &fs::read(env::var("BERT_MODEL_PATH")?)?,
+        &fs::read(env::var("TOKENIZER_PATH")?)?,
+    )?;
+    tts_holder.load_sbv2file(ident, fs::read(env::var("MODEL_PATH")?)?)?;
+
+    let audio = tts_holder.easy_synthesize(ident, &text, 0, tts::SynthesizeOptions::default())?;
+    fs::write("output.wav", audio)?;
+
+    Ok(())
+}
+#[cfg(not(feature = "std"))]
+fn main_inner() -> anyhow::Result<()> {
+    Ok(())
+}
+
+fn main() {
+    if let Err(e) = main_inner() {
+        println!("Error: {e}");
+    }
+}
--- a/sbv2_core/src/mecab.rs
+++ b/sbv2_core/src/mecab.rs
--- a/sbv2_core/src/model.rs
+++ b/sbv2_core/src/model.rs
@@ -0,0 +1,93 @@
+use crate::error::Result;
+use ndarray::{array, Array1, Array2, Array3, Axis};
+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
+        .get("output")
+        .unwrap()
+        .try_extract_tensor::<f32>()?
+        .to_owned();
+
+    Ok(Array3::from_shape_vec(
+        (
+            audio_array.shape()[0],
+            audio_array.shape()[1],
+            audio_array.shape()[2],
+        ),
+        audio_array.into_raw_vec_and_offset().0,
+    )?)
+}
--- a/crates/sbv2_core/src/mora.rs
+++ b/crates/sbv2_core/src/mora.rs
--- a/crates/sbv2_core/src/mora_list.json
+++ b/crates/sbv2_core/src/mora_list.json
--- a/crates/sbv2_core/src/nlp.rs
+++ b/crates/sbv2_core/src/nlp.rs
--- a/crates/sbv2_core/src/norm.rs
+++ b/crates/sbv2_core/src/norm.rs
--- a/crates/sbv2_core/src/sbv2file.rs
+++ b/crates/sbv2_core/src/sbv2file.rs
--- a/crates/sbv2_core/src/style.rs
+++ b/crates/sbv2_core/src/style.rs
--- a/crates/sbv2_core/src/tokenizer.rs
+++ b/crates/sbv2_core/src/tokenizer.rs
--- a/crates/sbv2_core/src/tts.rs
+++ b/crates/sbv2_core/src/tts.rs
@@ -1,13 +1,7 @@
 use crate::error::{Error, Result};
 use crate::{jtalk, model, style, tokenizer, tts_util};
-#[cfg(feature = "aivmx")]
-use base64::prelude::{Engine as _, BASE64_STANDARD};
-#[cfg(feature = "aivmx")]
-use ndarray::ShapeBuilder;
 use ndarray::{concatenate, Array1, Array2, Array3, Axis};
-use ort::session::Session;
-#[cfg(feature = "aivmx")]
-use std::io::Cursor;
+use ort::Session;
 use tokenizers::Tokenizer;

 #[derive(PartialEq, Eq, Clone)]
@@ -30,10 +24,9 @@ where
 }

 pub struct TTSModel {
-    vits2: Option<Session>,
+    vits2: Session,
    style_vectors: Array2<f32>,
    ident: TTSIdent,
-    bytes: Option<Vec<u8>>,
 }

 /// High-level Style-Bert-VITS2's API
@@ -42,7 +35,6 @@ pub struct TTSModelHolder {
    bert: Session,
    models: Vec<TTSModel>,
    jtalk: jtalk::JTalk,
-    max_loaded_models: Option<usize>,
 }

 impl TTSModelHolder {
@@ -51,13 +43,9 @@ impl TTSModelHolder {
    /// # Examples
    ///
    /// ```rs
-    /// let mut tts_holder = TTSModelHolder::new(std::fs::read("deberta.onnx")?, std::fs::read("tokenizer.json")?, None)?;
+    /// let mut tts_holder = TTSModelHolder::new(std::fs::read("deberta.onnx")?, std::fs::read("tokenizer.json")?)?;
    /// ```
-    pub fn new<P: AsRef<[u8]>>(
-        bert_model_bytes: P,
-        tokenizer_bytes: P,
-        max_loaded_models: Option<usize>,
-    ) -> Result<Self> {
+    pub fn new<P: AsRef<[u8]>>(bert_model_bytes: P, tokenizer_bytes: P) -> Result<Self> {
        let bert = model::load_model(bert_model_bytes, true)?;
        let jtalk = jtalk::JTalk::new()?;
        let tokenizer = tokenizer::get_tokenizer(tokenizer_bytes)?;
@@ -66,7 +54,6 @@ impl TTSModelHolder {
            models: vec![],
            jtalk,
            tokenizer,
-            max_loaded_models,
        })
    }

@@ -75,53 +62,6 @@ impl TTSModelHolder {
        self.models.iter().map(|m| m.ident.to_string()).collect()
    }

-    #[cfg(feature = "aivmx")]
-    pub fn load_aivmx<I: Into<TTSIdent>, P: AsRef<[u8]>>(
-        &mut self,
-        ident: I,
-        aivmx_bytes: P,
-    ) -> Result<()> {
-        let ident = ident.into();
-        if self.find_model(ident.clone()).is_err() {
-            let mut load = true;
-            if let Some(max) = self.max_loaded_models {
-                if self.models.iter().filter(|x| x.vits2.is_some()).count() >= max {
-                    load = false;
-                }
-            }
-            let model = model::load_model(&aivmx_bytes, false)?;
-            let metadata = model.metadata()?;
-            if let Some(aivm_style_vectors) = metadata.custom("aivm_style_vectors")? {
-                let aivm_style_vectors = BASE64_STANDARD.decode(aivm_style_vectors)?;
-                let style_vectors = Cursor::new(&aivm_style_vectors);
-                let reader = npyz::NpyFile::new(style_vectors)?;
-                let style_vectors = {
-                    let shape = reader.shape().to_vec();
-                    let order = reader.order();
-                    let data = reader.into_vec::<f32>()?;
-                    let shape = match shape[..] {
-                        [i1, i2] => [i1 as usize, i2 as usize],
-                        _ => panic!("expected 2D array"),
-                    };
-                    let true_shape = shape.set_f(order == npyz::Order::Fortran);
-                    ndarray::Array2::from_shape_vec(true_shape, data)?
-                };
-                drop(metadata);
-                self.models.push(TTSModel {
-                    vits2: if load { Some(model) } else { None },
-                    bytes: if self.max_loaded_models.is_some() {
-                        Some(aivmx_bytes.as_ref().to_vec())
-                    } else {
-                        None
-                    },
-                    ident,
-                    style_vectors,
-                })
-            }
-        }
-        Ok(())
-    }
-
    /// Load a .sbv2 file binary
    ///
    /// # Examples
@@ -154,25 +94,10 @@ impl TTSModelHolder {
    ) -> Result<()> {
        let ident = ident.into();
        if self.find_model(ident.clone()).is_err() {
-            let mut load = true;
-            if let Some(max) = self.max_loaded_models {
-                if self.models.iter().filter(|x| x.vits2.is_some()).count() >= max {
-                    load = false;
-                }
-            }
            self.models.push(TTSModel {
-                vits2: if load {
-                    Some(model::load_model(&vits2_bytes, false)?)
-                } else {
-                    None
-                },
+                vits2: model::load_model(vits2_bytes, false)?,
                style_vectors: style::load_style(style_vectors_bytes)?,
                ident,
-                bytes: if self.max_loaded_models.is_some() {
-                    Some(vits2_bytes.as_ref().to_vec())
-                } else {
-                    None
-                },
            })
        }
        Ok(())
@@ -200,7 +125,7 @@ impl TTSModelHolder {
    /// This function is for low-level usage, use `easy_synthesize` for high-level usage.
    #[allow(clippy::type_complexity)]
    pub fn parse_text(
-        &mut self,
+        &self,
        text: &str,
    ) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> {
        crate::tts_util::parse_text_blocking(
@@ -208,53 +133,17 @@ impl TTSModelHolder {
            &self.jtalk,
            &self.tokenizer,
            |token_ids, attention_masks| {
-                crate::bert::predict(&mut self.bert, token_ids, attention_masks)
+                crate::bert::predict(&self.bert, token_ids, attention_masks)
            },
        )
    }

-    fn find_model<I: Into<TTSIdent>>(&mut self, ident: I) -> Result<&mut TTSModel> {
+    fn find_model<I: Into<TTSIdent>>(&self, ident: I) -> Result<&TTSModel> {
        let ident = ident.into();
        self.models
-            .iter_mut()
-            .find(|m| m.ident == ident)
-            .ok_or(Error::ModelNotFoundError(ident.to_string()))
-    }
-    fn find_and_load_model<I: Into<TTSIdent>>(&mut self, ident: I) -> Result<bool> {
-        let ident = ident.into();
-        let (bytes, style_vectors) = {
-            let model = self
-                .models
-                .iter()
-                .find(|m| m.ident == ident)
-                .ok_or(Error::ModelNotFoundError(ident.to_string()))?;
-            if model.vits2.is_some() {
-                return Ok(true);
-            }
-            (model.bytes.clone().unwrap(), model.style_vectors.clone())
-        };
-        self.unload(ident.clone());
-        let s = model::load_model(&bytes, false)?;
-        if let Some(max) = self.max_loaded_models {
-            if self.models.iter().filter(|x| x.vits2.is_some()).count() >= max {
-                self.unload(self.models.first().unwrap().ident.clone());
-            }
-        }
-        self.models.push(TTSModel {
-            bytes: Some(bytes.to_vec()),
-            vits2: Some(s),
-            style_vectors,
-            ident: ident.clone(),
-        });
-        let model = self
-            .models
            .iter()
            .find(|m| m.ident == ident)
-            .ok_or(Error::ModelNotFoundError(ident.to_string()))?;
-        if model.vits2.is_some() {
-            return Ok(true);
-        }
-        Err(Error::ModelNotFoundError(ident.to_string()))
+            .ok_or(Error::ModelNotFoundError(ident.to_string()))
    }

    /// Get style vector by style id and weight
@@ -262,7 +151,7 @@ impl TTSModelHolder {
    /// # Note
    /// This function is for low-level usage, use `easy_synthesize` for high-level usage.
    pub fn get_style_vector<I: Into<TTSIdent>>(
-        &mut self,
+        &self,
        ident: I,
        style_id: i32,
        weight: f32,
@@ -278,14 +167,12 @@ impl TTSModelHolder {
    /// let audio = tts_holder.easy_synthesize("tsukuyomi", "こんにちは", 0, SynthesizeOptions::default())?;
    /// ```
    pub fn easy_synthesize<I: Into<TTSIdent> + Copy>(
-        &mut self,
+        &self,
        ident: I,
        text: &str,
        style_id: i32,
-        speaker_id: i64,
        options: SynthesizeOptions,
    ) -> Result<Vec<u8>> {
-        self.find_and_load_model(ident)?;
        let style_vector = self.get_style_vector(ident, style_id, options.style_weight)?;
        let audio_array = if options.split_sentences {
            let texts: Vec<&str> = text.split('\n').collect();
@@ -295,24 +182,15 @@ impl TTSModelHolder {
                    continue;
                }
                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(
-                    vits2,
+                    &self.find_model(ident)?.vits2,
                    bert_ori.to_owned(),
                    phones,
-                    Array1::from_vec(vec![speaker_id]),
                    tones,
                    lang_ids,
                    style_vector.clone(),
                    options.sdp_ratio,
                    options.length_scale,
-                    0.677,
-                    0.8,
                )?;
                audios.push(audio.clone());
                if i != texts.len() - 1 {
@@ -325,28 +203,48 @@ impl TTSModelHolder {
            )?
        } else {
            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(
-                vits2,
+                &self.find_model(ident)?.vits2,
                bert_ori.to_owned(),
                phones,
-                Array1::from_vec(vec![speaker_id]),
                tones,
                lang_ids,
                style_vector,
                options.sdp_ratio,
                options.length_scale,
-                0.677,
-                0.8,
            )?
        };
        tts_util::array_to_vec(audio_array)
    }
+
+    /// Synthesize text to audio
+    ///
+    /// # Note
+    /// This function is for low-level usage, use `easy_synthesize` for high-level usage.
+    #[allow(clippy::too_many_arguments)]
+    pub fn synthesize<I: Into<TTSIdent>>(
+        &self,
+        ident: I,
+        bert_ori: Array2<f32>,
+        phones: Array1<i64>,
+        tones: Array1<i64>,
+        lang_ids: Array1<i64>,
+        style_vector: Array1<f32>,
+        sdp_ratio: f32,
+        length_scale: f32,
+    ) -> Result<Vec<u8>> {
+        let audio_array = model::synthesize(
+            &self.find_model(ident)?.vits2,
+            bert_ori.to_owned(),
+            phones,
+            tones,
+            lang_ids,
+            style_vector,
+            sdp_ratio,
+            length_scale,
+        )?;
+        tts_util::array_to_vec(audio_array)
+    }
 }

 /// Synthesize options
--- a/crates/sbv2_core/src/tts_util.rs
+++ b/crates/sbv2_core/src/tts_util.rs
--- a/crates/sbv2_core/src/utils.rs
+++ b/crates/sbv2_core/src/utils.rs
--- a/crates/sbv2_wasm/Cargo.toml
+++ b/crates/sbv2_wasm/Cargo.toml
@@ -1,12 +1,7 @@
 [package]
 name = "sbv2_wasm"
-version.workspace = true
-edition.workspace = true
-description.workspace = true
-readme.workspace = true
-repository.workspace = true
-documentation.workspace = true
-license.workspace = true
+version = "0.1.0"
+edition = "2021"

 [lib]
 crate-type = ["cdylib", "rlib"]
@@ -18,3 +13,7 @@ once_cell.workspace = true
 js-sys = "0.3.70"
 ndarray.workspace = true
 wasm-bindgen-futures = "0.4.43"
+
+[profile.release]
+lto = true
+opt-level = "s"
--- a/crates/sbv2_wasm/README.md
+++ b/crates/sbv2_wasm/README.md
--- a/crates/sbv2_wasm/biome.json
+++ b/crates/sbv2_wasm/biome.json
--- a/sbv2_wasm/build.sh
+++ b/sbv2_wasm/build.sh
@@ -0,0 +1,4 @@
+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
--- a/sbv2_wasm/example.html
+++ b/sbv2_wasm/example.html
@@ -0,0 +1,51 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Style Bert VITS2 Web</title>
+    <script type="importmap">
+        {
+            "imports": {
+            "onnxruntime-web": "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.19.2/dist/ort.all.min.mjs",
+            "sbv2": "https://cdn.jsdelivr.net/npm/sbv2@0.1.1+esm"
+            }
+        }
+    </script>
+    <script type="module" async defer>
+        import { ModelHolder } from "sbv2";
+        await ModelHolder.globalInit(
+            await (
+                await fetch("https://esm.sh/sbv2@0.1.1/dist/sbv2_wasm_bg.wasm", { cache: "force-cache" })
+            ).arrayBuffer(),
+        );
+        const holder = await ModelHolder.create(
+            await (
+                await fetch("/models/tokenizer.json", { cache: "force-cache" })
+            ).text(),
+            await (
+                await fetch("/models/deberta.onnx", { cache: "force-cache" })
+            ).arrayBuffer(),
+        );
+        if (typeof window.onready == "function") {
+            window.onready(holder);
+        }
+    </script>
+    <script type="module" async defer>
+        window.onready = async function (holder) {
+            await holder.load(
+                "amitaro",
+                await (await fetch("/models/amitaro.sbv2")).arrayBuffer(),
+            );
+            const wave = await holder.synthesize("amitaro", "おはよう");
+            console.log(wave);
+        };
+    </script>
+</head>
+
+<body>
+    <div id="root"></div>
+</body>
+
+</html>
--- a/crates/sbv2_wasm/example.js
+++ b/crates/sbv2_wasm/example.js
@@ -3,12 +3,9 @@ import fs from "node:fs/promises";

 ModelHolder.globalInit(await fs.readFile("./dist/sbv2_wasm_bg.wasm"));
 const holder = await ModelHolder.create(
-	(await fs.readFile("../../models/tokenizer.json")).toString("utf-8"),
-	await fs.readFile("../../models/deberta.onnx"),
-);
-await holder.load(
-	"tsukuyomi",
-	await fs.readFile("../../models/tsukuyomi.sbv2"),
+	(await fs.readFile("../models/tokenizer.json")).toString("utf-8"),
+	await fs.readFile("../models/deberta.onnx"),
 );
+await holder.load("tsukuyomi", await fs.readFile("../models/iroha2.sbv2"));
 await fs.writeFile("out.wav", await holder.synthesize("tsukuyomi", "おはよう"));
 holder.unload("tsukuyomi");
--- a/crates/sbv2_wasm/package.json
+++ b/crates/sbv2_wasm/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "sbv2",
-	"version": "0.2.0-alpha6",
+	"version": "0.1.1",
 	"description": "Style Bert VITS2 wasm",
 	"main": "dist/index.js",
 	"types": "dist/index.d.ts",
@@ -13,13 +13,13 @@
 	"author": "tuna2134",
 	"license": "MIT",
 	"devDependencies": {
-		"@biomejs/biome": "^1.9.4",
-		"@types/node": "^22.13.5",
-		"esbuild": "^0.25.0",
-		"typescript": "^5.7.3"
+		"@biomejs/biome": "^1.9.2",
+		"@types/node": "^22.7.4",
+		"esbuild": "^0.24.0",
+		"typescript": "^5.6.2"
 	},
 	"dependencies": {
-		"onnxruntime-web": "^1.20.1"
+		"onnxruntime-web": "^1.19.2"
 	},
-	"files": ["dist/*", "package.json", "README.md", "pkg/*.ts", "pkg/*.js"]
+	"files": ["dist/*", "package.json", "README.md"]
 }
--- a/sbv2_wasm/pnpm-lock.yaml
+++ b/sbv2_wasm/pnpm-lock.yaml
@@ -0,0 +1,494 @@
+lockfileVersion: '9.0'
+
+settings:
+  autoInstallPeers: true
+  excludeLinksFromLockfile: false
+
+importers:
+
+  .:
+    dependencies:
+      onnxruntime-web:
+        specifier: ^1.19.2
+        version: 1.19.2
+    devDependencies:
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+        version: 1.9.3
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+        version: 22.7.4
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+        specifier: ^0.24.0
+        version: 0.24.0
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+        version: 5.6.2
+
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+
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+    hasBin: true
+
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+    engines: {node: '>=14.17'}
+    hasBin: true
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+
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+      '@biomejs/cli-linux-x64-musl': 1.9.3
+      '@biomejs/cli-win32-arm64': 1.9.3
+      '@biomejs/cli-win32-x64': 1.9.3
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+    optional: true
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+    optional: true
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+    optional: true
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+  '@biomejs/cli-linux-arm64@1.9.3':
+    optional: true
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+  '@biomejs/cli-linux-x64-musl@1.9.3':
+    optional: true
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+  '@biomejs/cli-linux-x64@1.9.3':
+    optional: true
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+    optional: true
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+  '@esbuild/win32-ia32@0.24.0':
+    optional: true
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+  '@esbuild/win32-x64@0.24.0':
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+  '@protobufjs/aspromise@1.1.2': {}
+
+  '@protobufjs/base64@1.1.2': {}
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+  '@protobufjs/codegen@2.0.4': {}
+
+  '@protobufjs/eventemitter@1.1.0': {}
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+  '@protobufjs/fetch@1.1.0':
+    dependencies:
+      '@protobufjs/aspromise': 1.1.2
+      '@protobufjs/inquire': 1.1.0
+
+  '@protobufjs/float@1.0.2': {}
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+  '@protobufjs/inquire@1.1.0': {}
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+  '@protobufjs/path@1.1.2': {}
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+  '@protobufjs/pool@1.1.0': {}
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+  '@protobufjs/utf8@1.1.0': {}
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+  '@types/node@22.7.4':
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+  esbuild@0.24.0:
+    optionalDependencies:
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+      '@esbuild/freebsd-x64': 0.24.0
+      '@esbuild/linux-arm': 0.24.0
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+      '@esbuild/linux-ia32': 0.24.0
+      '@esbuild/linux-loong64': 0.24.0
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+      '@esbuild/win32-arm64': 0.24.0
+      '@esbuild/win32-ia32': 0.24.0
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+
+  flatbuffers@1.12.0: {}
+
+  guid-typescript@1.0.9: {}
+
+  long@5.2.3: {}
+
+  onnxruntime-common@1.19.2: {}
+
+  onnxruntime-web@1.19.2:
+    dependencies:
+      flatbuffers: 1.12.0
+      guid-typescript: 1.0.9
+      long: 5.2.3
+      onnxruntime-common: 1.19.2
+      platform: 1.3.6
+      protobufjs: 7.4.0
+
+  platform@1.3.6: {}
+
+  protobufjs@7.4.0:
+    dependencies:
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+      '@protobufjs/base64': 1.1.2
+      '@protobufjs/codegen': 2.0.4
+      '@protobufjs/eventemitter': 1.1.0
+      '@protobufjs/fetch': 1.1.0
+      '@protobufjs/float': 1.0.2
+      '@protobufjs/inquire': 1.1.0
+      '@protobufjs/path': 1.1.2
+      '@protobufjs/pool': 1.1.0
+      '@protobufjs/utf8': 1.1.0
+      '@types/node': 22.7.4
+      long: 5.2.3
+
+  typescript@5.6.2: {}
+
+  undici-types@6.19.8: {}
--- a/crates/sbv2_wasm/src-js/index.ts
+++ b/crates/sbv2_wasm/src-js/index.ts
@@ -74,8 +74,6 @@ export class ModelHolder {
 							style_vec: e,
 							sdp_ratio: new Tensor("float32", [f]),
 							length_scale: new Tensor("float32", [g]),
-							noise_scale: new Tensor("float32", [0.677]),
-							noise_scale_w: new Tensor("float32", [0.8]),
 						})
 					).output;
 					return [new Uint32Array(res.dims), await res.getData(true)];
--- a/crates/sbv2_wasm/src/array_helper.rs
+++ b/crates/sbv2_wasm/src/array_helper.rs
--- a/crates/sbv2_wasm/src/lib.rs
+++ b/crates/sbv2_wasm/src/lib.rs
--- a/crates/sbv2_wasm/tsconfig.json
+++ b/crates/sbv2_wasm/tsconfig.json
--- a/scripts/.gitignore
+++ b/scripts/.gitignore
@@ -1,5 +0,0 @@
-*.json
-venv/
-tmp/
-*.safetensors
-*.npy
--- a/scripts/make_dict.sh
+++ b/scripts/make_dict.sh
@@ -1,14 +0,0 @@
-#!/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
--- a/scripts/sbv2-bindings-colab.ipynb
+++ b/scripts/sbv2-bindings-colab.ipynb
@@ -1,180 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 音声合成プログラム\n",
-    "\n",
-    "このノートブックでは、`sbv2_bindings` パッケージを使用して音声合成を行います。必要なモデルをダウンロードし、ユーザーが入力したテキストから音声を生成します。音声合成が終わったら、再度テキストの入力を求め、ユーザーが終了するまで繰り返します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 必要なパッケージのインストール\n",
-    "%pip install sbv2_bindings\n",
-    "\n",
-    "# 必要なモジュールのインポート\n",
-    "import os\n",
-    "import urllib.request\n",
-    "import time\n",
-    "from sbv2_bindings import TTSModel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## モデルのダウンロード\n",
-    "\n",
-    "モデルファイルとトークナイザーをダウンロードします。ユーザーが独自のモデルを使用したい場合は、該当するURLまたはローカルパスを指定してください。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# モデルの URL またはローカルパスの指定\n",
-    "user_sbv2_model_url = \"\"  # カスタムモデルのURLがあればここに指定\n",
-    "user_sbv2_model_path = \"\"  # カスタムモデルのローカルパスがあればここに指定\n",
-    "\n",
-    "# モデル用のディレクトリを作成\n",
-    "model_dir = 'models'\n",
-    "os.makedirs(model_dir, exist_ok=True)\n",
-    "\n",
-    "# ダウンロードするファイルの URL\n",
-    "file_urls = [\n",
-    "    \"https://huggingface.co/googlefan/sbv2_onnx_models/resolve/main/tokenizer.json\",\n",
-    "    \"https://huggingface.co/googlefan/sbv2_onnx_models/resolve/main/deberta.onnx\",\n",
-    "]\n",
-    "\n",
-    "# モデルのパス決定\n",
-    "if user_sbv2_model_path:\n",
-    "    sbv2_model_path = user_sbv2_model_path  # ローカルモデルのパスを使用\n",
-    "elif user_sbv2_model_url:\n",
-    "    sbv2_model_filename = os.path.basename(user_sbv2_model_url)\n",
-    "    sbv2_model_path = os.path.join(model_dir, sbv2_model_filename)\n",
-    "    file_urls.append(user_sbv2_model_url)\n",
-    "else:\n",
-    "    # デフォルトのモデルを使用\n",
-    "    sbv2_model_filename = \"tsukuyomi.sbv2\"\n",
-    "    sbv2_model_path = os.path.join(model_dir, sbv2_model_filename)\n",
-    "    file_urls.append(\"https://huggingface.co/googlefan/sbv2_onnx_models/resolve/main/tsukuyomi.sbv2\")\n",
-    "\n",
-    "# ファイルをダウンロード\n",
-    "for url in file_urls:\n",
-    "    file_name = os.path.join(model_dir, os.path.basename(url))\n",
-    "    if not os.path.exists(file_name):\n",
-    "        print(f\"{file_name} をダウンロードしています...\")\n",
-    "        urllib.request.urlretrieve(url, file_name)\n",
-    "    else:\n",
-    "        print(f\"{file_name} は既に存在します。\")\n",
-    "\n",
-    "# ダウンロードまたは使用するファイルを確認\n",
-    "print(\"\\n使用するファイル:\")\n",
-    "for file in os.listdir(model_dir):\n",
-    "    print(file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## モデルの読み込みと音声合成\n",
-    "\n",
-    "モデルを読み込み、ユーザーが入力したテキストから音声を生成します。話者名は使用する `.sbv2` ファイル名から自動的に取得します。音声合成が終わったら、再度テキストの入力を求め、ユーザーが終了するまで繰り返します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 音声合成の実行\n",
-    "def main():\n",
-    "    try:\n",
-    "        print(\"\\nモデルを読み込んでいます...\")\n",
-    "        model = TTSModel.from_path(\n",
-    "            os.path.join(model_dir, \"deberta.onnx\"),\n",
-    "            os.path.join(model_dir, \"tokenizer.json\")\n",
-    "        )\n",
-    "        print(\"モデルの読み込みが完了しました！\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"モデルの読み込みに失敗しました: {e}\")\n",
-    "        return\n",
-    "\n",
-    "    # 話者名を取得（.sbv2 ファイル名の拡張子を除いた部分）\n",
-    "    speaker_name = os.path.splitext(os.path.basename(sbv2_model_path))[0]\n",
-    "    \n",
-    "    # 指定されたモデルのパスを使用\n",
-    "    try:\n",
-    "        model.load_sbv2file_from_path(speaker_name, sbv2_model_path)\n",
-    "        print(f\"話者 '{speaker_name}' のセットアップが完了しました！\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"SBV2ファイルの読み込みに失敗しました: {e}\")\n",
-    "        return\n",
-    "\n",
-    "    # 音声合成を繰り返し実行\n",
-    "    while True:\n",
-    "        # 合成したいテキストをユーザーから入力\n",
-    "        user_input = input(\"\\n音声合成したいテキストを入力してください（終了するには 'exit' と入力）: \")\n",
-    "        \n",
-    "        if user_input.strip().lower() == 'exit':\n",
-    "            print(\"音声合成を終了します。\")\n",
-    "            break\n",
-    "\n",
-    "        # 出力ファイル名\n",
-    "        output_file = \"output.wav\"\n",
-    "\n",
-    "        # 音声合成を実行\n",
-    "        try:\n",
-    "            print(\"\\n音声合成を開始します...\")\n",
-    "            start_time = time.time()\n",
-    "\n",
-    "            audio_data = model.synthesize(user_input, speaker_name, 0, 0.0, 1)\n",
-    "\n",
-    "            with open(output_file, \"wb\") as f:\n",
-    "                f.write(audio_data)\n",
-    "\n",
-    "            end_time = time.time()\n",
-    "            elapsed_time = end_time - start_time\n",
-    "\n",
-    "            print(f\"\\n音声が '{output_file}' に保存されました。\")\n",
-    "            print(f\"音声合成にかかった時間: {elapsed_time:.2f} 秒\")\n",
-    "        except Exception as e:\n",
-    "            print(f\"音声合成に失敗しました: {e}\")\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    main()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.x"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
--- a/scripts/sbv2-test-api.py
+++ b/scripts/sbv2-test-api.py
Author	SHA1	Message	Date
tuna2134	4744f02792	fix	2024-10-08 09:48:04 +00:00
tuna2134	5de9514546	add vibrato feature	2024-10-08 09:30:16 +00:00