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base: rust:commit-8f3b7ff5
Branches Tags
rust:main rust:dependabot/cargo/axum-0.8.8 rust:ndarray-update rust:revert-274-dependabot/cargo/ndarray-0.17.1 rust:renovate/esbuild-0.x-lockfile rust:renovate/onnxruntime-web-1.x-lockfile rust:renovate/node-22.x-lockfile rust:marine rust:mecab rust:v0.1.x
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..
compare: rust:mecab
Branches Tags
rust:dependabot/cargo/axum-0.8.8 rust:main rust:ndarray-update rust:revert-274-dependabot/cargo/ndarray-0.17.1 rust:renovate/esbuild-0.x-lockfile rust:renovate/onnxruntime-web-1.x-lockfile rust:renovate/node-22.x-lockfile rust:marine rust:mecab rust:v0.1.x
rust:commit-b54874a7 rust:commit-e23d0ebf rust:commit-9b5cf6d7 rust:commit-7c0991a1 rust:commit-783a5e65 rust:commit-8f3b7ff5 rust:commit-84a16766 rust:v0.2.0 rust:commit-42cbdd5f rust:commit-896f1e52 rust:commit-9f543377 rust:commit-2cb661fc rust:commit-d6471ad9 rust:commit-75c44553 rust:commit-767bfadf rust:commit-82c9f13c rust:commit-a7e6fadf rust:commit-7034c1aa rust:commit-f00ce040 rust:commit-7d32db19 rust:v0.2.0-alpha9 rust:commit-2cb43aaf rust:commit-d5a0f16b rust:commit-9fbe7579 rust:commit-ca5887e8 rust:commit-f587a2c7 rust:commit-36f031bf rust:commit-47f2af14 rust:commit-b9e2c0fe rust:commit-0651b492 rust:commit-56e663c3 rust:commit-8d97d422 rust:commit-404fa0f7 rust:commit-33d6f1cc rust:commit-8e031387 rust:commit-51c0bf85 rust:commit-043f380c rust:commit-f98c6b82 rust:commit-703035cc rust:commit-3553d01a rust:commit-91ac2fad rust:commit-93c121da rust:v0.2.0-alpha8 rust:commit-69d2857d rust:commit-5cb4480a rust:commit-ebcb6f90 rust:commit-e47f57ad rust:commit-dc5fa884 rust:commit-25ca89e3 rust:commit-84e9118d rust:commit-d5fcacd7 rust:commit-3050cc1e rust:commit-9f22694d rust:commit-62ba2c80 rust:commit-e9ced32b rust:commit-e7a1575c rust:commit-320664ea rust:commit-55f05580 rust:commit-1725863f rust:commit-87903827 rust:commit-ee56e959 rust:commit-00f4787f rust:commit-4b6c72aa rust:commit-e9529be5 rust:commit-a6694b5d rust:commit-096859de rust:v0.2.0-alpha7 rust:commit-98ddaa3c rust:commit-120bc608 rust:commit-45c3255a rust:commit-159f5802 rust:commit-9d6aa46f rust:commit-02ac0885 rust:commit-1f96b09f rust:commit-d583c1ca rust:commit-61914129 rust:commit-97c63a2e rust:commit-bca6d04e rust:commit-96b53d42 rust:commit-e68f58d6 rust:commit-1de09597 rust:commit-38d86c92 rust:commit-2124fe46 rust:commit-0217c0a4 rust:commit-d7065ac6 rust:commit-1ad588bf rust:commit-843c1699 rust:v0.2.0-alpha6 rust:commit-9733ba95 rust:commit-f0821ea9 rust:commit-e5e92f62 rust:commit-3caf9344 rust:commit-9052fd41 rust:commit-b8355773 rust:commit-9174aa9b rust:commit-bbb3f000 rust:commit-6bccf046 rust:commit-4deefc59 rust:commit-306284c7 rust:commit-bf79a063 rust:commit-ed5fa41c rust:commit-eaff6102 rust:commit-b5aa4891 rust:commit-b007e1b1 rust:commit-f081b2ed rust:commit-cb125023 rust:commit-4ae9b63b rust:commit-1089dc1a rust:commit-110e0753 rust:commit-9063e620 rust:commit-8a0b2d56 rust:commit-463d988a rust:commit-990e7e9b rust:commit-04e50088 rust:commit-e9811765 rust:commit-9a8b4fde rust:commit-5a805342 rust:commit-9e2534fa rust:commit-3bc1d7ce rust:commit-8913148f rust:commit-1d57a58c rust:commit-de3dfb50 rust:commit-dd84c3a9 rust:commit-a8bd62ce rust:commit-7b20a414 rust:commit-33cc9581 rust:commit-c4d2f017 rust:commit-3142b1ea rust:commit-0ab1da9e rust:commit-99a4b130 rust:commit-e8ee2c06 rust:commit-61aae68d rust:commit-ecf58659 rust:commit-f86d3233 rust:commit-7bf2c487 rust:commit-adb699ef rust:commit-8e8f58b4 rust:commit-38c5471d rust:v0.2.0-alpha.5 rust:v0.2.0-rc.8 rust:v0.2.0-rc.7 rust:v0.2.0-rc.6 rust:v0.2.0-rc.5 rust:v0.2.0-rc.4 rust:v0.2.0-rc.3 rust:v0.2.0-rc.2 rust:v0.2.0-rc.1 rust:v0.2.0-rc rust:v0.2.0-alpha5 rust:v0.2.0-alpha4 rust:v0.2.0-alpha3 rust:v0.2.0-alpha2-1 rust:v0.2.0-alpha2 rust:v0.2.0-alpha1 rust:v0.2.0-alpha rust:py-v0.1.1 rust:py-v0.1.0 rust:v0.1.4-1 rust:v0.1.4

2 Commits
commit-8f3 ... mecab

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
85 changed files with 2240 additions and 4393 deletions
Expand all files Collapse all files

3
.env.sample
View File

@@ -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

3
.github/FUNDING.yml vendored
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@@ -1,3 +0,0 @@
# These are supported funding model platforms
github: [tuna2134]

15
.github/dependabot.yml vendored
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@@ -1,15 +0,0 @@
# To get started with Dependabot version updates, you'll need to specify which
# package ecosystems to update and where the package manifests are located.
# Please see the documentation for all configuration options:
# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
version: 2
updates:
- package-ecosystem: "cargo" # See documentation for possible values
directory: "/" # Location of package manifests
schedule:
interval: "weekly"
- package-ecosystem: "npm" # See documentation for possible values
directory: "/" # Location of package manifests
schedule:
interval: "weekly"

150
.github/workflows/build.yml → .github/workflows/CI.yml vendored
View File

@@ -1,34 +1,39 @@
name: Build
# 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: ${{ matrix.platform.runner }}
strategy:
matrix:
platform:
- runner: ubuntu-latest
target: x86_64
- runner: ubuntu-24.04-arm
- runner: ubuntu-latest
target: aarch64
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: 3.x
- run: docker build . -f .github/workflows/build.Dockerfile --tag ci
- name: Build wheels
uses: PyO3/maturin-action@v1
with:
@@ -36,15 +41,14 @@ jobs:
args: --release --out dist --find-interpreter
sccache: 'true'
manylinux: auto
container: ci
working-directory: ./crates/sbv2_bindings
working-directory: sbv2_bindings
- name: Upload wheels
uses: actions/upload-artifact@v4
with:
name: wheels-linux-${{ matrix.platform.target }}
path: ./crates/sbv2_bindings/dist
path: sbv2_bindings/dist
python-windows:
windows:
runs-on: ${{ matrix.platform.runner }}
strategy:
matrix:
@@ -63,18 +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:
@@ -88,14 +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
@@ -104,117 +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
path: 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-*/*
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 }}

4
.github/workflows/build.Dockerfile vendored
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@@ -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

26
.github/workflows/lint.yml vendored
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@@ -1,26 +0,0 @@
name: Lint
on:
pull_request:
jobs:
check:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
components:
- rustfmt
- clippy
steps:
- name: Setup
uses: actions/checkout@v4
- uses: actions-rust-lang/setup-rust-toolchain@v1
with:
components: ${{ matrix.components }}
- name: Format
if: ${{ matrix.components == 'rustfmt' }}
run: cargo fmt --all -- --check
- name: Lint
if: ${{ matrix.components == 'clippy' }}
run: cargo clippy --all-targets --all-features -- -D warnings

10
.gitignore vendored
View File

@@ -1,10 +1,8 @@
target/
target
models/
!models/.gitkeep
venv/
.env
*.wav
node_modules/
dist/
*.csv
*.bin
output.wav
node_modules
dist/

2705
Cargo.lock generated
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File diff suppressed because it is too large Load Diff

26
Cargo.toml
View File

@@ -1,25 +1,15 @@
[workspace]
resolver = "3"
members = ["./crates/sbv2_api", "./crates/sbv2_core", "./crates/sbv2_bindings", "./crates/sbv2_wasm"]
[workspace.package]
version = "0.2.0"
edition = "2021"
description = "Style-Bert-VITSの推論ライブラリ"
license = "MIT"
readme = "./README.md"
repository = "https://github.com/neodyland/sbv2-api"
documentation = "https://docs.rs/sbv2_core"
resolver = "2"
members = ["sbv2_api", "sbv2_core", "sbv2_bindings", "sbv2_wasm"]
[workspace.dependencies]
anyhow = "1.0.100"
anyhow = "1.0.86"
dotenvy = "0.15.7"
env_logger = "0.11.6"
ndarray = "0.17.1"
once_cell = "1.20.3"
env_logger = "0.11.5"
ndarray = "0.16.1"
once_cell = "1.19.0"
[profile.release]
strip = true
opt-level = "z"
lto = true
codegen-units = 1
debug = false
strip = true

1
LICENSE.md → LICENSE
View File

@@ -1,7 +1,6 @@
MIT License
Copyright (c) 2024 tuna2134
Copyright (c) 2025- neodyland
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal

48
README.md
View File

@@ -1,23 +1,11 @@
# SBV2-API
> [!CAUTION]
> 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/neodyland/sbv2-api/tree/v0.1.x)をご覧ください。
> [!CAUTION]
> 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。
> [!NOTE]
> このレポジトリはメンテナンスの都合上、[tuna2134](https:://github.com/tuna2134)氏の所属する[Neodyland](https://neody.land/)へとリポジトリ所在地を移動しました。
>
> 引き続きtuna2134氏がメインメンテナとして管理しています。
## 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。
## 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)を参照してください。
[こちら](https://github.com/tuna2134/sbv2-gui?tab=readme-ov-file)を参照してください。
コマンドやpythonの知識なしで簡単に使えるバージョンです。(できることはほぼ同じ)
@@ -29,7 +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/neodyland/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
@@ -41,27 +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
@@ -75,7 +59,7 @@ CPUの場合は
```sh
docker run -it --rm -p 3000:3000 --name sbv2 \
-v ./models:/work/models --env-file .env \
ghcr.io/neodyland/sbv2-api:cpu
ghcr.io/tuna2134/sbv2-api:cpu
```
<details>
@@ -90,7 +74,7 @@ CPUの場合は
```bash
docker run --platform linux/amd64 -it --rm -p 3000:3000 --name sbv2 \
-v ./models:/work/models --env-file .env \
ghcr.io/neodyland/sbv2-api:cpu
ghcr.io/tuna2134/sbv2-api:cpu
```
</details>
@@ -99,7 +83,7 @@ CUDAの場合は
docker run -it --rm -p 3000:3000 --name sbv2 \
-v ./models:/work/models --env-file .env \
--gpus all \
ghcr.io/neodyland/sbv2-api:cuda
ghcr.io/tuna2134/sbv2-api:cuda
```
### 起動確認
@@ -130,10 +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

14
content.txt Normal file
View File

@@ -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。

0
scripts/convert/README.md → convert/README.md
View File

6
scripts/convert/convert_deberta.py → convert/convert_deberta.py
View File

@@ -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")

26
scripts/convert/convert_model.py → convert/convert_model.py
View File

@@ -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")

5
convert/requirements.txt Normal file
View File

@@ -0,0 +1,5 @@
style-bert-vits2
onnxsim
numpy<2
zstandard
onnxruntime

29
crates/sbv2_api/Cargo.toml
View File

@@ -1,29 +0,0 @@
[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
[dependencies]
anyhow.workspace = true
axum = "0.8.7"
dotenvy.workspace = true
env_logger.workspace = true
log = "0.4.28"
sbv2_core = { version = "0.2.0-alpha6", path = "../sbv2_core", features = ["aivmx"] }
serde = { version = "1.0.228", features = ["derive"] }
tokio = { version = "1.48.0", features = ["full"] }
utoipa = { version = "5.4.0", features = ["axum_extras"] }
utoipa-scalar = { version = "0.3.0", features = ["axum"] }
[features]
coreml = ["sbv2_core/coreml"]
cuda = ["sbv2_core/cuda"]
cuda_tf32 = ["sbv2_core/cuda_tf32"]
dynamic = ["sbv2_core/dynamic"]
directml = ["sbv2_core/directml"]
tensorrt = ["sbv2_core/tensorrt"]

24
crates/sbv2_bindings/Cargo.toml
View File

@@ -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 = "0.27.1", features = ["anyhow"] }
sbv2_core = { path = "../sbv2_core", features = ["std"], default-features = false }
[features]
agpl_dict = ["sbv2_core/agpl_dict"]
default = ["agpl_dict"]

47
crates/sbv2_core/Cargo.toml
View File

@@ -1,47 +0,0 @@
[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
[dependencies]
anyhow.workspace = true
base64 = { version = "0.22.1", optional = true }
dotenvy.workspace = true
env_logger.workspace = true
hound = "3.5.1"
jpreprocess = { version = "0.13.2", features = ["naist-jdic"] }
ndarray.workspace = true
npyz = { version = "0.8.4", optional = true }
num_cpus = "1.17.0"
once_cell.workspace = true
ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.9", optional = true }
regex = "1.12.2"
serde = { version = "1.0.228", features = ["derive"] }
serde_json = "1.0.145"
tar = "0.4.41"
thiserror = "2.0.17"
tokenizers = { version = "0.22.1", default-features = false }
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"]
no_std = ["tokenizers/unstable_wasm"]
aivmx = ["npyz", "base64"]
base64 = ["dep:base64"]
[build-dependencies]
dirs = "6.0.0"
ureq = "3.1.4"

31
crates/sbv2_core/build.rs
View File

@@ -1,31 +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_dir = home_dir().unwrap().join(".cache/sbv2");
let static_path = static_dir.join("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() {
println!("cargo:info=Dictionary file already exists, skipping download.");
} 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();
if !static_dir.exists() {
fs::create_dir_all(static_dir)?;
}
let mut file = fs::File::create(&static_path)?;
copy(&mut response, &mut file)?;
}
if !out_path.exists() && fs::hard_link(&static_path, &out_path).is_err() {
println!("cargo:warning=Failed to create hard link, copying instead.");
fs::copy(static_path, out_path)?;
}
Ok(())
}

22
crates/sbv2_core/src/bert.rs
View File

@@ -1,22 +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_array::<f32>()?
.into_dimensionality::<Ix2>()?
.to_owned();
Ok(output)
}

48
crates/sbv2_core/src/main.rs
View File

@@ -1,48 +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}");
}
}

106
crates/sbv2_core/src/model.rs
View File

@@ -1,106 +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();
#[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_array::<f32>()?
.into_dimensionality::<Ix3>()?
.to_owned();
Ok(audio_array)
}

470
crates/sbv2_core/src/tts.rs
View File

@@ -1,470 +0,0 @@
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 tokenizers::Tokenizer;
#[derive(PartialEq, Eq, Clone)]
pub struct TTSIdent(String);
impl std::fmt::Display for TTSIdent {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.write_str(&self.0)?;
Ok(())
}
}
impl<S> From<S> for TTSIdent
where
S: AsRef<str>,
{
fn from(value: S) -> Self {
TTSIdent(value.as_ref().to_string())
}
}
pub struct TTSModel {
vits2: Option<Session>,
style_vectors: Array2<f32>,
ident: TTSIdent,
bytes: Option<Vec<u8>>,
}
/// High-level Style-Bert-VITS2's API
pub struct TTSModelHolder {
tokenizer: Tokenizer,
bert: Session,
models: Vec<TTSModel>,
pub jtalk: jtalk::JTalk,
max_loaded_models: Option<usize>,
}
impl TTSModelHolder {
/// Initialize a new TTSModelHolder
///
/// # Examples
///
/// ```rs
/// let mut tts_holder = TTSModelHolder::new(std::fs::read("deberta.onnx")?, std::fs::read("tokenizer.json")?, None)?;
/// ```
pub fn new<P: AsRef<[u8]>>(
bert_model_bytes: P,
tokenizer_bytes: P,
max_loaded_models: Option<usize>,
) -> Result<Self> {
let bert = model::load_model(bert_model_bytes, true)?;
let jtalk = jtalk::JTalk::new()?;
let tokenizer = tokenizer::get_tokenizer(tokenizer_bytes)?;
Ok(TTSModelHolder {
bert,
models: vec![],
jtalk,
tokenizer,
max_loaded_models,
})
}
/// Return a list of model names
pub fn models(&self) -> Vec<String> {
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
///
/// ```rs
/// tts_holder.load_sbv2file("tsukuyomi", std::fs::read("tsukuyomi.sbv2")?)?;
/// ```
pub fn load_sbv2file<I: Into<TTSIdent>, P: AsRef<[u8]>>(
&mut self,
ident: I,
sbv2_bytes: P,
) -> Result<()> {
let (style_vectors, vits2) = crate::sbv2file::parse_sbv2file(sbv2_bytes)?;
self.load(ident, style_vectors, vits2)?;
Ok(())
}
/// Load a style vector and onnx model binary
///
/// # Examples
///
/// ```rs
/// tts_holder.load("tsukuyomi", std::fs::read("style_vectors.json")?, std::fs::read("model.onnx")?)?;
/// ```
pub fn load<I: Into<TTSIdent>, P: AsRef<[u8]>>(
&mut self,
ident: I,
style_vectors_bytes: P,
vits2_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;
}
}
self.models.push(TTSModel {
vits2: if load {
Some(model::load_model(&vits2_bytes, false)?)
} else {
None
},
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(())
}
/// Unload a model
pub fn unload<I: Into<TTSIdent>>(&mut self, ident: I) -> bool {
let ident = ident.into();
if let Some((i, _)) = self
.models
.iter()
.enumerate()
.find(|(_, m)| m.ident == ident)
{
self.models.remove(i);
true
} else {
false
}
}
/// Parse text and return the input for synthesize
///
/// # Note
/// This function is for low-level usage, use `easy_synthesize` for high-level usage.
#[allow(clippy::type_complexity)]
pub fn parse_text(
&mut self,
text: &str,
) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> {
crate::tts_util::parse_text_blocking(
text,
None,
&self.jtalk,
&self.tokenizer,
|token_ids, attention_masks| {
crate::bert::predict(&mut self.bert, token_ids, attention_masks)
},
)
}
#[allow(clippy::type_complexity)]
pub fn parse_text_neo(
&mut self,
text: String,
given_tones: Option<Vec<i32>>,
) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> {
crate::tts_util::parse_text_blocking(
&text,
given_tones,
&self.jtalk,
&self.tokenizer,
|token_ids, attention_masks| {
crate::bert::predict(&mut self.bert, token_ids, attention_masks)
},
)
}
fn find_model<I: Into<TTSIdent>>(&mut self, ident: I) -> Result<&mut 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();
// Locate target model entry
let target_index = self
.models
.iter()
.position(|m| m.ident == ident)
.ok_or(Error::ModelNotFoundError(ident.to_string()))?;
// Already loaded
if self.models[target_index].vits2.is_some() {
return Ok(true);
}
// Get bytes to build a Session
let bytes = self.models[target_index]
.bytes
.clone()
.ok_or(Error::ModelNotFoundError(ident.to_string()))?;
// Enforce max loaded models by evicting a different loaded model's session, not removing the entry
if let Some(max) = self.max_loaded_models {
let loaded_count = self.models.iter().filter(|m| m.vits2.is_some()).count();
if loaded_count >= max {
if let Some(evict_index) = self
.models
.iter()
.position(|m| m.vits2.is_some() && m.ident != ident)
{
// Drop only the session to free memory; keep bytes/style for future reload
self.models[evict_index].vits2 = None;
}
}
}
// Build and set session in-place for the target model
let s = model::load_model(&bytes, false)?;
self.models[target_index].vits2 = Some(s);
Ok(true)
}
/// Get style vector by style id and weight
///
/// # 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,
ident: I,
style_id: i32,
weight: f32,
) -> Result<Array1<f32>> {
style::get_style_vector(&self.find_model(ident)?.style_vectors, style_id, weight)
}
/// Synthesize text to audio
///
/// # Examples
///
/// ```rs
/// 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,
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();
let mut audios = vec![];
for (i, t) in texts.iter().enumerate() {
if t.is_empty() {
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,
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 {
audios.push(Array3::zeros((1, 1, 22050)));
}
}
concatenate(
Axis(2),
&audios.iter().map(|x| x.view()).collect::<Vec<_>>(),
)?
} 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,
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)
}
pub fn easy_synthesize_neo<I: Into<TTSIdent> + Copy>(
&mut self,
ident: I,
text: &str,
given_tones: Option<Vec<i32>>,
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();
let mut audios = vec![];
for (i, t) in texts.iter().enumerate() {
if t.is_empty() {
continue;
}
let (bert_ori, phones, tones, lang_ids) =
self.parse_text_neo(t.to_string(), given_tones.clone())?;
let vits2 = self
.find_model(ident)?
.vits2
.as_mut()
.ok_or(Error::ModelNotFoundError(ident.into().to_string()))?;
let audio = model::synthesize(
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 {
audios.push(Array3::zeros((1, 1, 22050)));
}
}
concatenate(
Axis(2),
&audios.iter().map(|x| x.view()).collect::<Vec<_>>(),
)?
} 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,
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 options
///
/// # Fields
/// - `sdp_ratio`: SDP ratio
/// - `length_scale`: Length scale
/// - `style_weight`: Style weight
/// - `split_sentences`: Split sentences
pub struct SynthesizeOptions {
pub sdp_ratio: f32,
pub length_scale: f32,
pub style_weight: f32,
pub split_sentences: bool,
}
impl Default for SynthesizeOptions {
fn default() -> Self {
SynthesizeOptions {
sdp_ratio: 0.0,
length_scale: 1.0,
style_weight: 1.0,
split_sentences: true,
}
}
}

19
crates/sbv2_editor/Cargo.toml
View File

@@ -1,19 +0,0 @@
[package]
name = "sbv2_editor"
version.workspace = true
edition.workspace = true
description.workspace = true
license.workspace = true
readme.workspace = true
repository.workspace = true
documentation.workspace = true
[dependencies]
anyhow.workspace = true
axum = "0.8.1"
dotenvy.workspace = true
env_logger.workspace = true
log = "0.4.27"
sbv2_core = { version = "0.2.0-alpha6", path = "../sbv2_core", features = ["aivmx"] }
serde = { version = "1.0.219", features = ["derive"] }
tokio = { version = "1.44.1", features = ["full"] }

2
crates/sbv2_editor/README.md
View File

@@ -1,2 +0,0 @@
# sbv2-voicevox
sbv2-apiをvoicevox化します。

226
crates/sbv2_editor/query2.json
View File

@@ -1,226 +0,0 @@
{
"accent_phrases": [
{
"moras": [
{
"text": "コ",
"consonant": "k",
"consonant_length": 0.10002632439136505,
"vowel": "o",
"vowel_length": 0.15740256011486053,
"pitch": 5.749961853027344
},
{
"text": "ン",
"consonant": null,
"consonant_length": null,
"vowel": "N",
"vowel_length": 0.08265873789787292,
"pitch": 5.89122200012207
},
{
"text": "ニ",
"consonant": "n",
"consonant_length": 0.03657080978155136,
"vowel": "i",
"vowel_length": 0.1175866425037384,
"pitch": 5.969866752624512
},
{
"text": "チ",
"consonant": "ch",
"consonant_length": 0.09005842357873917,
"vowel": "i",
"vowel_length": 0.08666137605905533,
"pitch": 5.958892822265625
},
{
"text": "ワ",
"consonant": "w",
"consonant_length": 0.07833231985569,
"vowel": "a",
"vowel_length": 0.21250136196613312,
"pitch": 5.949411392211914
}
],
"accent": 5,
"pause_mora": {
"text": "、",
"consonant": null,
"consonant_length": null,
"vowel": "pau",
"vowel_length": 0.4723339378833771,
"pitch": 0.0
},
"is_interrogative": false
},
{
"moras": [
{
"text": "オ",
"consonant": null,
"consonant_length": null,
"vowel": "o",
"vowel_length": 0.22004225850105286,
"pitch": 5.6870927810668945
},
{
"text": "ン",
"consonant": null,
"consonant_length": null,
"vowel": "N",
"vowel_length": 0.09161105751991272,
"pitch": 5.93472957611084
},
{
"text": "セ",
"consonant": "s",
"consonant_length": 0.08924821764230728,
"vowel": "e",
"vowel_length": 0.14142127335071564,
"pitch": 6.121850490570068
},
{
"text": "エ",
"consonant": null,
"consonant_length": null,
"vowel": "e",
"vowel_length": 0.10636933892965317,
"pitch": 6.157896041870117
},
{
"text": "ゴ",
"consonant": "g",
"consonant_length": 0.07600915431976318,
"vowel": "o",
"vowel_length": 0.09598273783922195,
"pitch": 6.188933849334717
},
{
"text": "オ",
"consonant": null,
"consonant_length": null,
"vowel": "o",
"vowel_length": 0.1079121008515358,
"pitch": 6.235202789306641
},
{
"text": "セ",
"consonant": "s",
"consonant_length": 0.09591838717460632,
"vowel": "e",
"vowel_length": 0.10286372154951096,
"pitch": 6.153214454650879
},
{
"text": "エ",
"consonant": null,
"consonant_length": null,
"vowel": "e",
"vowel_length": 0.08992656320333481,
"pitch": 6.02571439743042
},
{
"text": "ノ",
"consonant": "n",
"consonant_length": 0.05660202354192734,
"vowel": "o",
"vowel_length": 0.09676017612218857,
"pitch": 5.711844444274902
}
],
"accent": 5,
"pause_mora": null,
"is_interrogative": false
},
{
"moras": [
{
"text": "セ",
"consonant": "s",
"consonant_length": 0.07805486768484116,
"vowel": "e",
"vowel_length": 0.09617523103952408,
"pitch": 5.774399280548096
},
{
"text": "カ",
"consonant": "k",
"consonant_length": 0.06712044775485992,
"vowel": "a",
"vowel_length": 0.148829385638237,
"pitch": 6.063965797424316
},
{
"text": "イ",
"consonant": null,
"consonant_length": null,
"vowel": "i",
"vowel_length": 0.11061104387044907,
"pitch": 6.040698051452637
},
{
"text": "エ",
"consonant": null,
"consonant_length": null,
"vowel": "e",
"vowel_length": 0.13046696782112122,
"pitch": 5.806027889251709
}
],
"accent": 1,
"pause_mora": null,
"is_interrogative": false
},
{
"moras": [
{
"text": "ヨ",
"consonant": "y",
"consonant_length": 0.07194744795560837,
"vowel": "o",
"vowel_length": 0.08622600883245468,
"pitch": 5.694094657897949
},
{
"text": "オ",
"consonant": null,
"consonant_length": null,
"vowel": "o",
"vowel_length": 0.10635452717542648,
"pitch": 5.787222385406494
},
{
"text": "コ",
"consonant": "k",
"consonant_length": 0.07077334076166153,
"vowel": "o",
"vowel_length": 0.09248624742031097,
"pitch": 5.793357849121094
},
{
"text": "ソ",
"consonant": "s",
"consonant_length": 0.08705667406320572,
"vowel": "o",
"vowel_length": 0.2238258570432663,
"pitch": 5.643765449523926
}
],
"accent": 1,
"pause_mora": null,
"is_interrogative": false
}
],
"speedScale": 1.0,
"pitchScale": 0.0,
"intonationScale": 1.0,
"volumeScale": 1.0,
"prePhonemeLength": 0.1,
"postPhonemeLength": 0.1,
"pauseLength": null,
"pauseLengthScale": 1.0,
"outputSamplingRate": 24000,
"outputStereo": false,
"kana": "コンニチワ'、オンセエゴ'オセエノ/セ'カイエ/ヨ'オコソ"
}

27
crates/sbv2_editor/src/error.rs
View File

@@ -1,27 +0,0 @@
use axum::{
http::StatusCode,
response::{IntoResponse, Response},
};
pub type AppResult<T> = std::result::Result<T, AppError>;
pub struct AppError(anyhow::Error);
impl IntoResponse for AppError {
fn into_response(self) -> Response {
(
StatusCode::INTERNAL_SERVER_ERROR,
format!("Something went wrong: {}", self.0),
)
.into_response()
}
}
impl<E> From<E> for AppError
where
E: Into<anyhow::Error>,
{
fn from(err: E) -> Self {
Self(err.into())
}
}

197
crates/sbv2_editor/src/main.rs
View File

@@ -1,197 +0,0 @@
use axum::extract::State;
use axum::{
extract::Query,
http::header::CONTENT_TYPE,
response::IntoResponse,
routing::{get, post},
Json, Router,
};
use sbv2_core::tts_util::kata_tone2phone_tone;
use sbv2_core::{
tts::{SynthesizeOptions, TTSModelHolder},
tts_util::preprocess_parse_text,
};
use serde::{Deserialize, Serialize};
use tokio::{fs, net::TcpListener, sync::Mutex};
use std::env;
use std::sync::Arc;
use error::AppResult;
mod error;
#[derive(Deserialize)]
struct RequestCreateAudioQuery {
text: String,
}
#[derive(Serialize, Deserialize)]
struct AudioQuery {
kana: String,
tone: i32,
}
#[derive(Serialize)]
struct ResponseCreateAudioQuery {
audio_query: Vec<AudioQuery>,
text: String,
}
async fn create_audio_query(
State(state): State<AppState>,
Query(request): Query<RequestCreateAudioQuery>,
) -> AppResult<impl IntoResponse> {
let (text, process) = {
let tts_model = state.tts_model.lock().await;
preprocess_parse_text(&request.text, &tts_model.jtalk)?
};
let kana_tone_list = process.g2kana_tone()?;
let audio_query = kana_tone_list
.iter()
.map(|(kana, tone)| AudioQuery {
kana: kana.clone(),
tone: *tone,
})
.collect::<Vec<_>>();
Ok(Json(ResponseCreateAudioQuery { audio_query, text }))
}
#[derive(Deserialize)]
pub struct RequestSynthesis {
text: String,
speaker_id: i64,
sdp_ratio: f32,
length_scale: f32,
style_id: i32,
audio_query: Vec<AudioQuery>,
ident: String,
}
async fn synthesis(
State(state): State<AppState>,
Json(request): Json<RequestSynthesis>,
) -> AppResult<impl IntoResponse> {
let phone_tone = request
.audio_query
.iter()
.map(|query| (query.kana.clone(), query.tone))
.collect::<Vec<_>>();
let phone_tone = kata_tone2phone_tone(phone_tone);
let tones = phone_tone.iter().map(|(_, tone)| *tone).collect::<Vec<_>>();
let buffer = {
let mut tts_model = state.tts_model.lock().await;
tts_model.easy_synthesize_neo(
&request.ident,
&request.text,
Some(tones),
request.style_id,
request.speaker_id,
SynthesizeOptions {
sdp_ratio: request.sdp_ratio,
length_scale: request.length_scale,
..Default::default()
},
)?
};
Ok(([(CONTENT_TYPE, "audio/wav")], buffer))
}
#[derive(Clone)]
struct AppState {
tts_model: Arc<Mutex<TTSModelHolder>>,
}
impl AppState {
pub async fn new() -> anyhow::Result<Self> {
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?;
let mut entries = vec![];
while let Ok(Some(e)) = f.next_entry().await {
let name = e.file_name().to_string_lossy().to_string();
if name.ends_with(".onnx") && name.starts_with("model_") {
let name_len = name.len();
let name = name.chars();
entries.push(
name.collect::<Vec<_>>()[6..name_len - 5]
.iter()
.collect::<String>(),
);
} else if name.ends_with(".sbv2") {
let entry = &name[..name.len() - 5];
log::info!("Try loading: {entry}");
let sbv2_bytes = match fs::read(format!("{models}/{entry}.sbv2")).await {
Ok(b) => b,
Err(e) => {
log::warn!("Error loading sbv2_bytes from file {entry}: {e}");
continue;
}
};
if let Err(e) = tts_model.load_sbv2file(entry, sbv2_bytes) {
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 {
log::info!("Try loading: {entry}");
let style_vectors_bytes =
match fs::read(format!("{models}/style_vectors_{entry}.json")).await {
Ok(b) => b,
Err(e) => {
log::warn!("Error loading style_vectors_bytes from file {entry}: {e}");
continue;
}
};
let vits2_bytes = match fs::read(format!("{models}/model_{entry}.onnx")).await {
Ok(b) => b,
Err(e) => {
log::warn!("Error loading vits2_bytes from file {entry}: {e}");
continue;
}
};
if let Err(e) = tts_model.load(&entry, style_vectors_bytes, vits2_bytes) {
log::warn!("Error loading {entry}: {e}");
};
log::info!("Loaded: {entry}");
}
Ok(Self {
tts_model: Arc::new(Mutex::new(tts_model)),
})
}
}
#[tokio::main]
async fn main() -> anyhow::Result<()> {
dotenvy::dotenv_override().ok();
env_logger::init();
let app = Router::new()
.route("/", get(|| async { "Hello, world!" }))
.route("/audio_query", get(create_audio_query))
.route("/synthesis", post(synthesis))
.with_state(AppState::new().await?);
let listener = TcpListener::bind("0.0.0.0:8080").await?;
axum::serve(listener, app).await?;
Ok(())
}

2
crates/sbv2_wasm/README.md
View File

@@ -1,2 +0,0 @@
# StyleBertVITS2 wasm
refer to https://github.com/neodyland/sbv2-api

8
crates/sbv2_wasm/build.sh
View File

@@ -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

504
crates/sbv2_wasm/pnpm-lock.yaml generated
View File

@@ -1,504 +0,0 @@
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'@esbuild/freebsd-arm64': 0.25.0
'@esbuild/freebsd-x64': 0.25.0
'@esbuild/linux-arm': 0.25.0
'@esbuild/linux-arm64': 0.25.0
'@esbuild/linux-ia32': 0.25.0
'@esbuild/linux-loong64': 0.25.0
'@esbuild/linux-mips64el': 0.25.0
'@esbuild/linux-ppc64': 0.25.0
'@esbuild/linux-riscv64': 0.25.0
'@esbuild/linux-s390x': 0.25.0
'@esbuild/linux-x64': 0.25.0
'@esbuild/netbsd-arm64': 0.25.0
'@esbuild/netbsd-x64': 0.25.0
'@esbuild/openbsd-arm64': 0.25.0
'@esbuild/openbsd-x64': 0.25.0
'@esbuild/sunos-x64': 0.25.0
'@esbuild/win32-arm64': 0.25.0
'@esbuild/win32-ia32': 0.25.0
'@esbuild/win32-x64': 0.25.0
flatbuffers@1.12.0: {}
guid-typescript@1.0.9: {}
long@5.3.1: {}
onnxruntime-common@1.20.1: {}
onnxruntime-web@1.20.1:
dependencies:
flatbuffers: 1.12.0
guid-typescript: 1.0.9
long: 5.3.1
onnxruntime-common: 1.20.1
platform: 1.3.6
protobufjs: 7.4.0
platform@1.3.6: {}
protobufjs@7.4.0:
dependencies:
'@protobufjs/aspromise': 1.1.2
'@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': 22.13.5
long: 5.3.1
typescript@5.8.3: {}
undici-types@6.20.0: {}

9
docker/cpu.Dockerfile Normal file
View File

@@ -0,0 +1,9 @@
FROM rust AS builder
WORKDIR /work
COPY . .
RUN cargo build -r --bin sbv2_api
FROM gcr.io/distroless/cc-debian12
WORKDIR /work
COPY --from=builder /work/target/release/sbv2_api /work/main
COPY --from=builder /work/target/release/*.so /work
CMD ["/work/main"]

13
scripts/docker/cuda.Dockerfile → docker/cuda.Dockerfile
View File

@@ -2,16 +2,9 @@ FROM rust AS builder
WORKDIR /work
COPY . .
RUN cargo build -r --bin sbv2_api -F cuda,cuda_tf32
FROM ubuntu AS upx
WORKDIR /work
RUN apt update && apt-get install -y upx binutils
COPY --from=builder /work/target/release/sbv2_api /work/main
COPY --from=builder /work/target/release/*.so /work
RUN upx --best --lzma /work/main
RUN find /work -maxdepth 1 -name "*.so" -exec strip --strip-unneeded {} +
FROM nvidia/cuda:12.3.2-cudnn9-runtime-ubuntu22.04
WORKDIR /work
COPY --from=upx /work/main /work/main
COPY --from=upx /work/*.so /work
COPY --from=builder /work/target/release/sbv2_api /work/main
COPY --from=builder /work/target/release/*.so /work
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/work
CMD ["/work/main"]
CMD ["/work/main"]

2
scripts/docker/run_cpu.sh → docker/run_cpu.sh
View File

@@ -1,3 +1,3 @@
docker run -it --rm -p 3000:3000 --name sbv2 \
-v ./models:/work/models --env-file .env \
ghcr.io/neodyland/sbv2-api:cpu
ghcr.io/tuna2134/sbv2-api:cpu

2
scripts/docker/run_cuda.sh → docker/run_cuda.sh
View File

@@ -1,4 +1,4 @@
docker run -it --rm -p 3000:3000 --name sbv2 \
-v ./models:/work/models --env-file .env \
--gpus all \
ghcr.io/neodyland/sbv2-api:cuda
ghcr.io/tuna2134/sbv2-api:cuda

24
sbv2_api/Cargo.toml Normal file
View File

@@ -0,0 +1,24 @@
[package]
name = "sbv2_api"
version = "0.2.0-alpha"
edition = "2021"
[dependencies]
anyhow.workspace = true
axum = "0.7.5"
dotenvy.workspace = true
env_logger.workspace = true
log = "0.4.22"
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 = "4.2.3", features = ["axum_extras"] }
utoipa-scalar = { version = "0.1.0", features = ["axum"] }
[features]
coreml = ["sbv2_core/coreml"]
cuda = ["sbv2_core/cuda"]
cuda_tf32 = ["sbv2_core/cuda_tf32"]
dynamic = ["sbv2_core/dynamic"]
directml = ["sbv2_core/directml"]
tensorrt = ["sbv2_core/tensorrt"]

0
crates/sbv2_api/build.rs → sbv2_api/build.rs
View File

0
crates/sbv2_api/src/error.rs → sbv2_api/src/error.rs
View File

40
crates/sbv2_api/src/main.rs → sbv2_api/src/main.rs
View File

@@ -40,30 +40,14 @@ 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,
ident: String,
#[serde(default = "sdp_default")]
#[schema(example = 0.0_f32)]
sdp_ratio: f32,
#[serde(default = "length_default")]
#[schema(example = 1.0_f32)]
length_scale: f32,
#[serde(default = "style_id_default")]
#[schema(example = 0_i32)]
style_id: i32,
#[serde(default = "speaker_id_default")]
#[schema(example = 0_i64)]
speaker_id: i64,
}
#[utoipa::path(
@@ -81,18 +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,
@@ -113,9 +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?;
@@ -144,20 +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 {

15
sbv2_bindings/Cargo.toml Normal file
View File

@@ -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" }

6
scripts/sbv2-test-bindings.py → sbv2_bindings/examples/basic.py
View File

@@ -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)
)

0
crates/sbv2_bindings/pyproject.toml → sbv2_bindings/pyproject.toml
View File

0
crates/sbv2_bindings/src/lib.rs → sbv2_bindings/src/lib.rs
View File

35
crates/sbv2_bindings/src/sbv2.rs → sbv2_bindings/src/sbv2.rs
View File

@@ -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,
@@ -136,29 +120,26 @@ impl TTSModel {
/// -------
/// voice_data : bytes
/// 音声データ
#[allow(clippy::too_many_arguments)]
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 {

0
crates/sbv2_bindings/src/style.rs → sbv2_bindings/src/style.rs
View File

40
sbv2_core/Cargo.toml Normal file
View File

@@ -0,0 +1,40 @@
[package]
name = "sbv2_core"
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
dotenvy.workspace = true
env_logger.workspace = true
hound = "3.5.1"
jpreprocess = { version = "0.10.0", features = ["naist-jdic"] }
ndarray.workspace = true
num_cpus = "1.16.0"
once_cell.workspace = 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 = "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"]
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"]
no_std = ["tokenizers/unstable_wasm"]
mecab = ["vibrato"]

0
crates/sbv2_core/mora_convert.py → sbv2_core/mora_convert.py
View File

23
sbv2_core/src/bert.rs Normal file
View File

@@ -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())
}

7
crates/sbv2_core/src/error.rs → sbv2_core/src/error.rs
View File

@@ -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),
@@ -23,13 +21,8 @@ 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),
#[error("Style error: {0}")]
StyleError(String),
}
pub type Result<T> = std::result::Result<T, Error>;

265
crates/sbv2_core/src/jtalk.rs → sbv2_core/src/jtalk.rs
View File

@@ -1,204 +1,21 @@
/*
このファイルのコードは
https://github.com/litagin02/Style-Bert-VITS2/blob/master/style_bert_vits2/nlp/japanese/g2p.py
を参考にRustに書き換えています。
以下はライセンスです。
GNU LESSER GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
This version of the GNU Lesser General Public License incorporates
the terms and conditions of version 3 of the GNU General Public
License, supplemented by the additional permissions listed below.
0. Additional Definitions.
As used herein, "this License" refers to version 3 of the GNU Lesser
General Public License, and the "GNU GPL" refers to version 3 of the GNU
General Public License.
"The Library" refers to a covered work governed by this License,
other than an Application or a Combined Work as defined below.
An "Application" is any work that makes use of an interface provided
by the Library, but which is not otherwise based on the Library.
Defining a subclass of a class defined by the Library is deemed a mode
of using an interface provided by the Library.
A "Combined Work" is a work produced by combining or linking an
Application with the Library. The particular version of the Library
with which the Combined Work was made is also called the "Linked
Version".
The "Minimal Corresponding Source" for a Combined Work means the
Corresponding Source for the Combined Work, excluding any source code
for portions of the Combined Work that, considered in isolation, are
based on the Application, and not on the Linked Version.
The "Corresponding Application Code" for a Combined Work means the
object code and/or source code for the Application, including any data
and utility programs needed for reproducing the Combined Work from the
Application, but excluding the System Libraries of the Combined Work.
1. Exception to Section 3 of the GNU GPL.
You may convey a covered work under sections 3 and 4 of this License
without being bound by section 3 of the GNU GPL.
2. Conveying Modified Versions.
If you modify a copy of the Library, and, in your modifications, a
facility refers to a function or data to be supplied by an Application
that uses the facility (other than as an argument passed when the
facility is invoked), then you may convey a copy of the modified
version:
a) under this License, provided that you make a good faith effort to
ensure that, in the event an Application does not supply the
function or data, the facility still operates, and performs
whatever part of its purpose remains meaningful, or
b) under the GNU GPL, with none of the additional permissions of
this License applicable to that copy.
3. Object Code Incorporating Material from Library Header Files.
The object code form of an Application may incorporate material from
a header file that is part of the Library. You may convey such object
code under terms of your choice, provided that, if the incorporated
material is not limited to numerical parameters, data structure
layouts and accessors, or small macros, inline functions and templates
(ten or fewer lines in length), you do both of the following:
a) Give prominent notice with each copy of the object code that the
Library is used in it and that the Library and its use are
covered by this License.
b) Accompany the object code with a copy of the GNU GPL and this license
document.
4. Combined Works.
You may convey a Combined Work under terms of your choice that,
taken together, effectively do not restrict modification of the
portions of the Library contained in the Combined Work and reverse
engineering for debugging such modifications, if you also do each of
the following:
a) Give prominent notice with each copy of the Combined Work that
the Library is used in it and that the Library and its use are
covered by this License.
b) Accompany the Combined Work with a copy of the GNU GPL and this license
document.
c) For a Combined Work that displays copyright notices during
execution, include the copyright notice for the Library among
these notices, as well as a reference directing the user to the
copies of the GNU GPL and this license document.
d) Do one of the following:
0) Convey the Minimal Corresponding Source under the terms of this
License, and the Corresponding Application Code in a form
suitable for, and under terms that permit, the user to
recombine or relink the Application with a modified version of
the Linked Version to produce a modified Combined Work, in the
manner specified by section 6 of the GNU GPL for conveying
Corresponding Source.
1) Use a suitable shared library mechanism for linking with the
Library. A suitable mechanism is one that (a) uses at run time
a copy of the Library already present on the user's computer
system, and (b) will operate properly with a modified version
of the Library that is interface-compatible with the Linked
Version.
e) Provide Installation Information, but only if you would otherwise
be required to provide such information under section 6 of the
GNU GPL, and only to the extent that such information is
necessary to install and execute a modified version of the
Combined Work produced by recombining or relinking the
Application with a modified version of the Linked Version. (If
you use option 4d0, the Installation Information must accompany
the Minimal Corresponding Source and Corresponding Application
Code. If you use option 4d1, you must provide the Installation
Information in the manner specified by section 6 of the GNU GPL
for conveying Corresponding Source.)
5. Combined Libraries.
You may place library facilities that are a work based on the
Library side by side in a single library together with other library
facilities that are not Applications and are not covered by this
License, and convey such a combined library under terms of your
choice, if you do both of the following:
a) Accompany the combined library with a copy of the same work based
on the Library, uncombined with any other library facilities,
conveyed under the terms of this License.
b) Give prominent notice with the combined library that part of it
is a work based on the Library, and explaining where to find the
accompanying uncombined form of the same work.
6. Revised Versions of the GNU Lesser General Public License.
The Free Software Foundation may publish revised and/or new versions
of the GNU Lesser General Public License from time to time. Such new
versions will be similar in spirit to the present version, but may
differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the
Library as you received it specifies that a certain numbered version
of the GNU Lesser General Public License "or any later version"
applies to it, you have the option of following the terms and
conditions either of that published version or of any later version
published by the Free Software Foundation. If the Library as you
received it does not specify a version number of the GNU Lesser
General Public License, you may choose any version of the GNU Lesser
General Public License ever published by the Free Software Foundation.
If the Library as you received it specifies that a proxy can decide
whether future versions of the GNU Lesser General Public License shall
apply, that proxy's public statement of acceptance of any version is
permanent authorization for you to choose that version for the
Library.
*/
use crate::error::{Error, Result};
use crate::mora::{CONSONANTS, MORA_KATA_TO_MORA_PHONEMES, MORA_PHONEMES_TO_MORA_KATA, VOWELS};
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)
}
@@ -248,34 +65,6 @@ static MORA_PATTERN: Lazy<Vec<String>> = Lazy::new(|| {
});
static LONG_PATTERN: Lazy<Regex> = Lazy::new(|| Regex::new(r"(\w)(ー*)").unwrap());
fn phone_tone_to_kana(phones: Vec<String>, tones: Vec<i32>) -> Vec<(String, i32)> {
let phones = &phones[1..];
let tones = &tones[1..];
let mut results = Vec::new();
let mut current_mora = String::new();
for ((phone, _next_phone), (&tone, &next_tone)) in phones
.iter()
.zip(phones.iter().skip(1))
.zip(tones.iter().zip(tones.iter().skip(1)))
{
if PUNCTUATIONS.contains(&phone.clone().as_str()) {
results.push((phone.to_string(), tone));
continue;
}
if CONSONANTS.contains(&phone.clone()) {
assert_eq!(current_mora, "");
assert_eq!(tone, next_tone);
current_mora = phone.to_string()
} else {
current_mora += phone;
let kana = MORA_PHONEMES_TO_MORA_KATA.get(&current_mora).unwrap();
results.push((kana.to_string(), tone));
current_mora = String::new();
}
}
results
}
pub struct JTalkProcess {
jpreprocess: Arc<JPreprocessType>,
parsed: Vec<String>,
@@ -295,24 +84,24 @@ impl JTalkProcess {
.map(|(_letter, tone)| *tone)
.collect();
if tone_values.len() == 1 {
assert!(tone_values == hash_set![0], "{tone_values:?}");
assert!(tone_values == hash_set![0], "{:?}", tone_values);
Ok(phone_tone_list)
} else if tone_values.len() == 2 {
if tone_values == hash_set![0, 1] {
Ok(phone_tone_list)
return Ok(phone_tone_list);
} else if tone_values == hash_set![-1, 0] {
Ok(phone_tone_list
return Ok(phone_tone_list
.iter()
.map(|x| {
let new_tone = if x.1 == -1 { 0 } else { 1 };
(x.0.clone(), new_tone)
})
.collect())
.collect());
} else {
Err(Error::ValueError("Invalid tone values 0".to_string()))
return Err(Error::ValueError("Invalid tone values 0".to_string()));
}
} else {
Err(Error::ValueError("Invalid tone values 1".to_string()))
return Err(Error::ValueError("Invalid tone values 1".to_string()));
}
}
@@ -365,11 +154,6 @@ impl JTalkProcess {
Ok((phones, tones, new_word2ph))
}
pub fn g2kana_tone(&self) -> Result<Vec<(String, i32)>> {
let (phones, tones, _) = self.g2p()?;
Ok(phone_tone_to_kana(phones, tones))
}
fn distribute_phone(n_phone: i32, n_word: i32) -> Vec<i32> {
let mut phones_per_word = vec![0; n_word as usize];
for _ in 0..n_phone {
@@ -398,12 +182,12 @@ impl JTalkProcess {
} else if PUNCTUATIONS.contains(&phone.as_str()) {
result.push((phone, 0));
} else {
println!("phones {phone_with_punct:?}");
println!("phone_tone_list: {phone_tone_list:?}");
println!("result: {result:?}");
println!("tone_index: {tone_index:?}");
println!("phone: {phone:?}");
return Err(Error::ValueError(format!("Mismatched phoneme: {phone}")));
println!("phones {:?}", phone_with_punct);
println!("phone_tone_list: {:?}", phone_tone_list);
println!("result: {:?}", result);
println!("tone_index: {:?}", tone_index);
println!("phone: {:?}", phone);
return Err(Error::ValueError(format!("Mismatched phoneme: {}", phone)));
}
}
@@ -448,7 +232,8 @@ impl JTalkProcess {
}
if !KATAKANA_PATTERN.is_match(&text) {
return Err(Error::ValueError(format!(
"Input must be katakana only: {text}"
"Input must be katakana only: {}",
text
)));
}
@@ -456,7 +241,7 @@ impl JTalkProcess {
let mora = mora.to_string();
let (consonant, vowel) = MORA_KATA_TO_MORA_PHONEMES.get(&mora).unwrap();
if consonant.is_none() {
text = text.replace(&mora, &format!(" {vowel}"));
text = text.replace(&mora, &format!(" {}", vowel));
} else {
text = text.replace(
&mora,
@@ -490,7 +275,7 @@ impl JTalkProcess {
let (string, pron) = self.parse_to_string_and_pron(parts.clone());
let mut yomi = pron.replace('’', "");
let word = replace_punctuation(string);
assert!(!yomi.is_empty(), "Empty yomi: {word}");
assert!(!yomi.is_empty(), "Empty yomi: {}", word);
if yomi == "、" {
if !word
.chars()
@@ -501,7 +286,7 @@ impl JTalkProcess {
yomi = word.clone();
}
} else if yomi == "？" {
assert!(word == "?", "yomi `？` comes from: {word}");
assert!(word == "?", "yomi `？` comes from: {}", word);
yomi = "?".to_string();
}
seq_text.push(word);

3
crates/sbv2_core/src/lib.rs → sbv2_core/src/lib.rs
View File

@@ -14,3 +14,6 @@ pub mod tokenizer;
pub mod tts;
pub mod tts_util;
pub mod utils;
#[cfg(feature = "mecab")]
pub mod mecab;

31
sbv2_core/src/main.rs Normal file
View File

@@ -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}");
}
}

0
sbv2_core/src/mecab.rs Normal file
View File

93
sbv2_core/src/model.rs Normal file
View File

@@ -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,
)?)
}

23
crates/sbv2_core/src/mora.rs → sbv2_core/src/mora.rs
View File

@@ -25,21 +25,6 @@ static MORA_LIST_ADDITIONAL: Lazy<Vec<Mora>> = Lazy::new(|| {
data.additional
});
pub static MORA_PHONEMES_TO_MORA_KATA: Lazy<HashMap<String, String>> = Lazy::new(|| {
let mut map = HashMap::new();
for mora in MORA_LIST_MINIMUM.iter() {
map.insert(
format!(
"{}{}",
mora.consonant.clone().unwrap_or("".to_string()),
mora.vowel
),
mora.mora.clone(),
);
}
map
});
pub static MORA_KATA_TO_MORA_PHONEMES: Lazy<HashMap<String, (Option<String>, String)>> =
Lazy::new(|| {
let mut map = HashMap::new();
@@ -52,12 +37,4 @@ pub static MORA_KATA_TO_MORA_PHONEMES: Lazy<HashMap<String, (Option<String>, Str
map
});
pub static CONSONANTS: Lazy<Vec<String>> = Lazy::new(|| {
let consonants = MORA_KATA_TO_MORA_PHONEMES
.values()
.filter_map(|(consonant, _)| consonant.clone())
.collect::<Vec<_>>();
consonants
});
pub const VOWELS: [&str; 6] = ["a", "i", "u", "e", "o", "N"];

0
crates/sbv2_core/src/mora_list.json → sbv2_core/src/mora_list.json
View File

0
crates/sbv2_core/src/nlp.rs → sbv2_core/src/nlp.rs
View File

0
crates/sbv2_core/src/norm.rs → sbv2_core/src/norm.rs
View File

0
crates/sbv2_core/src/sbv2file.rs → sbv2_core/src/sbv2file.rs
View File

14
crates/sbv2_core/src/style.rs → sbv2_core/src/style.rs
View File

@@ -1,4 +1,4 @@
use crate::error::{Error, Result};
use crate::error::Result;
use ndarray::{s, Array1, Array2};
use serde::Deserialize;
@@ -21,18 +21,6 @@ pub fn get_style_vector(
style_id: i32,
weight: f32,
) -> Result<Array1<f32>> {
if style_vectors.shape().len() != 2 {
return Err(Error::StyleError(
"Invalid shape for style vectors".to_string(),
));
}
if style_id < 0 || style_id >= style_vectors.shape()[0] as i32 {
return Err(Error::StyleError(format!(
"Invalid style ID: {}. Max ID: {}",
style_id,
style_vectors.shape()[0] - 1
)));
}
let mean = style_vectors.slice(s![0, ..]).to_owned();
let style_vector = style_vectors.slice(s![style_id as usize, ..]).to_owned();
let diff = (style_vector - &mean) * weight;

0
crates/sbv2_core/src/tokenizer.rs → sbv2_core/src/tokenizer.rs
View File

273
sbv2_core/src/tts.rs Normal file
View File

@@ -0,0 +1,273 @@
use crate::error::{Error, Result};
use crate::{jtalk, model, style, tokenizer, tts_util};
use ndarray::{concatenate, Array1, Array2, Array3, Axis};
use ort::Session;
use tokenizers::Tokenizer;
#[derive(PartialEq, Eq, Clone)]
pub struct TTSIdent(String);
impl std::fmt::Display for TTSIdent {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.write_str(&self.0)?;
Ok(())
}
}
impl<S> From<S> for TTSIdent
where
S: AsRef<str>,
{
fn from(value: S) -> Self {
TTSIdent(value.as_ref().to_string())
}
}
pub struct TTSModel {
vits2: Session,
style_vectors: Array2<f32>,
ident: TTSIdent,
}
/// High-level Style-Bert-VITS2's API
pub struct TTSModelHolder {
tokenizer: Tokenizer,
bert: Session,
models: Vec<TTSModel>,
jtalk: jtalk::JTalk,
}
impl TTSModelHolder {
/// Initialize a new TTSModelHolder
///
/// # Examples
///
/// ```rs
/// 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) -> Result<Self> {
let bert = model::load_model(bert_model_bytes, true)?;
let jtalk = jtalk::JTalk::new()?;
let tokenizer = tokenizer::get_tokenizer(tokenizer_bytes)?;
Ok(TTSModelHolder {
bert,
models: vec![],
jtalk,
tokenizer,
})
}
/// Return a list of model names
pub fn models(&self) -> Vec<String> {
self.models.iter().map(|m| m.ident.to_string()).collect()
}
/// Load a .sbv2 file binary
///
/// # Examples
///
/// ```rs
/// tts_holder.load_sbv2file("tsukuyomi", std::fs::read("tsukuyomi.sbv2")?)?;
/// ```
pub fn load_sbv2file<I: Into<TTSIdent>, P: AsRef<[u8]>>(
&mut self,
ident: I,
sbv2_bytes: P,
) -> Result<()> {
let (style_vectors, vits2) = crate::sbv2file::parse_sbv2file(sbv2_bytes)?;
self.load(ident, style_vectors, vits2)?;
Ok(())
}
/// Load a style vector and onnx model binary
///
/// # Examples
///
/// ```rs
/// tts_holder.load("tsukuyomi", std::fs::read("style_vectors.json")?, std::fs::read("model.onnx")?)?;
/// ```
pub fn load<I: Into<TTSIdent>, P: AsRef<[u8]>>(
&mut self,
ident: I,
style_vectors_bytes: P,
vits2_bytes: P,
) -> Result<()> {
let ident = ident.into();
if self.find_model(ident.clone()).is_err() {
self.models.push(TTSModel {
vits2: model::load_model(vits2_bytes, false)?,
style_vectors: style::load_style(style_vectors_bytes)?,
ident,
})
}
Ok(())
}
/// Unload a model
pub fn unload<I: Into<TTSIdent>>(&mut self, ident: I) -> bool {
let ident = ident.into();
if let Some((i, _)) = self
.models
.iter()
.enumerate()
.find(|(_, m)| m.ident == ident)
{
self.models.remove(i);
true
} else {
false
}
}
/// Parse text and return the input for synthesize
///
/// # Note
/// This function is for low-level usage, use `easy_synthesize` for high-level usage.
#[allow(clippy::type_complexity)]
pub fn parse_text(
&self,
text: &str,
) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> {
crate::tts_util::parse_text_blocking(
text,
&self.jtalk,
&self.tokenizer,
|token_ids, attention_masks| {
crate::bert::predict(&self.bert, token_ids, attention_masks)
},
)
}
fn find_model<I: Into<TTSIdent>>(&self, ident: I) -> Result<&TTSModel> {
let ident = ident.into();
self.models
.iter()
.find(|m| m.ident == ident)
.ok_or(Error::ModelNotFoundError(ident.to_string()))
}
/// Get style vector by style id and weight
///
/// # Note
/// This function is for low-level usage, use `easy_synthesize` for high-level usage.
pub fn get_style_vector<I: Into<TTSIdent>>(
&self,
ident: I,
style_id: i32,
weight: f32,
) -> Result<Array1<f32>> {
style::get_style_vector(&self.find_model(ident)?.style_vectors, style_id, weight)
}
/// Synthesize text to audio
///
/// # Examples
///
/// ```rs
/// let audio = tts_holder.easy_synthesize("tsukuyomi", "こんにちは", 0, SynthesizeOptions::default())?;
/// ```
pub fn easy_synthesize<I: Into<TTSIdent> + Copy>(
&self,
ident: I,
text: &str,
style_id: i32,
options: SynthesizeOptions,
) -> Result<Vec<u8>> {
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();
let mut audios = vec![];
for (i, t) in texts.iter().enumerate() {
if t.is_empty() {
continue;
}
let (bert_ori, phones, tones, lang_ids) = self.parse_text(t)?;
let audio = model::synthesize(
&self.find_model(ident)?.vits2,
bert_ori.to_owned(),
phones,
tones,
lang_ids,
style_vector.clone(),
options.sdp_ratio,
options.length_scale,
)?;
audios.push(audio.clone());
if i != texts.len() - 1 {
audios.push(Array3::zeros((1, 1, 22050)));
}
}
concatenate(
Axis(2),
&audios.iter().map(|x| x.view()).collect::<Vec<_>>(),
)?
} else {
let (bert_ori, phones, tones, lang_ids) = self.parse_text(text)?;
model::synthesize(
&self.find_model(ident)?.vits2,
bert_ori.to_owned(),
phones,
tones,
lang_ids,
style_vector,
options.sdp_ratio,
options.length_scale,
)?
};
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
///
/// # Fields
/// - `sdp_ratio`: SDP ratio
/// - `length_scale`: Length scale
/// - `style_weight`: Style weight
/// - `split_sentences`: Split sentences
pub struct SynthesizeOptions {
pub sdp_ratio: f32,
pub length_scale: f32,
pub style_weight: f32,
pub split_sentences: bool,
}
impl Default for SynthesizeOptions {
fn default() -> Self {
SynthesizeOptions {
sdp_ratio: 0.0,
length_scale: 1.0,
style_weight: 1.0,
split_sentences: true,
}
}
}

65
crates/sbv2_core/src/tts_util.rs → sbv2_core/src/tts_util.rs
View File

@@ -1,22 +1,10 @@
use std::io::Cursor;
use crate::error::Result;
use crate::jtalk::JTalkProcess;
use crate::mora::MORA_KATA_TO_MORA_PHONEMES;
use crate::norm::PUNCTUATIONS;
use crate::{jtalk, nlp, norm, tokenizer, utils};
use hound::{SampleFormat, WavSpec, WavWriter};
use ndarray::{concatenate, s, Array, Array1, Array2, Array3, Axis};
use tokenizers::Tokenizer;
pub fn preprocess_parse_text(text: &str, jtalk: &jtalk::JTalk) -> Result<(String, JTalkProcess)> {
let text = jtalk.num2word(text)?;
let normalized_text = norm::normalize_text(&text);
let process = jtalk.process_text(&normalized_text)?;
Ok((normalized_text, process))
}
/// Parse text and return the input for synthesize
///
/// # Note
@@ -33,9 +21,13 @@ pub async fn parse_text(
Box<dyn std::future::Future<Output = Result<ndarray::Array2<f32>>>>,
>,
) -> Result<(Array2<f32>, Array1<i64>, Array1<i64>, Array1<i64>)> {
let (normalized_text, process) = preprocess_parse_text(text, jtalk)?;
let text = jtalk.num2word(text)?;
let normalized_text = norm::normalize_text(&text);
let process = jtalk.process_text(&normalized_text)?;
let (phones, tones, mut word2ph) = process.g2p()?;
let (phones, tones, lang_ids) = nlp::cleaned_text_to_sequence(phones, tones);
let phones = utils::intersperse(&phones, 0);
let tones = utils::intersperse(&tones, 0);
let lang_ids = utils::intersperse(&lang_ids, 0);
@@ -100,7 +92,6 @@ pub async fn parse_text(
#[allow(clippy::type_complexity)]
pub fn parse_text_blocking(
text: &str,
given_tones: Option<Vec<i32>>,
jtalk: &jtalk::JTalk,
tokenizer: &Tokenizer,
bert_predict: impl FnOnce(Vec<i64>, Vec<i64>) -> Result<ndarray::Array2<f32>>,
@@ -109,10 +100,7 @@ pub fn parse_text_blocking(
let normalized_text = norm::normalize_text(&text);
let process = jtalk.process_text(&normalized_text)?;
let (phones, mut tones, mut word2ph) = process.g2p()?;
if let Some(given_tones) = given_tones {
tones = given_tones;
}
let (phones, tones, mut word2ph) = process.g2p()?;
let (phones, tones, lang_ids) = nlp::cleaned_text_to_sequence(phones, tones);
let phones = utils::intersperse(&phones, 0);
@@ -173,15 +161,8 @@ pub fn parse_text_blocking(
}
pub fn array_to_vec(audio_array: Array3<f32>) -> Result<Vec<u8>> {
// If SBV2_FORCE_STEREO is set ("1"/"true"), duplicate mono to stereo
let force_stereo = std::env::var("SBV2_FORCE_STEREO")
.ok()
.map(|v| matches!(v.as_str(), "1" | "true" | "TRUE" | "True"))
.unwrap_or(false);
let channels: u16 = if force_stereo { 2 } else { 1 };
let spec = WavSpec {
channels,
channels: 1,
sample_rate: 44100,
bits_per_sample: 32,
sample_format: SampleFormat::Float,
@@ -190,38 +171,10 @@ pub fn array_to_vec(audio_array: Array3<f32>) -> Result<Vec<u8>> {
let mut writer = WavWriter::new(&mut cursor, spec)?;
for i in 0..audio_array.shape()[0] {
let output = audio_array.slice(s![i, 0, ..]).to_vec();
if force_stereo {
for sample in output {
// Write to Left and Right channels
writer.write_sample(sample)?;
writer.write_sample(sample)?;
}
} else {
for sample in output {
writer.write_sample(sample)?;
}
for sample in output {
writer.write_sample(sample)?;
}
}
writer.finalize()?;
Ok(cursor.into_inner())
}
pub fn kata_tone2phone_tone(kata_tone: Vec<(String, i32)>) -> Vec<(String, i32)> {
let mut results = vec![("_".to_string(), 0)];
for (mora, tone) in kata_tone {
if PUNCTUATIONS.contains(&mora.as_str()) {
results.push((mora, 0));
continue;
} else {
let (consonant, vowel) = MORA_KATA_TO_MORA_PHONEMES.get(&mora).unwrap();
if let Some(consonant) = consonant {
results.push((consonant.to_string(), tone));
results.push((vowel.to_string(), tone));
} else {
results.push((vowel.to_string(), tone));
}
}
}
results.push(("_".to_string(), 0));
results
}

0
crates/sbv2_core/src/utils.rs → sbv2_core/src/utils.rs
View File

15
crates/sbv2_wasm/Cargo.toml → sbv2_wasm/Cargo.toml
View File

@@ -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"]
@@ -17,4 +12,8 @@ sbv2_core = { path = "../sbv2_core", default-features = false, features = ["no_s
once_cell.workspace = true
js-sys = "0.3.70"
ndarray.workspace = true
wasm-bindgen-futures = "0.4.55"
wasm-bindgen-futures = "0.4.43"
[profile.release]
lto = true
opt-level = "s"

2
sbv2_wasm/README.md Normal file
View File

@@ -0,0 +1,2 @@
# StyleBertVITS2 wasm
refer to https://github.com/tuna2134/sbv2-api

0
crates/sbv2_wasm/biome.json → sbv2_wasm/biome.json
View File

4
sbv2_wasm/build.sh Executable file
View File

@@ -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

51
sbv2_wasm/example.html Normal file
View File

@@ -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>

9
crates/sbv2_wasm/example.js → sbv2_wasm/example.js
View File

@@ -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");

15
crates/sbv2_wasm/package.json → sbv2_wasm/package.json
View File

@@ -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",
@@ -11,16 +11,15 @@
},
"keywords": [],
"author": "tuna2134",
"contributes": ["neodyland"],
"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"]
}

494
sbv2_wasm/pnpm-lock.yaml generated Normal file
View File

@@ -0,0 +1,494 @@
lockfileVersion: '9.0'
settings:
autoInstallPeers: true
excludeLinksFromLockfile: false
importers:
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'@biomejs/cli-linux-arm64': 1.9.3
'@biomejs/cli-linux-arm64-musl': 1.9.3
'@biomejs/cli-linux-x64': 1.9.3
'@biomejs/cli-linux-x64-musl': 1.9.3
'@biomejs/cli-win32-arm64': 1.9.3
'@biomejs/cli-win32-x64': 1.9.3
'@biomejs/cli-darwin-arm64@1.9.3':
optional: true
'@biomejs/cli-darwin-x64@1.9.3':
optional: true
'@biomejs/cli-linux-arm64-musl@1.9.3':
optional: true
'@biomejs/cli-linux-arm64@1.9.3':
optional: true
'@biomejs/cli-linux-x64-musl@1.9.3':
optional: true
'@biomejs/cli-linux-x64@1.9.3':
optional: true
'@biomejs/cli-win32-arm64@1.9.3':
optional: true
'@biomejs/cli-win32-x64@1.9.3':
optional: true
'@esbuild/aix-ppc64@0.24.0':
optional: true
'@esbuild/android-arm64@0.24.0':
optional: true
'@esbuild/android-arm@0.24.0':
optional: true
'@esbuild/android-x64@0.24.0':
optional: true
'@esbuild/darwin-arm64@0.24.0':
optional: true
'@esbuild/darwin-x64@0.24.0':
optional: true
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optional: true
'@esbuild/freebsd-x64@0.24.0':
optional: true
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optional: true
'@esbuild/linux-arm@0.24.0':
optional: true
'@esbuild/linux-ia32@0.24.0':
optional: true
'@esbuild/linux-loong64@0.24.0':
optional: true
'@esbuild/linux-mips64el@0.24.0':
optional: true
'@esbuild/linux-ppc64@0.24.0':
optional: true
'@esbuild/linux-riscv64@0.24.0':
optional: true
'@esbuild/linux-s390x@0.24.0':
optional: true
'@esbuild/linux-x64@0.24.0':
optional: true
'@esbuild/netbsd-x64@0.24.0':
optional: true
'@esbuild/openbsd-arm64@0.24.0':
optional: true
'@esbuild/openbsd-x64@0.24.0':
optional: true
'@esbuild/sunos-x64@0.24.0':
optional: true
'@esbuild/win32-arm64@0.24.0':
optional: true
'@esbuild/win32-ia32@0.24.0':
optional: true
'@esbuild/win32-x64@0.24.0':
optional: true
'@protobufjs/aspromise@1.1.2': {}
'@protobufjs/base64@1.1.2': {}
'@protobufjs/codegen@2.0.4': {}
'@protobufjs/eventemitter@1.1.0': {}
'@protobufjs/fetch@1.1.0':
dependencies:
'@protobufjs/aspromise': 1.1.2
'@protobufjs/inquire': 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':
dependencies:
undici-types: 6.19.8
esbuild@0.24.0:
optionalDependencies:
'@esbuild/aix-ppc64': 0.24.0
'@esbuild/android-arm': 0.24.0
'@esbuild/android-arm64': 0.24.0
'@esbuild/android-x64': 0.24.0
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'@esbuild/freebsd-arm64': 0.24.0
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'@esbuild/win32-ia32': 0.24.0
'@esbuild/win32-x64': 0.24.0
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:
'@protobufjs/aspromise': 1.1.2
'@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: {}

2
crates/sbv2_wasm/src-js/index.ts → sbv2_wasm/src-js/index.ts
View File

@@ -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)];

0
crates/sbv2_wasm/src/array_helper.rs → sbv2_wasm/src/array_helper.rs
View File

0
crates/sbv2_wasm/src/lib.rs → sbv2_wasm/src/lib.rs
View File

0
crates/sbv2_wasm/tsconfig.json → sbv2_wasm/tsconfig.json
View File

5
scripts/.gitignore vendored
View File

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

1
scripts/convert/.python-version
View File

@@ -1 +0,0 @@
3.11

6
scripts/convert/requirements.txt
View File

@@ -1,6 +0,0 @@
git+https://github.com/neodyland/style-bert-vits2-ref
onnxsim
numpy<2
zstandard
onnxruntime
cmake<4

17
scripts/docker/cpu.Dockerfile
View File

@@ -1,17 +0,0 @@
FROM rust AS builder
WORKDIR /work
COPY . .
RUN cargo build -r --bin sbv2_api
FROM ubuntu AS upx
WORKDIR /work
RUN apt update && apt-get install -y upx binutils
COPY --from=builder /work/target/release/sbv2_api /work/main
COPY --from=builder /work/target/release/*.so /work
RUN upx --best --lzma /work/main
RUN find /work -maxdepth 1 -name "*.so" -exec strip --strip-unneeded {} +
RUN find /work -maxdepth 1 -name "*.so" -exec upx --best --lzma {} +
FROM gcr.io/distroless/cc-debian12
WORKDIR /work
COPY --from=upx /work/main /work/main
COPY --from=upx /work/*.so /work
CMD ["/work/main"]

14
scripts/make_dict.sh
View File

@@ -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

180
scripts/sbv2-bindings-colab.ipynb
View File

@@ -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
}

8
scripts/sbv2-test-api.py
View File

@@ -1,8 +0,0 @@
import requests
res = requests.post(
"http://localhost:3000/synthesize",
json={"text": "おはようございます", "ident": "tsukuyomi"},
)
with open("output.wav", "wb") as f:
f.write(res.content)

23
test.py
View File

@@ -1,19 +1,8 @@
import requests
data = (requests.get("http://localhost:8080/audio_query", params={
"text": "こんにちは、今日はいい天気ですね。",
})).json()
print(data)
data = (requests.post("http://localhost:8080/synthesis", json={
"text": data["text"],
"ident": "tsukuyomi",
"speaker_id": 0,
"style_id": 0,
"sdp_ratio": 0.5,
"length_scale": 0.5,
"audio_query": data["audio_query"],
})).content
with open("test.wav", "wb") as f:
f.write(data)
res = requests.post(
"http://localhost:3000/synthesize",
json={"text": "おはようございます", "ident": "tsukuyomi"},
)
with open("output.wav", "wb") as f:
f.write(res.content)