Merge branch 'main' of https://github.com/tuna2134/sbv2-api into python

.dockerignore

@@ -0,0 +1,6 @@
+target/
+models/
+docker/
+.env*
+renovate.json
+*.py

.env.sample

@@ -0,0 +1,6 @@
+BERT_MODEL_PATH=models/deberta.onnx
+MODEL_PATH=models/model_tsukuyomi.onnx
+MODELS_PATH=models
+STYLE_VECTORS_PATH=models/style_vectors.json
+TOKENIZER_PATH=models/tokenizer.json
+ADDR=localhost:3000

.github/workflows/build.yml

@@ -0,0 +1,36 @@
+name: Push to github container register
+
+on:
+  release:
+    types: [created]
+  workflow_dispatch:
+
+jobs:
+  push-docker:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    strategy:
+      matrix:
+        tag: [cpu, cuda]
+    steps:
+      - 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: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: |
+            ghcr.io/${{ github.repository }}:${{ matrix.tag }}
+          file: docker/${{ matrix.tag }}.Dockerfile

.gitignore

@@ -1,5 +1,6 @@
 target
 models/*.onnx
 models/*.json
-venv
+venv/
-.env
+.env
+output.wav

Cargo.toml

@@ -1,6 +1,11 @@
 [workspace]
 resolver = "2"
+<<<<<<< HEAD
 members = [ "sbv2_api","sbv2_core", "sbv2_bindings"]
+=======
+members = ["sbv2_api", "sbv2_core"]
+>>>>>>> cfea5d735aeb7d0abad5b913a3dda3810d8e59f8
 
 [workspace.dependencies]
 anyhow = "1.0.86"
+dotenvy = "0.15.7"

LICENSE

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

README.md

@@ -1,25 +1,52 @@
 # sbv2-api
-このプロジェクトはStyle-Bert-ViTS2をONNX化したものをRustで実行するのを目的としています。つまり推論しか行いません。
+このプロジェクトはStyle-Bert-ViTS2をONNX化したものをRustで実行するのを目的としています。
 
-学習したいのであれば、Style-Bert-ViT2で調べてやってください。
+学習したい場合は、Style-Bert-ViTS2 学習方法 などで調べるとよいかもしれません。
 
-注意：JP-Extraしか対応していません。
+JP-Extraしか対応していません。(基本的に対応する予定もありません)
 
 ## ONNX化する方法
-dabertaとstbv2本体をonnx化する必要があります。
+```sh
-
+cd convert
-あくまで推奨ですが、onnxsimを使うことをお勧めします。
+# (何かしらの方法でvenv作成(推奨))
-onnxsim使うことでモデルのサイズを軽くすることができます。
+pip install -r requirements.txt
-
+python convert_deberta.py
-## onnxモデルの配置方法
+python convert_model.py --style_file ../../style-bert-vits2/model_assets/something/style_vectors.npy --config_file ../../style-bert-vits2/model_assets/something/config.json --model_file ../../style-bert-vits2/model_assets/something/something_eXXX_sXXXX.safetensors
-- `models/daberta.onnx` - DaBertaのonnxモデル
+```
-- `models/sbv2.onnx` - `Style-Bert-ViT2`の本体
 
 ## Todo
 - [x] WebAPIの実装
 - [x] Rustライブラリの実装
 - [ ] 余裕があればPyO3使ってPythonで利用可能にする
+- [x] GPU対応(優先的にCUDA)
+- [ ] WASM変換(ortがサポートやめたので、中止)
+
+## 構造説明
+- `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
+
+## APIの起動方法
+```sh
+cargo run -p sbv2_api -r
+```
+
+### CUDAでの起動
+```sh
+cargo run -p sbv2_api -r -F cuda,cuda_tf32
+```
+
+### Dynamic Linkサポート
+```sh
+ORT_DYLIB_PATH=./libonnxruntime.dll cargo run -p sbv2_api -r -F dynamic
+```
+
+### テストコマンド
+```sh
+curl -XPOST -H "Content-type: application/json" -d '{"text": "こんにちは","ident": "something"}' 'http://localhost:3000/synthesize'
+curl http://localhost:3000/models
+```
 
 ## 謝辞
 - [litagin02/Style-Bert-VITS2](https://github.com/litagin02/Style-Bert-VITS2) - このコードの書くにあたり、ベースとなる部分を参考にさせていただきました。
-- [Googlefan](https://github.com/Googlefan256) - 彼にモデルをONNXヘ変換および効率化をする方法を教わりました。
+- [Googlefan](https://github.com/Googlefan256) - 彼にモデルをONNXヘ変換および効率化をする方法を教わりました。

convert/convert_deberta.py

@@ -0,0 +1,50 @@
+from transformers.convert_slow_tokenizer import BertConverter
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.constants import Languages
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import torch
+from torch import nn
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+parser.add_argument("--model", default="ku-nlp/deberta-v2-large-japanese-char-wwm")
+args = parser.parse_args()
+model_name = args.model
+
+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")
+
+
+class ORTDeberta(nn.Module):
+    def __init__(self, model_name):
+        super(ORTDeberta, self).__init__()
+        self.model = AutoModelForMaskedLM.from_pretrained(model_name)
+
+    def forward(self, input_ids, token_type_ids, attention_mask):
+        inputs = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+        }
+        res = self.model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        return res
+
+
+model = ORTDeberta(model_name)
+inputs = AutoTokenizer.from_pretrained(model_name)(
+    "今日はいい天気ですね", return_tensors="pt"
+)
+
+torch.onnx.export(
+    model,
+    (inputs["input_ids"], inputs["token_type_ids"], inputs["attention_mask"]),
+    "../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"}},
+)

convert/convert_model.py

@@ -0,0 +1,143 @@
+import numpy as np
+import json
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.models.infer import get_net_g, get_text
+from style_bert_vits2.models.hyper_parameters import HyperParameters
+import torch
+from style_bert_vits2.constants import (
+    DEFAULT_ASSIST_TEXT_WEIGHT,
+    DEFAULT_STYLE,
+    DEFAULT_STYLE_WEIGHT,
+    Languages,
+)
+from style_bert_vits2.tts_model import TTSModel
+import numpy as np
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+parser.add_argument("--style_file", required=True)
+parser.add_argument("--config_file", required=True)
+parser.add_argument("--model_file", required=True)
+args = parser.parse_args()
+style_file = args.style_file
+config_file = args.config_file
+model_file = args.model_file
+
+bert_models.load_model(Languages.JP, "ku-nlp/deberta-v2-large-japanese-char-wwm")
+bert_models.load_tokenizer(Languages.JP, "ku-nlp/deberta-v2-large-japanese-char-wwm")
+
+array = np.load(style_file)
+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:
+    json.dump(
+        {
+            "data": data,
+            "shape": array.shape,
+        },
+        f,
+    )
+text = "今日はいい天気ですね。"
+
+bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
+    text,
+    Languages.JP,
+    hyper_parameters,
+    "cpu",
+    assist_text=None,
+    assist_text_weight=DEFAULT_ASSIST_TEXT_WEIGHT,
+    given_phone=None,
+    given_tone=None,
+)
+
+tts_model = TTSModel(
+    model_path=model_file,
+    config_path=config_file,
+    style_vec_path=style_file,
+    device="cpu",
+)
+device = "cpu"
+style_id = tts_model.style2id[DEFAULT_STYLE]
+
+
+def get_style_vector(style_id, weight):
+    style_vectors = np.load(style_file)
+    mean = style_vectors[0]
+    style_vec = style_vectors[style_id]
+    style_vec = mean + (style_vec - mean) * weight
+    return style_vec
+
+
+style_vector = get_style_vector(style_id, DEFAULT_STYLE_WEIGHT)
+
+x_tst = phones.to(device).unsqueeze(0)
+tones = tones.to(device).unsqueeze(0)
+lang_ids = lang_ids.to(device).unsqueeze(0)
+bert = bert.to(device).unsqueeze(0)
+ja_bert = ja_bert.to(device).unsqueeze(0)
+en_bert = en_bert.to(device).unsqueeze(0)
+x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+style_vec_tensor = torch.from_numpy(style_vector).to(device).unsqueeze(0)
+
+model = get_net_g(
+    model_file,
+    hyper_parameters.version,
+    device,
+    hyper_parameters,
+)
+
+
+def forward(x, x_len, sid, tone, lang, bert, style, length_scale, sdp_ratio):
+    return model.infer(
+        x,
+        x_len,
+        sid,
+        tone,
+        lang,
+        bert,
+        style,
+        sdp_ratio=sdp_ratio,
+        length_scale=length_scale,
+    )
+
+
+model.forward = forward
+
+torch.onnx.export(
+    model,
+    (
+        x_tst,
+        x_tst_lengths,
+        torch.LongTensor([0]).to(device),
+        tones,
+        lang_ids,
+        bert,
+        style_vec_tensor,
+        torch.tensor(1.0),
+        torch.tensor(0.0),
+    ),
+    f"../models/model_{out_name}.onnx",
+    verbose=True,
+    dynamic_axes={
+        "x_tst": {1: "batch_size"},
+        "x_tst_lengths": {0: "batch_size"},
+        "tones": {1: "batch_size"},
+        "language": {1: "batch_size"},
+        "bert": {2: "batch_size"},
+    },
+    input_names=[
+        "x_tst",
+        "x_tst_lengths",
+        "sid",
+        "tones",
+        "language",
+        "bert",
+        "style_vec",
+        "length_scale",
+        "sdp_ratio",
+    ],
+    output_names=["output"],
+)

convert/requirements.txt

@@ -0,0 +1,3 @@
+style-bert-vits2
+onnxsim
+numpy<3

docker/cpu.Dockerfile

@@ -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"]

docker/cuda.Dockerfile

@@ -0,0 +1,10 @@
+FROM rust AS builder
+WORKDIR /work
+COPY . .
+RUN cargo build -r --bin sbv2_api -F cuda,cuda_tf32
+
+FROM nvidia/cuda:12.6.1-cudnn-runtime-ubuntu24.04
+WORKDIR /work
+COPY --from=builder /work/target/release/sbv2_api /work/main
+COPY --from=builder /work/target/release/*.so /work
+CMD ["/work/main"]

docker/run.sh

@@ -0,0 +1 @@
+docker run -it --rm -p 3000:3000 --name sbv2 -v ./models:/work/models --env-file .env sbv2

sbv2_api/Cargo.toml

@@ -6,7 +6,14 @@ edition = "2021"
 [dependencies]
 anyhow.workspace = true
 axum = "0.7.5"
-dotenvy = "0.15.7"
+dotenvy.workspace = true
+env_logger = "0.11.5"
+log = "0.4.22"
 sbv2_core = { version = "0.1.0", path = "../sbv2_core" }
 serde = { version = "1.0.210", features = ["derive"] }
 tokio = { version = "1.40.0", features = ["full"] }
+
+[features]
+cuda = ["sbv2_core/cuda"]
+cuda_tf32 = ["sbv2_core/cuda_tf32"]
+dynamic = ["sbv2_core/dynamic"]

sbv2_api/src/main.rs

@@ -5,58 +5,130 @@ use axum::{
     routing::{get, post},
     Json, Router,
 };
-use sbv2_core::tts::TTSModel;
+use sbv2_core::tts::TTSModelHolder;
 use serde::Deserialize;
 use std::env;
 use std::sync::Arc;
+use tokio::fs;
 use tokio::sync::Mutex;
 
 mod error;
 use crate::error::AppResult;
 
+async fn models(State(state): State<AppState>) -> AppResult<impl IntoResponse> {
+    Ok(Json(state.tts_model.lock().await.models()))
+}
+
+fn sdp_default() -> f32 {
+    0.0
+}
+
+fn length_default() -> f32 {
+    1.0
+}
 #[derive(Deserialize)]
 struct SynthesizeRequest {
     text: String,
+    ident: String,
+    #[serde(default = "sdp_default")]
+    sdp_ratio: f32,
+    #[serde(default = "length_default")]
+    length_scale: f32,
 }
 
 async fn synthesize(
-    State(state): State<Arc<AppState>>,
+    State(state): State<AppState>,
-    Json(SynthesizeRequest { text }): Json<SynthesizeRequest>,
+    Json(SynthesizeRequest {
+        text,
+        ident,
+        sdp_ratio,
+        length_scale,
+    }): 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;
-        let tts_model = if let Some(tts_model) = &*tts_model {
-            tts_model
-        } else {
-            *tts_model = Some(TTSModel::new(
-                &env::var("BERT_MODEL_PATH")?,
-                &env::var("MAIN_MODEL_PATH")?,
-                &env::var("STYLE_VECTORS_PATH")?,
-            )?);
-            tts_model.as_ref().unwrap()
-        };
         let (bert_ori, phones, tones, lang_ids) = tts_model.parse_text(&text)?;
-        let style_vector = tts_model.get_style_vector(0, 1.0)?;
+        let style_vector = tts_model.get_style_vector(&ident, 0, 1.0)?;
-        tts_model.synthesize(bert_ori.to_owned(), phones, tones, lang_ids, style_vector)?
+        tts_model.synthesize(
+            ident,
+            bert_ori.to_owned(),
+            phones,
+            tones,
+            lang_ids,
+            style_vector,
+            sdp_ratio,
+            length_scale,
+        )?
     };
     Ok(([(CONTENT_TYPE, "audio/wav")], buffer))
 }
 
+#[derive(Clone)]
 struct AppState {
-    tts_model: Arc<Mutex<Option<TTSModel>>>,
+    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?,
+        )?;
+        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>(),
+                );
+            }
+        }
+        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}");
+            };
+        }
+        Ok(Self {
+            tts_model: Arc::new(Mutex::new(tts_model)),
+        })
+    }
 }
 
 #[tokio::main]
 async fn main() -> anyhow::Result<()> {
     dotenvy::dotenv().ok();
+    env_logger::init();
     let app = Router::new()
         .route("/", get(|| async { "Hello, World!" }))
         .route("/synthesize", post(synthesize))
-        .with_state(Arc::new(AppState {
+        .route("/models", get(models))
-            tts_model: Arc::new(Mutex::new(None)),
+        .with_state(AppState::new().await?);
-        }));
+    let addr = env::var("ADDR").unwrap_or("0.0.0.0:3000".to_string());
-
+    let listener = tokio::net::TcpListener::bind(&addr).await?;
-    let listener = tokio::net::TcpListener::bind("0.0.0.0:3000").await?;
+    log::info!("Listening on {addr}");
     axum::serve(listener, app).await?;
 
     Ok(())

sbv2_core/Cargo.toml

@@ -5,14 +5,20 @@ edition = "2021"
 
 [dependencies]
 anyhow.workspace = true
+dotenvy.workspace = true
 hound = "3.5.1"
 jpreprocess = { version = "0.10.0", features = ["naist-jdic"] }
 ndarray = "0.16.1"
 num_cpus = "1.16.0"
 once_cell = "1.19.0"
-ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.5" }
+ort = { git = "https://github.com/pykeio/ort.git", version = "2.0.0-rc.6" }
 regex = "1.10.6"
 serde = { version = "1.0.210", features = ["derive"] }
 serde_json = "1.0.128"
 thiserror = "1.0.63"
 tokenizers = "0.20.0"
+
+[features]
+cuda = ["ort/cuda"]
+cuda_tf32 = []
+dynamic = ["ort/load-dynamic"]

sbv2_core/mora_convert.py

@@ -193,4 +193,4 @@ for mora, consonant, vowel in __MORA_LIST_ADDITIONAL:
 
 
 with open("src/mora_list.json", "w") as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)
+    json.dump(data, f, ensure_ascii=False, indent=4)

sbv2_core/src/error.rs

@@ -18,6 +18,8 @@ pub enum Error {
     IoError(#[from] std::io::Error),
     #[error("hound error: {0}")]
     HoundError(#[from] hound::Error),
+    #[error("model not found error")]
+    ModelNotFoundError(String),
 }
 
 pub type Result<T> = std::result::Result<T, Error>;

sbv2_core/src/jtalk.rs

@@ -4,13 +4,13 @@ use crate::norm::{replace_punctuation, PUNCTUATIONS};
 use jpreprocess::*;
 use once_cell::sync::Lazy;
 use regex::Regex;
+use std::cmp::Reverse;
 use std::collections::HashSet;
 use std::sync::Arc;
-use tokenizers::Tokenizer;
 
 type JPreprocessType = JPreprocess<DefaultFetcher>;
 
-fn get_jtalk() -> Result<JPreprocessType> {
+fn initialize_jtalk() -> Result<JPreprocessType> {
     let config = JPreprocessConfig {
         dictionary: SystemDictionaryConfig::Bundled(kind::JPreprocessDictionaryKind::NaistJdic),
         user_dictionary: None,
@@ -50,7 +50,7 @@ pub struct JTalk {
 
 impl JTalk {
     pub fn new() -> Result<Self> {
-        let jpreprocess = Arc::new(get_jtalk()?);
+        let jpreprocess = Arc::new(initialize_jtalk()?);
         Ok(Self { jpreprocess })
     }
 
@@ -64,7 +64,7 @@ impl JTalk {
 static KATAKANA_PATTERN: Lazy<Regex> = Lazy::new(|| Regex::new(r"[\u30A0-\u30FF]+").unwrap());
 static MORA_PATTERN: Lazy<Vec<String>> = Lazy::new(|| {
     let mut sorted_keys: Vec<String> = MORA_KATA_TO_MORA_PHONEMES.keys().cloned().collect();
-    sorted_keys.sort_by(|a, b| b.len().cmp(&a.len()));
+    sorted_keys.sort_by_key(|b| Reverse(b.len()));
     sorted_keys
 });
 static LONG_PATTERN: Lazy<Regex> = Lazy::new(|| Regex::new(r"(\w)(ー*)").unwrap());
@@ -128,8 +128,8 @@ impl JTalkProcess {
             JTalkProcess::align_tones(phone_w_punct, phone_tone_list_wo_punct)?;
 
         let mut sep_tokenized: Vec<Vec<String>> = Vec::new();
-        for i in 0..seq_text.len() {
+        for seq_text_item in &seq_text {
-            let text = seq_text[i].clone();
+            let text = seq_text_item.clone();
             if !PUNCTUATIONS.contains(&text.as_str()) {
                 sep_tokenized.push(text.chars().map(|x| x.to_string()).collect());
             } else {
@@ -390,22 +390,3 @@ impl JTalkProcess {
         Ok(phones)
     }
 }
-
-pub fn get_tokenizer() -> Result<Tokenizer> {
-    let tokenizer = Tokenizer::from_file("tokenizer.json")?;
-    Ok(tokenizer)
-}
-
-pub fn tokenize(text: &str, tokenizer: &Tokenizer) -> Result<(Vec<i64>, Vec<i64>)> {
-    let mut token_ids = vec![1];
-    let mut attention_masks = vec![1];
-    for content in text.chars() {
-        let token = tokenizer.encode(content.to_string(), false)?;
-        let ids = token.get_ids();
-        token_ids.extend(ids.iter().map(|&x| x as i64));
-        attention_masks.extend(token.get_attention_mask().iter().map(|&x| x as i64));
-    }
-    token_ids.push(2);
-    attention_masks.push(1);
-    Ok((token_ids, attention_masks))
-}

sbv2_core/src/lib.rs

@@ -6,20 +6,6 @@ pub mod mora;
 pub mod nlp;
 pub mod norm;
 pub mod style;
+pub mod tokenizer;
 pub mod tts;
 pub mod utils;
-
-pub fn add(left: usize, right: usize) -> usize {
-    left + right
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn it_works() {
-        let result = add(2, 2);
-        assert_eq!(result, 4);
-    }
-}

sbv2_core/src/main.rs

@@ -1,20 +1,49 @@
-use sbv2_core::{error, tts};
+use std::{fs, time::Instant};
 
-fn main() -> error::Result<()> {
+use sbv2_core::tts;
+use std::env;
+
+fn main() -> anyhow::Result<()> {
+    dotenvy::dotenv().ok();
     let text = "眠たい";
-
+    let ident = "aaa";
-    let tts_model = tts::TTSModel::new(
+    let mut tts_holder = tts::TTSModelHolder::new(
-        "models/debert.onnx",
+        &fs::read(env::var("BERT_MODEL_PATH")?)?,
-        "models/model_opt.onnx",
+        &fs::read(env::var("TOKENIZER_PATH")?)?,
-        "models/style_vectors.json",
+    )?;
+    tts_holder.load(
+        ident,
+        fs::read(env::var("STYLE_VECTORS_PATH")?)?,
+        fs::read(env::var("MODEL_PATH")?)?,
     )?;
 
-    let (bert_ori, phones, tones, lang_ids) = tts_model.parse_text(text)?;
+    let (bert_ori, phones, tones, lang_ids) = tts_holder.parse_text(text)?;
-
-    let style_vector = tts_model.get_style_vector(0, 1.0)?;
-    let data = tts_model.synthesize(bert_ori.to_owned(), phones, tones, lang_ids, style_vector)?;
 
+    let style_vector = tts_holder.get_style_vector(ident, 0, 1.0)?;
+    let data = tts_holder.synthesize(
+        ident,
+        bert_ori.to_owned(),
+        phones.clone(),
+        tones.clone(),
+        lang_ids.clone(),
+        style_vector.clone(),
+        0.0,
+        0.5,
+    )?;
     std::fs::write("output.wav", data)?;
-
+    let now = Instant::now();
+    for _ in 0..10 {
+        tts_holder.synthesize(
+            ident,
+            bert_ori.to_owned(),
+            phones.clone(),
+            tones.clone(),
+            lang_ids.clone(),
+            style_vector.clone(),
+            0.0,
+            1.0,
+        )?;
+    }
+    println!("Time taken: {}", now.elapsed().as_millis());
     Ok(())
 }

sbv2_core/src/model.rs

@@ -4,17 +4,29 @@ use ndarray::{array, s, Array1, Array2, Axis};
 use ort::{GraphOptimizationLevel, Session};
 use std::io::Cursor;
 
-pub fn load_model(model_file: &str) -> Result<Session> {
+#[allow(clippy::vec_init_then_push)]
-    let session = Session::builder()?
+pub fn load_model<P: AsRef<[u8]>>(model_file: P) -> Result<Session> {
+    let mut exp = Vec::new();
+    #[cfg(feature = "cuda")]
+    {
+        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());
+    }
+    exp.push(ort::CPUExecutionProvider::default().build());
+    Ok(Session::builder()?
+        .with_execution_providers(exp)?
         .with_optimization_level(GraphOptimizationLevel::Level3)?
-        .with_intra_threads(1)?
         .with_intra_threads(num_cpus::get_physical())?
         .with_parallel_execution(true)?
         .with_inter_threads(num_cpus::get_physical())?
-        .commit_from_file(model_file)?;
+        .commit_from_memory(model_file.as_ref())?)
-    Ok(session)
 }
-
+#[allow(clippy::too_many_arguments)]
 pub fn synthesize(
     session: &Session,
     bert_ori: Array2<f32>,
@@ -22,6 +34,8 @@ pub fn synthesize(
     tones: Array1<i64>,
     lang_ids: Array1<i64>,
     style_vector: Array1<f32>,
+    sdp_ratio: f32,
+    length_scale: f32,
 ) -> Result<Vec<u8>> {
     let bert = bert_ori.insert_axis(Axis(0));
     let x_tst_lengths: Array1<i64> = array![x_tst.shape()[0] as i64];
@@ -36,7 +50,9 @@ pub fn synthesize(
         "tones" => tones,
         "language" => lang_ids,
         "bert" => bert,
-        "ja_bert" => style_vector,
+        "style_vec" => style_vector,
+        "sdp_ratio" => array![sdp_ratio],
+        "length_scale" => array![length_scale],
     }?)?;
 
     let audio_array = outputs

sbv2_core/src/style.rs

@@ -8,8 +8,8 @@ pub struct Data {
     pub data: Vec<Vec<f32>>,
 }
 
-pub fn load_style(path: &str) -> Result<Array2<f32>> {
+pub fn load_style<P: AsRef<[u8]>>(path: P) -> Result<Array2<f32>> {
-    let data: Data = serde_json::from_str(&std::fs::read_to_string(path)?)?;
+    let data: Data = serde_json::from_slice(path.as_ref())?;
     Ok(Array2::from_shape_vec(
         data.shape,
         data.data.iter().flatten().copied().collect(),
@@ -17,7 +17,7 @@ pub fn load_style(path: &str) -> Result<Array2<f32>> {
 }
 
 pub fn get_style_vector(
-    style_vectors: Array2<f32>,
+    style_vectors: &Array2<f32>,
     style_id: i32,
     weight: f32,
 ) -> Result<Array1<f32>> {

sbv2_core/src/tokenizer.rs

@@ -0,0 +1,21 @@
+use crate::error::Result;
+use tokenizers::Tokenizer;
+
+pub fn get_tokenizer<P: AsRef<[u8]>>(p: P) -> Result<Tokenizer> {
+    let tokenizer = Tokenizer::from_bytes(p)?;
+    Ok(tokenizer)
+}
+
+pub fn tokenize(text: &str, tokenizer: &Tokenizer) -> Result<(Vec<i64>, Vec<i64>)> {
+    let mut token_ids = vec![1];
+    let mut attention_masks = vec![1];
+    for content in text.chars() {
+        let token = tokenizer.encode(content.to_string(), false)?;
+        let ids = token.get_ids();
+        token_ids.extend(ids.iter().map(|&x| x as i64));
+        attention_masks.extend(token.get_attention_mask().iter().map(|&x| x as i64));
+    }
+    token_ids.push(2);
+    attention_masks.push(1);
+    Ok((token_ids, attention_masks))
+}

sbv2_core/src/tts.rs

@@ -1,33 +1,91 @@
-use crate::error::Result;
+use crate::error::{Error, Result};
-use crate::{bert, jtalk, model, nlp, norm, style, utils};
+use crate::{bert, jtalk, model, nlp, norm, style, tokenizer, utils};
 use ndarray::{concatenate, s, Array, Array1, Array2, 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 {
-    bert: Session,
     vits2: Session,
     style_vectors: Array2<f32>,
+    ident: TTSIdent,
+}
+
+pub struct TTSModelHolder {
+    tokenizer: Tokenizer,
+    bert: Session,
+    models: Vec<TTSModel>,
     jtalk: jtalk::JTalk,
 }
 
-impl TTSModel {
+impl TTSModelHolder {
-    pub fn new(
+    pub fn new<P: AsRef<[u8]>>(bert_model_bytes: P, tokenizer_bytes: P) -> Result<Self> {
-        bert_model_path: &str,
+        let bert = model::load_model(bert_model_bytes)?;
-        main_model_path: &str,
-        style_vector_path: &str,
-    ) -> Result<Self> {
-        let bert = model::load_model(bert_model_path)?;
-        let vits2 = model::load_model(main_model_path)?;
-        let style_vectors = style::load_style(style_vector_path)?;
         let jtalk = jtalk::JTalk::new()?;
-        Ok(TTSModel {
+        let tokenizer = tokenizer::get_tokenizer(tokenizer_bytes)?;
+        Ok(TTSModelHolder {
             bert,
-            vits2,
+            models: vec![],
-            style_vectors,
             jtalk,
+            tokenizer,
         })
     }
 
+    pub fn models(&self) -> Vec<String> {
+        self.models.iter().map(|m| m.ident.to_string()).collect()
+    }
+
+    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)?,
+                style_vectors: style::load_style(style_vectors_bytes)?,
+                ident,
+            })
+        }
+        Ok(())
+    }
+
+    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
+        }
+    }
+
+    #[allow(clippy::type_complexity)]
     pub fn parse_text(
         &self,
         text: &str,
@@ -40,13 +98,11 @@ impl TTSModel {
         let phones = utils::intersperse(&phones, 0);
         let tones = utils::intersperse(&tones, 0);
         let lang_ids = utils::intersperse(&lang_ids, 0);
-        for i in 0..word2ph.len() {
+        for item in &mut word2ph {
-            word2ph[i] *= 2;
+            *item *= 2;
         }
         word2ph[0] += 1;
-
+        let (token_ids, attention_masks) = tokenizer::tokenize(&normalized_text, &self.tokenizer)?;
-        let tokenizer = jtalk::get_tokenizer()?;
-        let (token_ids, attention_masks) = jtalk::tokenize(&normalized_text, &tokenizer)?;
 
         let bert_content = bert::predict(&self.bert, token_ids, attention_masks)?;
 
@@ -58,9 +114,9 @@ impl TTSModel {
         );
 
         let mut phone_level_feature = vec![];
-        for i in 0..word2ph.len() {
+        for (i, reps) in word2ph.iter().enumerate() {
             let repeat_feature = {
-                let (reps_rows, reps_cols) = (word2ph[i], 1);
+                let (reps_rows, reps_cols) = (*reps, 1);
                 let arr_len = bert_content.slice(s![i, ..]).len();
 
                 let mut results: Array2<f32> =
@@ -92,25 +148,44 @@ impl TTSModel {
         ))
     }
 
-    pub fn get_style_vector(&self, style_id: i32, weight: f32) -> Result<Array1<f32>> {
+    fn find_model<I: Into<TTSIdent>>(&self, ident: I) -> Result<&TTSModel> {
-        style::get_style_vector(self.style_vectors.clone(), style_id, weight)
+        let ident = ident.into();
+        self.models
+            .iter()
+            .find(|m| m.ident == ident)
+            .ok_or(Error::ModelNotFoundError(ident.to_string()))
     }
 
-    pub fn synthesize(
+    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)
+    }
+
+    #[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 buffer = model::synthesize(
-            &self.vits2,
+            &self.find_model(ident)?.vits2,
             bert_ori.to_owned(),
             phones,
             tones,
             lang_ids,
             style_vector,
+            sdp_ratio,
+            length_scale,
         )?;
         Ok(buffer)
     }

test.py

@@ -1,8 +1,8 @@
 import requests
 
-
+res = requests.post(
-res = requests.post('http://localhost:3000/synthesize', json={
+    "http://localhost:3001/synthesize",
-    "text": "初めて神戸に移り住んだ時に地元の人に教わった「阪急はオシャレして乗らなあかん。阪神はスリッパで乗っていい。JRは早い。」、好きすぎていまだに東京の人に説明するとき使ってる。"
+    json={"text": "おはようございます", "ident": "tsukuyomi"},
-})
+)
-with open('output.wav', 'wb') as f:
+with open("output.wav", "wb") as f:
-    f.write(res.content)
+    f.write(res.content)