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LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+
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+                     END OF TERMS AND CONDITIONS
+
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+
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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
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+to attach them to the start of each source file to most effectively
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+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -0,0 +1,59 @@
+---
+license: agpl-3.0
+tags:
+- tts
+- text-to-speech
+- gpt-sovits
+- voice-clone
+- japanese
+language:
+- ja
+---
+
+# ATRI Voice Model — GPT-SoVITS v2Pro
+
+> **WARNING: This model is for personal and research use only. Do not use it for commercial purposes or to impersonate real individuals.**
+
+---
+
+### Overview
+
+A fine-tuned [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) v2Pro voice model for ATRI (from *ATRI -My Dear Moments-*), capable of synthesizing speech in Japanese, Chinese, and English.
+
+### Files
+
+- `ATR_e8_s3952.pth` — Fine-tuned SoVITS model weights (8 epochs, 3952 steps)
+- `ref_audio.wav` — Reference audio for inference
+- `api_atri.py` — FastAPI-based TTS inference server
+
+### Usage
+
+1. Clone and set up [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) following its instructions.
+2. Download the GPT pretrained model `s1v3.ckpt` from GPT-SoVITS (included in its pretrained models).
+3. Place `ATR_e8_s3952.pth` and `ref_audio.wav` in your preferred location.
+4. Update the paths in `api_atri.py` (replace `/path/to/` placeholders with actual paths).
+5. Run the API server:
+
+```bash
+cd /path/to/GPT-SoVITS
+python api_atri.py
+```
+
+API docs will be available at `http://127.0.0.1:9880/docs`.
+
+### API Endpoints
+
+| Endpoint | Method | Description |
+|---|---|---|
+| `/health` | GET | Health check |
+| `/tts` | POST | Text-to-speech (returns full audio) |
+| `/tts/stream` | POST | Streaming text-to-speech |
+
+### Reference Audio
+
+- **Text**: わたしはマスターの所有物ですので。 勝手に売買するのは違法です
+- **Language**: Japanese
+
+### License
+
+This project is licensed under [AGPL-3.0](LICENSE), consistent with [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS).

api_atri.py

@@ -0,0 +1,242 @@
+"""
+ATR TTS API Server
+
+启动: python api_atri.py [-a 127.0.0.1] [-p 9880]
+
+接口文档: http://127.0.0.1:9880/docs
+"""
+
+import os
+import sys
+import signal
+import argparse
+import subprocess
+import threading
+import wave
+from io import BytesIO
+from typing import Generator, Optional, Union
+
+import numpy as np
+import soundfile as sf
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+from pydantic import BaseModel, Field
+import uvicorn
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+sys.path.append(os.path.join(now_dir, "GPT_SoVITS"))
+
+from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
+
+# ── Config ──────────────────────────────────────────────
+GPT_MODEL = "/path/to/GPT_SoVITS/pretrained_models/s1v3.ckpt"
+SOVITS_MODEL = "/path/to/ATR_e8_s3952.pth"
+REF_AUDIO = "/path/to/ref_audio.wav"
+REF_TEXT = "わたしはマスターの所有物ですので。 勝手に売買するのは違法です"
+REF_LANG = "ja"
+VERSION = "v2Pro"
+
+
+# ── Request / Response Models ───────────────────────────
+class TTSRequest(BaseModel):
+    text: str = Field(..., description="要合成的文本", examples=["こんにちは、お元気ですか？"])
+    text_lang: str = Field(..., description="文本语言: zh, en, ja, ko, yue", examples=["ja"])
+    ref_audio_path: Optional[str] = Field(None, description="参考音频路径 (留空使用默认)")
+    prompt_text: Optional[str] = Field(None, description="参考音频的文本 (留空使用默认)")
+    prompt_lang: Optional[str] = Field(None, description="参考音频的语言 (留空使用默认)")
+    speed_factor: float = Field(1.0, ge=0.5, le=2.0, description="语速倍率")
+    top_k: int = Field(15, ge=1, description="Top-K 采样")
+    top_p: float = Field(1.0, ge=0.0, le=1.0, description="Top-P 采样")
+    temperature: float = Field(1.0, ge=0.0, le=2.0, description="采样温度")
+    seed: int = Field(-1, description="随机种子 (-1 为随机)")
+    media_type: str = Field("wav", description="输出格式: wav, ogg, aac, raw")
+    text_split_method: str = Field("cut5", description="文本切分方式: cut0-cut5")
+    batch_size: int = Field(1, ge=1, description="推理批大小")
+    sample_steps: int = Field(32, ge=1, description="采样步数")
+
+    model_config = {"json_schema_extra": {
+        "examples": [
+            {"text": "こんにちは、お元気ですか？", "text_lang": "ja"},
+            {"text": "你好，很高兴认识你。", "text_lang": "zh"},
+        ]
+    }}
+
+
+class TTSStreamRequest(TTSRequest):
+    streaming_mode: int = Field(1, ge=1, le=3,
+        description="流式模式: 1=分段(最高质量), 2=真流式(中等质量), 3=定长流式(最快响应)")
+
+
+class HealthResponse(BaseModel):
+    status: str
+    gpt_model: str
+    sovits_model: str
+    ref_audio: str
+    version: str
+
+
+# ── Audio packing helpers ───────────────────────────────
+def pack_wav(data: np.ndarray, rate: int) -> bytes:
+    buf = BytesIO()
+    sf.write(buf, data, rate, format="wav")
+    buf.seek(0)
+    return buf.read()
+
+
+def pack_ogg(data: np.ndarray, rate: int) -> bytes:
+    buf = BytesIO()
+    def _write():
+        with sf.SoundFile(buf, mode="w", samplerate=rate, channels=1, format="ogg") as f:
+            f.write(data)
+    t = threading.Thread(target=_write)
+    threading.stack_size(4096 * 4096)
+    t.start()
+    t.join()
+    buf.seek(0)
+    return buf.read()
+
+
+def pack_aac(data: np.ndarray, rate: int) -> bytes:
+    proc = subprocess.Popen(
+        ["ffmpeg", "-f", "s16le", "-ar", str(rate), "-ac", "1",
+         "-i", "pipe:0", "-c:a", "aac", "-b:a", "192k", "-vn", "-f", "adts", "pipe:1"],
+        stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+    )
+    out, _ = proc.communicate(input=data.tobytes())
+    return out
+
+
+def pack_audio(data: np.ndarray, rate: int, media_type: str) -> bytes:
+    if media_type == "ogg":
+        return pack_ogg(data, rate)
+    elif media_type == "aac":
+        return pack_aac(data, rate)
+    elif media_type == "wav":
+        return pack_wav(data, rate)
+    return data.tobytes()
+
+
+def wave_header_chunk(sample_rate: int = 32000) -> bytes:
+    buf = BytesIO()
+    with wave.open(buf, "wb") as w:
+        w.setnchannels(1)
+        w.setsampwidth(2)
+        w.setframerate(sample_rate)
+        w.writeframes(b"")
+    buf.seek(0)
+    return buf.read()
+
+
+# ── App ─────────────────────────────────────────────────
+app = FastAPI(
+    title="ATR TTS API",
+    description="ATR 角色语音合成接口。基于 GPT-SoVITS v2Pro 模型。",
+    version="1.0.0",
+)
+tts_pipeline: TTS = None
+
+
+@app.on_event("startup")
+def startup():
+    global tts_pipeline
+    print("Loading models...")
+    config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
+    config.device = "cpu"
+    config.is_half = False
+    config.version = VERSION
+    config.t2s_weights_path = GPT_MODEL
+    config.vits_weights_path = SOVITS_MODEL
+    tts_pipeline = TTS(config)
+    print(f"Models loaded. Version: {VERSION}")
+    print(f"  GPT:    {GPT_MODEL}")
+    print(f"  SoVITS: {SOVITS_MODEL}")
+    print(f"  Ref:    {REF_AUDIO}")
+
+
+def _build_req(req: TTSRequest, streaming_mode: int = 0) -> dict:
+    return {
+        "text": req.text,
+        "text_lang": req.text_lang.lower(),
+        "ref_audio_path": req.ref_audio_path or REF_AUDIO,
+        "prompt_text": req.prompt_text if req.prompt_text is not None else REF_TEXT,
+        "prompt_lang": (req.prompt_lang or REF_LANG).lower(),
+        "top_k": req.top_k,
+        "top_p": req.top_p,
+        "temperature": req.temperature,
+        "text_split_method": req.text_split_method,
+        "batch_size": req.batch_size,
+        "batch_threshold": 0.75,
+        "split_bucket": True,
+        "speed_factor": req.speed_factor,
+        "fragment_interval": 0.3,
+        "seed": req.seed,
+        "media_type": req.media_type,
+        "streaming_mode": streaming_mode in (2, 3),
+        "return_fragment": streaming_mode == 1,
+        "fixed_length_chunk": streaming_mode == 3,
+        "parallel_infer": True,
+        "repetition_penalty": 1.35,
+        "sample_steps": req.sample_steps,
+        "super_sampling": False,
+    }
+
+
+# ── Endpoints ───────────────────────────────────────────
+@app.get("/health", response_model=HealthResponse, summary="健康检查")
+async def health():
+    return HealthResponse(
+        status="ok",
+        gpt_model=GPT_MODEL,
+        sovits_model=SOVITS_MODEL,
+        ref_audio=REF_AUDIO,
+        version=VERSION,
+    )
+
+
+@app.post("/tts", summary="语音合成", description="输入文本，返回完整的音频文件。",
+          responses={200: {"content": {"audio/wav": {}}}, 400: {"description": "合成失败"}})
+async def tts_endpoint(request: TTSRequest):
+    req = _build_req(request, streaming_mode=0)
+    try:
+        gen = tts_pipeline.run(req)
+        sr, audio = next(gen)
+        audio_bytes = pack_audio(audio, sr, request.media_type)
+        return Response(audio_bytes, media_type=f"audio/{request.media_type}")
+    except Exception as e:
+        return JSONResponse(status_code=400, content={"message": "tts failed", "error": str(e)})
+
+
+@app.post("/tts/stream", summary="流式语音合成",
+          description="输入文本，流式返回音频数据。适用于长文本实时播放。",
+          responses={200: {"content": {"audio/wav": {}}}, 400: {"description": "合成失败"}})
+async def tts_stream_endpoint(request: TTSStreamRequest):
+    req = _build_req(request, streaming_mode=request.streaming_mode)
+    try:
+        gen = tts_pipeline.run(req)
+        media = request.media_type
+
+        def stream(gen: Generator):
+            first = True
+            for sr, chunk in gen:
+                if first and media == "wav":
+                    yield wave_header_chunk(sample_rate=sr)
+                    first = False
+                    yield pack_audio(chunk, sr, "raw")
+                else:
+                    yield pack_audio(chunk, sr, media)
+
+        return StreamingResponse(stream(gen), media_type=f"audio/{media}")
+    except Exception as e:
+        return JSONResponse(status_code=400, content={"message": "tts failed", "error": str(e)})
+
+
+# ── Main ────────────────────────────────────────────────
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="ATR TTS API Server")
+    parser.add_argument("-a", "--host", default="127.0.0.1", help="绑定地址 (默认 127.0.0.1)")
+    parser.add_argument("-p", "--port", type=int, default=9880, help="端口号 (默认 9880)")
+    args = parser.parse_args()
+
+    print(f"\n  API docs: http://{args.host}:{args.port}/docs\n")
+    uvicorn.run(app=app, host=args.host, port=args.port, workers=1)

ref_audio.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1922a8ca728643b2e62166bc3ffcd94253bf9faac2c5aa3af0fd9218d37f103c
+size 316654