Add Git LFS config

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,38 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+
+# Checkpoints and cache
+checkpoints/
+*.ckpt
+*.pth
+*.pt
+
+# Audio files (except reference)
+output*.wav
+chunk_*.wav
+temp_*.wav
+
+# Logs
+*.log
+server.log
+
+# macOS
+.DS_Store
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Test files
+test_*.py
+verify_*.py
+measure_*.py
+*.sh

Dockerfile

@@ -0,0 +1,36 @@
+FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04
+
+# 環境変数
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+ENV HF_HUB_CACHE=/app/checkpoints/hf_cache
+
+# 作業ディレクトリ
+WORKDIR /app
+
+# システムパッケージのインストール
+RUN apt-get update && apt-get install -y \
+    python3.10 \
+    python3-pip \
+    git \
+    ffmpeg \
+    libsndfile1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Pythonパッケージのインストール
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# アプリケーションファイルのコピー
+COPY app.py .
+COPY source_original.wav .
+COPY seed-vc/ ./seed-vc/
+
+# チェックポイント用ディレクトリ作成
+RUN mkdir -p /app/checkpoints/hf_cache
+
+# ポート公開（HF Spacesは7860を使用）
+EXPOSE 7860
+
+# アプリケーション起動
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,393 @@
+"""
+Seed-VC Streaming API Server
+architecture.md と model_ref.md に基づいて実装
+"""
+import io
+import os
+import sys
+import time
+import uuid
+from typing import Optional, Dict
+from argparse import Namespace
+
+import numpy as np
+import soundfile as sf
+import librosa
+import torch
+import torchaudio
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi.responses import Response
+from pydantic import BaseModel
+
+# Seed-VC
+sys.path.insert(0, 'seed-vc')
+os.environ['HF_HUB_CACHE'] = './checkpoints/hf_cache'
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
+
+# MPSを無効化してCPUを強制
+import torch
+torch.backends.mps.is_available = lambda: False
+
+from inference import load_models
+
+# =============================================================================
+# Configuration (architecture.md Section 5)
+# =============================================================================
+DEFAULT_SAMPLE_RATE = 16000
+DEFAULT_CHUNK_LEN_MS = 1000
+DEFAULT_OVERLAP_MS = 200
+SESSION_EXPIRE_SEC = 600
+
+# model_ref.md Section 3.1
+DEFAULT_REF_PRESET = "default_01"
+REF_PRESETS = {
+    "default_01": "source_original.wav",
+}
+
+# =============================================================================
+# Global Variables
+# =============================================================================
+# MPSは避ける（seed-vcとの互換性問題）
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Seed-VCモデル (inference.py load_models()の戻り値)
+model = None
+semantic_fn = None
+f0_fn = None
+vocoder_fn = None
+campplus_model = None
+to_mel = None
+mel_fn_args = None
+model_sr = 22050
+
+# =============================================================================
+# Session State (architecture.md Section 4.1)
+# =============================================================================
+class SessionState:
+    def __init__(self, sample_rate: int, tgt_speaker_id: Optional[str] = None):
+        self.sample_rate = sample_rate
+        self.tgt_speaker_id = tgt_speaker_id
+        self.last_output_tail: Optional[np.ndarray] = None
+        # model_ref.md Section 3: 参照音声の管理
+        self.ref_audio_tensor = None  # 参照音声 (model_sr, float tensor)
+        self.ref_mel = None
+        self.ref_semantic = None
+        self.style_embed = None
+        self.last_access_ts = time.time()
+        self.chunk_len_ms = DEFAULT_CHUNK_LEN_MS
+        self.overlap_ms = DEFAULT_OVERLAP_MS
+
+SESSIONS: Dict[str, SessionState] = {}
+
+# =============================================================================
+# FastAPI App
+# =============================================================================
+app = FastAPI(title="Seed-VC Streaming API", version="1.0.0")
+
+@app.on_event("startup")
+async def startup_event():
+    """モデルロード (architecture.md Section 4.3.1)"""
+    global model, semantic_fn, f0_fn, vocoder_fn, campplus_model, to_mel, mel_fn_args, model_sr
+
+    print(f"Device: {device}")
+    print("Loading Seed-VC models...")
+
+    # inference.pyのload_modelsをそのまま使用
+    args = Namespace(
+        f0_condition=False,  # model_ref.md: 22050Hz系を使う
+        checkpoint=None,
+        config=None,
+        fp16=False
+    )
+
+    model, semantic_fn, f0_fn, vocoder_fn, campplus_model, to_mel, mel_fn_args = load_models(args)
+    model_sr = mel_fn_args['sampling_rate']
+
+    print(f"Models loaded! SR={model_sr}")
+
+# =============================================================================
+# Pydantic Models (architecture.md Section 3.2)
+# =============================================================================
+class SessionCreateRequest(BaseModel):
+    sample_rate: int = DEFAULT_SAMPLE_RATE
+    tgt_speaker_id: Optional[str] = None
+    ref_preset_id: Optional[str] = None
+    use_uploaded_ref: bool = False
+    chunk_len_ms: int = DEFAULT_CHUNK_LEN_MS
+    overlap_ms: int = DEFAULT_OVERLAP_MS
+
+class SessionCreateResponse(BaseModel):
+    session_id: str
+    sample_rate: int
+    chunk_len_ms: int
+    overlap_ms: int
+
+class SessionEndRequest(BaseModel):
+    session_id: str
+
+# =============================================================================
+# Utility Functions
+# =============================================================================
+def load_wav_to_numpy(file_bytes: bytes, target_sr: int) -> tuple[np.ndarray, int]:
+    """WAVファイルをnumpy配列に変換"""
+    audio, sr = sf.read(io.BytesIO(file_bytes))
+    if len(audio.shape) > 1:
+        audio = audio.mean(axis=1)
+    if sr != target_sr:
+        audio = librosa.resample(audio, orig_sr=sr, target_sr=target_sr)
+        sr = target_sr
+    if audio.dtype in (np.float32, np.float64):
+        audio = (audio * 32767).astype(np.int16)
+    return audio, sr
+
+def numpy_to_wav_bytes(audio: np.ndarray, sr: int) -> bytes:
+    """numpy配列をWAVバイト列に変換"""
+    buffer = io.BytesIO()
+    sf.write(buffer, audio, sr, format="WAV", subtype="PCM_16")
+    buffer.seek(0)
+    return buffer.read()
+
+def crossfade(prev_tail: Optional[np.ndarray], new_chunk: np.ndarray, fade_len: int) -> np.ndarray:
+    """クロスフェード (architecture.md Section 4.2.1)"""
+    if prev_tail is None:
+        return new_chunk
+
+    fade_len = min(fade_len, len(prev_tail), len(new_chunk))
+    if fade_len <= 0:
+        return new_chunk
+
+    fade_in = np.linspace(0.0, 1.0, fade_len, endpoint=True)
+    fade_out = 1.0 - fade_in
+
+    mixed_head = (prev_tail[-fade_len:] * fade_out + new_chunk[:fade_len] * fade_in).astype(np.int16)
+    tail = new_chunk[fade_len:]
+    return np.concatenate([mixed_head, tail])
+
+def prepare_reference_audio(audio_path: str, state: SessionState):
+    """
+    参照音声を準備 (model_ref.md Section 3)
+    inference.py の main() と同じロジック
+    """
+    # 参照音声をロード
+    ref_audio, file_sr = librosa.load(audio_path, sr=model_sr)
+    ref_audio = ref_audio[:model_sr * 25]  # 25秒まで
+
+    # tensorに変換
+    ref_audio_tensor = torch.tensor(ref_audio).unsqueeze(0).float().to(device)
+    state.ref_audio_tensor = ref_audio_tensor
+
+    # mel spectrogram
+    state.ref_mel = to_mel(ref_audio_tensor)
+
+    # Whisper semantic features
+    ref_waves_16k = torchaudio.functional.resample(ref_audio_tensor, model_sr, 16000)
+    state.ref_semantic = semantic_fn(ref_waves_16k)
+
+    # CAMPPlus style embedding
+    feat = torchaudio.compliance.kaldi.fbank(
+        ref_waves_16k,
+        num_mel_bins=80,
+        dither=0,
+        sample_frequency=16000
+    )
+    feat = feat - feat.mean(dim=0, keepdim=True)
+    state.style_embed = campplus_model(feat.unsqueeze(0))
+
+    print(f"Reference prepared: mel={state.ref_mel.shape}, semantic={state.ref_semantic.shape}")
+
+def seed_vc_infer(chunk_np: np.ndarray, chunk_sr: int, state: SessionState) -> np.ndarray:
+    """
+    Seed-VCで音声変換 (architecture.md Section 4.3.2)
+    inference.py main()のロジックを使用
+    """
+    # int16 -> float32
+    if chunk_np.dtype == np.int16:
+        source_audio = chunk_np.astype(np.float32) / 32768.0
+    else:
+        source_audio = chunk_np.astype(np.float32)
+
+    # model_sr にリサンプル
+    if chunk_sr != model_sr:
+        source_audio = librosa.resample(source_audio, orig_sr=chunk_sr, target_sr=model_sr)
+
+    # tensor化
+    source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
+
+    # 16kHz変換してWhisper特徴抽出
+    converted_waves_16k = torchaudio.functional.resample(source_audio, model_sr, 16000)
+    S_alt = semantic_fn(converted_waves_16k)
+
+    # mel spectrogram
+    mel = to_mel(source_audio.to(device).float())
+
+    # target lengths
+    target_lengths = torch.LongTensor([mel.size(2)]).to(device)
+    target2_lengths = torch.LongTensor([state.ref_mel.size(2)]).to(device)
+
+    # length regulator (inference.py line 354-360)
+    with torch.no_grad():
+        cond, _, _, _, _ = model.length_regulator(
+            S_alt, ylens=target_lengths, n_quantizers=3, f0=None
+        )
+        prompt_condition, _, _, _, _ = model.length_regulator(
+            state.ref_semantic, ylens=target2_lengths, n_quantizers=3, f0=None
+        )
+
+    # 条件結合
+    cat_condition = torch.cat([prompt_condition, cond], dim=1)
+
+    # CFM inference (inference.py line 373-376)
+    with torch.no_grad():
+        vc_target = model.cfm.inference(
+            cat_condition,
+            torch.LongTensor([cat_condition.size(1)]).to(device),
+            state.ref_mel,
+            state.style_embed,
+            None,
+            10,  # diffusion_steps
+            inference_cfg_rate=0.7
+        )
+        # プロンプト部分削除
+        vc_target = vc_target[:, :, state.ref_mel.size(-1):]
+
+    # Vocoder (inference.py line 378)
+    with torch.no_grad():
+        vc_wave = vocoder_fn(vc_target.float()).squeeze()
+    vc_wave = vc_wave[None, :]
+
+    # numpy変換
+    output_wave = vc_wave[0].cpu().numpy()
+
+    # int16に戻す
+    output_int16 = (output_wave * 32767).clip(-32768, 32767).astype(np.int16)
+
+    return output_int16
+
+# =============================================================================
+# Endpoints (architecture.md Section 3.2)
+# =============================================================================
+@app.get("/health")
+async def health_check():
+    """3.2.1 GET /health"""
+    return {"status": "ok"}
+
+@app.post("/session", response_model=SessionCreateResponse)
+async def create_session(body: SessionCreateRequest):
+    """
+    3.2.2 POST /session
+    model_ref.md Section 2.2(A)
+    """
+    session_id = str(uuid.uuid4())
+
+    state = SessionState(
+        sample_rate=body.sample_rate,
+        tgt_speaker_id=body.tgt_speaker_id
+    )
+    state.chunk_len_ms = body.chunk_len_ms
+    state.overlap_ms = body.overlap_ms
+
+    # 参照音声設定 (model_ref.md Section 3.2)
+    if not body.use_uploaded_ref:
+        preset_id = body.ref_preset_id or DEFAULT_REF_PRESET
+        wav_path = REF_PRESETS.get(preset_id, REF_PRESETS[DEFAULT_REF_PRESET])
+        prepare_reference_audio(wav_path, state)
+
+    SESSIONS[session_id] = state
+
+    return SessionCreateResponse(
+        session_id=session_id,
+        sample_rate=body.sample_rate,
+        chunk_len_ms=body.chunk_len_ms,
+        overlap_ms=body.overlap_ms,
+    )
+
+@app.post("/session/ref")
+async def upload_ref_audio(
+    session_id: str = Form(...),
+    ref_audio: UploadFile = File(...)
+):
+    """
+    model_ref.md Section 2.2(B)
+    """
+    if session_id not in SESSIONS:
+        raise HTTPException(status_code=400, detail="Invalid session_id")
+
+    state = SESSIONS[session_id]
+
+    # 一時ファイル保存
+    import tempfile
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+        content = await ref_audio.read()
+        tmp.write(content)
+        tmp_path = tmp.name
+
+    try:
+        prepare_reference_audio(tmp_path, state)
+    finally:
+        os.unlink(tmp_path)
+
+    state.last_access_ts = time.time()
+    return {"status": "ok"}
+
+@app.post("/chunk")
+async def process_chunk(
+    session_id: str = Form(...),
+    chunk_id: int = Form(...),
+    audio: UploadFile = File(...)
+):
+    """
+    3.2.3 POST /chunk
+    architecture.md Section 3.2.3 サーバ内部処理フロー
+    """
+    if session_id not in SESSIONS:
+        raise HTTPException(status_code=400, detail="Invalid session_id")
+
+    state = SESSIONS[session_id]
+
+    if chunk_id < 0:
+        raise HTTPException(status_code=400, detail="chunk_id must be non-negative")
+
+    # Step 2: 音声読み込み
+    audio_bytes = await audio.read()
+    chunk_np, chunk_sr = load_wav_to_numpy(audio_bytes, target_sr=state.sample_rate)
+
+    # Step 3: サンプルレートチェック
+    if chunk_sr != state.sample_rate:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Sample rate mismatch: expected {state.sample_rate}, got {chunk_sr}"
+        )
+
+    # Step 4: Seed-VCで変換
+    converted = seed_vc_infer(chunk_np, chunk_sr, state)
+
+    # Step 5: クロスフェード
+    fade_len = int(model_sr * state.overlap_ms / 1000)
+    output = crossfade(state.last_output_tail, converted, fade_len)
+
+    # Step 6: tail更新
+    if len(output) >= fade_len:
+        state.last_output_tail = output[-fade_len:].copy()
+    else:
+        state.last_output_tail = output.copy()
+
+    state.last_access_ts = time.time()
+
+    # Step 7: WAVエンコード
+    wav_bytes = numpy_to_wav_bytes(output, model_sr)
+
+    return Response(
+        content=wav_bytes,
+        media_type="audio/wav",
+        headers={"X-Chunk-Id": str(chunk_id)}
+    )
+
+@app.post("/end")
+async def end_session(body: SessionEndRequest):
+    """3.2.4 POST /end"""
+    SESSIONS.pop(body.session_id, None)
+    return {"status": "ended"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,20 @@
+fastapi==0.109.0
+uvicorn[standard]==0.27.0
+python-multipart==0.0.6
+soundfile==0.12.1
+
+# Seed-VC dependencies (from seed-vc/requirements.txt)
+torch==2.4.0
+torchaudio==2.4.0
+scipy==1.13.1
+librosa==0.10.2
+huggingface-hub>=0.28.1
+munch==4.0.0
+einops==0.8.0
+descript-audio-codec==1.0.0
+transformers==4.46.3
+numpy==1.26.4
+hydra-core==1.3.2
+pyyaml
+python-dotenv
+accelerate

seed-vc

@@ -0,0 +1 @@
+Subproject commit 51383efd921027683c89e5348211d93ff12ac2a8