这是常用的一版
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api_server.py
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134
api_server.py
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#!/usr/bin/env python3
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import os
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import sys
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# 关键：强制离线模式，避免连接 HuggingFace
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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os.environ["HF_DATASETS_OFFLINE"] = "1"
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import traceback
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from pathlib import Path
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def log_error(msg):
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print(f"[ERROR] {msg}", file=sys.stderr)
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def log_info(msg):
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print(f"[INFO] {msg}")
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try:
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log_info("Starting API server (OFFLINE MODE)...")
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log_info(f"Python version: {sys.version}")
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from fastapi import FastAPI, UploadFile, File, Form
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from fastapi.responses import FileResponse
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import uvicorn
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import tempfile
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log_info("Dependencies imported successfully")
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from indextts.infer_v2 import IndexTTS2
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from transformers import SeamlessM4TFeatureExtractor
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log_info("IndexTTS2 imported successfully")
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# 检查模型路径
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cfg_path = "checkpoints/config.yaml"
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model_dir = "checkpoints"
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if not os.path.exists(cfg_path):
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log_error(f"Config file not found: {cfg_path}")
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sys.exit(1)
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log_info("Initializing model...")
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# DGX Spark 优化：禁用 CUDA 内核编译（因为 sm_121 不支持 compute_70）
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# 强制使用纯 PyTorch 模式
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os.environ["DISABLE_BIGVGAN_CUDA_KERNEL"] = "1"
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local_w2v_path = "./models/w2v-bert-2.0"
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if os.path.exists(local_w2v_path):
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log_info(f"Loading w2v-bert-2.0 from local path: {local_w2v_path}")
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feature_extractor = SeamlessM4TFeatureExtractor.from_pretrained(local_w2v_path)
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log_info("Feature extractor loaded locally")
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else:
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log_info("Local w2v-bert-2.0 not found, will try to load from cache...")
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# 初始化 IndexTTS2
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try:
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tts = IndexTTS2(
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cfg_path=cfg_path,
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model_dir=model_dir,
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use_fp16=True, # DGX Spark 支持 FP16
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use_cuda_kernel=False # 关键：禁用自定义 CUDA 内核，避免 sm_121 编译错误
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)
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log_info("Model loaded successfully!")
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except Exception as e:
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log_error(f"Model initialization failed: {e}")
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traceback.print_exc()
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sys.exit(1)
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app = FastAPI(title="IndexTTS2 API (DGX Spark)")
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@app.post("/tts")
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async def text_to_speech(
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text: str = Form(...),
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spk_audio: UploadFile = File(...),
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emo_audio: UploadFile = None,
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emo_alpha: float = Form(0.8)
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):
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log_info(f"Processing: {text[:50]}...")
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as spk_tmp:
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content = await spk_audio.read()
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spk_tmp.write(content)
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spk_path = spk_tmp.name
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emo_path = None
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if emo_audio:
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as emo_tmp:
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content = await emo_audio.read()
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emo_tmp.write(content)
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emo_path = emo_tmp.name
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output_path = tempfile.mktemp(suffix=".wav")
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try:
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tts.infer(
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spk_audio_prompt=spk_path,
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text=text,
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output_path=output_path,
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emo_audio_prompt=emo_path,
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emo_alpha=emo_alpha
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)
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return FileResponse(
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output_path,
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media_type="audio/wav",
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filename="generated.wav"
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)
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finally:
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if os.path.exists(spk_path):
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os.unlink(spk_path)
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if emo_path and os.path.exists(emo_path):
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os.unlink(emo_path)
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@app.get("/health")
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def health_check():
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return {"status": "ok", "model": "IndexTTS2", "device": "cuda"}
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@app.get("/")
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def root():
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return {"message": "IndexTTS2 API for DGX Spark", "docs": "/docs"}
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log_info("Starting server on port 11996...")
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=11996)
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except Exception as e:
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log_error(f"Fatal error: {e}")
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traceback.print_exc()
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sys.exit(1)
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@ -113,7 +113,7 @@ class IndexTTS2:
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print(f"{e!r}")
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self.use_cuda_kernel = False
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self.extract_features = SeamlessM4TFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
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self.extract_features = SeamlessM4TFeatureExtractor.from_pretrained("./models/w2v-bert-2.0")
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self.semantic_model, self.semantic_mean, self.semantic_std = build_semantic_model(
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os.path.join(self.model_dir, self.cfg.w2v_stat))
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self.semantic_model = self.semantic_model.to(self.device)
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@ -122,7 +122,8 @@ class IndexTTS2:
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self.semantic_std = self.semantic_std.to(self.device)
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semantic_codec = build_semantic_codec(self.cfg.semantic_codec)
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semantic_code_ckpt = hf_hub_download("amphion/MaskGCT", filename="semantic_codec/model.safetensors")
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# semantic_code_ckpt = hf_hub_download("./models/MaskGCT", filename="./models/MaskGCT/semantic_codec/model.safetensors")
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semantic_code_ckpt = "./models/MaskGCT/semantic_codec/model.safetensors"
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safetensors.torch.load_model(semantic_codec, semantic_code_ckpt)
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self.semantic_codec = semantic_codec.to(self.device)
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self.semantic_codec.eval()
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@ -151,9 +152,8 @@ class IndexTTS2:
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print(">> s2mel weights restored from:", s2mel_path)
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# load campplus_model
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campplus_ckpt_path = hf_hub_download(
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"funasr/campplus", filename="campplus_cn_common.bin"
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)
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# campplus_ckpt_path = hf_hub_download("funasr/campplus", filename="campplus_cn_common.bin")
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campplus_ckpt_path = "./models/campplus/campplus_cn_common.bin"
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campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
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campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
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self.campplus_model = campplus_model.to(self.device)
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Binary file not shown.
@ -1,2 +1,3 @@
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# ninja log v7
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0 8799 1771051743097851885 anti_alias_activation.o 6d4acaa78fc0c336
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0 9806 1771307045391159629 anti_alias_activation.o 6d4acaa78fc0c336
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@ -85,7 +85,7 @@ class JsonHParams:
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def build_semantic_model(path_='./models/tts/maskgct/ckpt/wav2vec2bert_stats.pt'):
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semantic_model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
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semantic_model = Wav2Vec2BertModel.from_pretrained("./models/w2v-bert-2.0")
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semantic_model.eval()
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stat_mean_var = torch.load(path_)
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semantic_mean = stat_mean_var["mean"]
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