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Add Whisper offline loading for air-gapped servers.

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Pre-download via HF mirror scripts so inner-network deploys avoid Hub Network is unreachable errors.

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
dekun
2026-06-12 16:11:57 +08:00
parent 39e29fe6a9
commit 0f5277c22e
6 changed files with 310 additions and 164 deletions
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.env.example
+2
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@@ -10,4 +10,6 @@ OLLAMA_PORT=11434
# ChatTTS 模型目录（预下载脚本写入） # ChatTTS 模型目录（预下载脚本写入）
# CHATTTS_MODEL_DIR=/opt/Trading_Studio/models/ChatTTS # CHATTTS_MODEL_DIR=/opt/Trading_Studio/models/ChatTTS
# WHISPER_MODEL_DIR=/opt/Trading_Studio/models/whisper
# WHISPER_MODEL_SIZE=small
# HF_ENDPOINT=https://hf-mirror.com # HF_ENDPOINT=https://hf-mirror.com
DEPLOY.md
+13 -2
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@@ -361,9 +361,20 @@ cd /opt/Trading_Studio
pip install -r requirements.txt pip install -r requirements.txt
``` ```
### 6.1 Faster-Whisper ### 6.1 Faster-Whisper（必须预下载）
随 `requirements.txt` 安装。首次运行会自动下载 `small` 模型（约 500MB）至 HuggingFace 缓存。 随 `requirements.txt` 安装。**内网服务器无法访问 HuggingFace 时会报 `Network is unreachable`。**
部署后执行（推荐与 ChatTTS 一起）：
```bash
cd /opt/Trading_Studio
bash scripts/download_all_models.sh
# 或仅 Whisper: bash scripts/download_whisper_models.sh small
pm2 restart trading_studio
```
模型目录：`/opt/Trading_Studio/models/whisper/small/`（含 `model.bin`）。
### 6.2 ChatTTS（必须预下载，勿依赖 GitHub） ### 6.2 ChatTTS（必须预下载，勿依赖 GitHub）
config.py
+17 -7
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@@ -76,23 +76,33 @@ SYSTEM_PROMPT = (
"去掉所有无意义的口头禅，字数不做删减。" "去掉所有无意义的口头禅，字数不做删减。"
) )
# ---------------------------------------------------------------------------
# 路径
# ---------------------------------------------------------------------------
BASE_DIR = Path(__file__).resolve().parent
INSTALL_DIR = Path("/opt/Trading_Studio")
# --------------------------------------------------------------------------- # ---------------------------------------------------------------------------
# Faster-Whisper 配置 # Faster-Whisper 配置
# --------------------------------------------------------------------------- # ---------------------------------------------------------------------------
WHISPER_MODEL_SIZE = "small" WHISPER_MODEL_SIZE = _env_str("WHISPER_MODEL_SIZE", "small")
WHISPER_DEVICE = "cuda" WHISPER_DEVICE = "cuda"
WHISPER_COMPUTE_TYPE = "float16" WHISPER_COMPUTE_TYPE = "float16"
WHISPER_LANGUAGE = "zh" WHISPER_LANGUAGE = "zh"
WHISPER_MODEL_DIR = Path(_env_str("WHISPER_MODEL_DIR", str(BASE_DIR / "models" / "whisper")))
WHISPER_HF_REPO = {
"tiny": "Systran/faster-whisper-tiny",
"base": "Systran/faster-whisper-base",
"small": "Systran/faster-whisper-small",
"medium": "Systran/faster-whisper-medium",
"large-v2": "Systran/faster-whisper-large-v2",
"large-v3": "Systran/faster-whisper-large-v3",
}
# --------------------------------------------------------------------------- # ---------------------------------------------------------------------------
# ChatTTS 配置 # ChatTTS 配置
# --------------------------------------------------------------------------- # ---------------------------------------------------------------------------
# 标准生产安装路径（/opt，root 部署）
INSTALL_DIR = Path("/opt/Trading_Studio")
# 项目根目录（开发/生产均自适应，以实际 app.py 所在目录为准）
BASE_DIR = Path(__file__).resolve().parent
# 固定音色 Embedding 存储路径 # 固定音色 Embedding 存储路径
SPEAKER_EMB_PATH = BASE_DIR / "speaker_emb.pt" SPEAKER_EMB_PATH = BASE_DIR / "speaker_emb.pt"
scripts/download_all_models.sh
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@@ -0,0 +1,15 @@
#!/usr/bin/env bash
# 一次性下载 Whisper + ChatTTS 全部模型（内网服务器部署必跑）
set -euo pipefail
ROOT="$(cd "$(dirname "$0")/.." && pwd)"
cd "${ROOT}"
echo "========== 下载 Whisper (small) =========="
bash scripts/download_whisper_models.sh small
echo ""
echo "========== 下载 ChatTTS =========="
bash scripts/download_chattts_models.sh
echo ""
echo "[OK] 全部模型下载完成，请: pm2 restart trading_studio"
scripts/download_whisper_models.sh
+59
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@@ -0,0 +1,59 @@
#!/usr/bin/env bash
# 预下载 Faster-Whisper 模型（HF 镜像，内网服务器离线可用）
# 用法: bash scripts/download_whisper_models.sh [tiny|base|small|medium|large-v3]
set -euo pipefail
ROOT="$(cd "$(dirname "$0")/.." && pwd)"
SIZE="${1:-small}"
VENV_PY="${ROOT}/venv/bin/python"
MODEL_DIR="${WHISPER_MODEL_DIR:-${ROOT}/models/whisper}/${SIZE}"
export HF_ENDPOINT="${HF_ENDPOINT:-https://hf-mirror.com}"
export HF_HUB_DOWNLOAD_TIMEOUT="${HF_HUB_DOWNLOAD_TIMEOUT:-600}"
export HF_HOME="${HF_HOME:-${ROOT}/models/hf_cache}"
export MODEL_DIR
export WHISPER_SIZE="${SIZE}"
echo "[INFO] Whisper 模型: ${SIZE}"
echo "[INFO] 保存目录: ${MODEL_DIR}"
echo "[INFO] HF 镜像: ${HF_ENDPOINT}"
if [[ ! -x "${VENV_PY}" ]]; then
echo "[ERROR] 未找到 venv，请先 bash deploy.sh deps"
exit 1
fi
"${VENV_PY}" -m pip install -q huggingface_hub
"${VENV_PY}" << 'PY'
import os
from pathlib import Path
from huggingface_hub import snapshot_download
size = os.environ["WHISPER_SIZE"]
repos = {
"tiny": "Systran/faster-whisper-tiny",
"base": "Systran/faster-whisper-base",
"small": "Systran/faster-whisper-small",
"medium": "Systran/faster-whisper-medium",
"large-v2": "Systran/faster-whisper-large-v2",
"large-v3": "Systran/faster-whisper-large-v3",
}
repo = repos.get(size)
if not repo:
raise SystemExit(f"未知模型尺寸: {size}, 可选: {list(repos)}")
target = Path(os.environ["MODEL_DIR"])
target.mkdir(parents=True, exist_ok=True)
print(f"[INFO] 正在下载 {repo} ...")
snapshot_download(repo_id=repo, local_dir=str(target), local_dir_use_symlinks=False)
if not (target / "model.bin").is_file():
raise SystemExit(f"[ERROR] 下载不完整，未找到 model.bin: {target}")
print(f"[OK] Whisper 模型就绪: {target}")
PY
echo ""
echo "[OK] 请执行: pm2 restart trading_studio"
whisper_service.py
+204 -155
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@@ -1,155 +1,204 @@
""" """
Faster-Whisper CUDA 语音识别服务 Faster-Whisper CUDA 语音识别服务
封装本地 GPU 加速的音频转写逻辑，适配 RTX 3060 Ti 8GB 显存。 封装本地 GPU 加速的音频转写逻辑，适配 RTX 3060 Ti 8GB 显存。
""" """
from __future__ import annotations from __future__ import annotations
import logging import logging
import traceback import os
from typing import Optional, Tuple import traceback
from pathlib import Path
from config import ( from typing import Optional, Tuple
WHISPER_COMPUTE_TYPE,
WHISPER_DEVICE, from config import (
WHISPER_LANGUAGE, BASE_DIR,
WHISPER_MODEL_SIZE, HF_ENDPOINT,
) HF_HOME,
HF_HUB_DOWNLOAD_TIMEOUT,
logger = logging.getLogger(__name__) WHISPER_COMPUTE_TYPE,
WHISPER_DEVICE,
# 全局懒加载模型实例，避免 Gradio 重复初始化占用显存 WHISPER_HF_REPO,
_model = None WHISPER_LANGUAGE,
_model_error: Optional[str] = None WHISPER_MODEL_DIR,
WHISPER_MODEL_SIZE,
)
def _is_cuda_error(exc: BaseException) -> bool:
"""判断异常是否与 CUDA/GPU 相关。""" logger = logging.getLogger(__name__)
msg = str(exc).lower()
cuda_keywords = ( _model = None
"cuda", _model_error: Optional[str] = None
"cudnn",
"cublas",
"gpu", def _ensure_hf_env() -> None:
"out of memory", os.environ.setdefault("HF_ENDPOINT", HF_ENDPOINT)
"no kernel image", os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", str(HF_HUB_DOWNLOAD_TIMEOUT))
"device-side assert", os.environ.setdefault("HF_HOME", str(HF_HOME))
) WHISPER_MODEL_DIR.mkdir(parents=True, exist_ok=True)
return any(k in msg for k in cuda_keywords)
def _whisper_local_path() -> Optional[Path]:
def get_whisper_model(): """返回已预下载的本地模型目录。"""
""" local = WHISPER_MODEL_DIR / WHISPER_MODEL_SIZE
获取或初始化 Faster-Whisper 模型。 if (local / "model.bin").is_file():
强制 device=cuda, compute_type=float16。 return local
""" return None
global _model, _model_error
if _model is not None: def _is_cuda_error(exc: BaseException) -> bool:
return _model, None msg = str(exc).lower()
cuda_keywords = (
if _model_error is not None: "cuda", "cudnn", "cublas", "gpu",
return None, _model_error "out of memory", "no kernel image", "device-side assert",
)
try: return any(k in msg for k in cuda_keywords)
from faster_whisper import WhisperModel
logger.info( def _is_network_error(exc: BaseException) -> bool:
"正在加载 Whisper 模型: size=%s, device=%s, compute_type=%s", msg = str(exc).lower()
WHISPER_MODEL_SIZE, return any(
WHISPER_DEVICE, k in msg
WHISPER_COMPUTE_TYPE, for k in (
) "network is unreachable",
_model = WhisperModel( "connection error",
WHISPER_MODEL_SIZE, "connecterror",
device=WHISPER_DEVICE, "timed out",
compute_type=WHISPER_COMPUTE_TYPE, "couldn't connect",
) "name resolution",
logger.info("Whisper 模型加载成功。") "hub",
return _model, None )
)
except ImportError as exc:
_model_error = (
"未安装 faster-whisper，请执行: pip install faster-whisper\n" def _build_load_error(exc: BaseException) -> str:
f"原始错误: {exc}" lines = [
) "Whisper 模型加载失败。",
logger.exception("faster-whisper 导入失败") f"详情: {exc}",
return None, _model_error "",
]
except Exception as exc: if _is_network_error(exc):
if _is_cuda_error(exc): lines.extend([
_model_error = ( "原因：服务器无法访问 HuggingFace 下载模型（内网/无外网常见）。",
"CUDA 初始化失败，请检查 NVIDIA 驱动、CUDA 运行时及 cuDNN 是否正确安装。\n" "请在服务器执行（走 HF 镜像，仅需一次）：",
f"错误详情: {exc}\n" f" cd {BASE_DIR}",
f"{traceback.format_exc()}" " bash scripts/download_whisper_models.sh",
) " pm2 restart trading_studio",
else: "",
_model_error = f"Whisper 模型加载失败: {exc}\n{traceback.format_exc()}" f"模型将保存到: {WHISPER_MODEL_DIR / WHISPER_MODEL_SIZE}",
logger.exception("Whisper 模型加载异常") ])
return None, _model_error else:
lines.append(f"完整日志:\n{traceback.format_exc()}")
return "\n".join(lines)
def transcribe_audio(audio_path: str) -> Tuple[bool, str]:
"""
将音频文件转写为中文文本。 def get_whisper_model():
"""获取或初始化 Faster-Whisper 模型（优先本地预下载）。"""
Args: global _model, _model_error
audio_path: 本地音频文件绝对或相对路径
if _model is not None:
Returns: return _model, None
(success, text_or_error_message)
""" if _model_error is not None:
if not audio_path: return None, _model_error
return False, "未提供音频文件路径。"
try:
model, init_error = get_whisper_model() _ensure_hf_env()
if model is None: from faster_whisper import WhisperModel
return False, init_error or "Whisper 模型不可用。"
local = _whisper_local_path()
try: if local:
segments, info = model.transcribe( model_id = str(local)
audio_path, logger.info("Whisper 从本地加载: %s", model_id)
language=WHISPER_LANGUAGE, else:
beam_size=5, model_id = WHISPER_MODEL_SIZE
vad_filter=True, logger.warning(
) "未找到本地 Whisper 模型 (%s)，尝试在线下载（可能失败）…",
WHISPER_MODEL_DIR / WHISPER_MODEL_SIZE,
text_parts = [] )
for segment in segments:
text_parts.append(segment.text.strip()) logger.info(
"Whisper 加载: model=%s, device=%s, compute_type=%s",
result_text = "".join(text_parts).strip() model_id,
WHISPER_DEVICE,
if not result_text: WHISPER_COMPUTE_TYPE,
return False, ( )
"识别结果为空，请检查音频是否有效、音量是否足够，" _model = WhisperModel(
f"或尝试更换格式。检测到语言: {getattr(info, 'language', 'unknown')}" model_id,
) device=WHISPER_DEVICE,
compute_type=WHISPER_COMPUTE_TYPE,
logger.info( download_root=str(WHISPER_MODEL_DIR),
"转写完成: 语言=%s, 概率=%.2f, 字数=%d", )
getattr(info, "language", "?"), logger.info("Whisper 模型加载成功。")
getattr(info, "language_probability", 0.0), return _model, None
len(result_text),
) except ImportError as exc:
return True, result_text _model_error = (
"未安装 faster-whisper，请执行: pip install faster-whisper\n"
except Exception as exc: f"原始错误: {exc}"
if _is_cuda_error(exc): )
err = ( logger.exception("faster-whisper 导入失败")
"CUDA 推理异常：显存可能不足或 GPU 状态异常。" return None, _model_error
"建议关闭其他占用显存的进程后重试。\n"
f"错误详情: {exc}" except Exception as exc:
) if _is_cuda_error(exc):
else: _model_error = (
err = f"音频转写失败: {exc}\n{traceback.format_exc()}" "CUDA 初始化失败，请检查 NVIDIA 驱动、CUDA 运行时及 cuDNN。\n"
f"错误详情: {exc}"
logger.exception("transcribe_audio 失败") )
return False, err else:
_model_error = _build_load_error(exc)
logger.exception("Whisper 模型加载异常")
def reset_whisper_model() -> None: return None, _model_error
"""释放模型引用（用于调试或显存回收）。"""
global _model, _model_error
_model = None def transcribe_audio(audio_path: str) -> Tuple[bool, str]:
_model_error = None """将音频文件转写为中文文本。"""
if not audio_path:
return False, "未提供音频文件路径。"
model, init_error = get_whisper_model()
if model is None:
return False, init_error or "Whisper 模型不可用。"
try:
segments, info = model.transcribe(
audio_path,
language=WHISPER_LANGUAGE,
beam_size=5,
vad_filter=True,
)
text_parts = [segment.text.strip() for segment in segments]
result_text = "".join(text_parts).strip()
if not result_text:
return False, (
"识别结果为空，请检查音频是否有效、音量是否足够，"
f"或尝试更换格式。检测到语言: {getattr(info, 'language', 'unknown')}"
)
logger.info(
"转写完成: 语言=%s, 概率=%.2f, 字数=%d",
getattr(info, "language", "?"),
getattr(info, "language_probability", 0.0),
len(result_text),
)
return True, result_text
except Exception as exc:
if _is_cuda_error(exc):
err = (
"CUDA 推理异常：显存可能不足或 GPU 状态异常。\n"
f"错误详情: {exc}"
)
else:
err = f"音频转写失败: {exc}\n{traceback.format_exc()}"
logger.exception("transcribe_audio 失败")
return False, err
def reset_whisper_model() -> None:
global _model, _model_error
_model = None
_model_error = None