refactor: 更新音频文件路径和UI样式调整
fix: 修正TTS提供商配置中的null值问题 chore: 清理无用文件和更新输入文本内容
This commit is contained in:
hex2077
72
server/ext/doubao-voice-list.py
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72
server/ext/doubao-voice-list.py
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import requests
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import json
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# --- 配置 ---
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# 请将这里的URL替换为你要获取数据的实际URL
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URL = "https://lf3-config.bytetcc.com/obj/tcc-config-web/tcc-v2-data-lab.speech.tts_middle_layer-default" # <--- 替换成你的URL
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OUTPUT_FILENAME = "data_from_url_volc_bigtts.json"
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# 设置请求超时（秒），防止程序因网络问题无限期等待
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TIMEOUT = 10
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print(f"准备从URL获取数据: {URL}")
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# --- 主逻辑 ---
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try:
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# 1. 发送GET请求到URL
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# a. requests.get() 发送请求
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# b. timeout=TIMEOUT 是一个好习惯，避免程序卡死
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response = requests.get(URL, timeout=TIMEOUT)
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# 2. 检查响应状态码，确保请求成功 (例如 200 OK)
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# response.raise_for_status() 会在响应码为 4xx 或 5xx (客户端/服务器错误) 时抛出异常
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response.raise_for_status()
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print("✅ HTTP请求成功，状态码: 200 OK")
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# 3. 解析最外层的JSON
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# requests库的 .json() 方法可以直接将响应内容解析为Python字典
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# 这完成了我们的第一次解析
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outer_data = response.json()
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# 4. 从解析后的字典中提取内层的JSON字符串
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# 这一步可能会因为键不存在而抛出KeyError
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volc_bigtts_string = outer_data['data']['volc_bigtts']
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# 5. 解析内层的JSON字符串，得到最终的JSON数组（Python列表）
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# 这一步可能会因为字符串格式不正确而抛出JSONDecodeError
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final_json_array = json.loads(volc_bigtts_string)
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print("✅ 成功解析嵌套的JSON数据。")
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print("解析出的数组内容:", final_json_array)
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# 6. 将最终的JSON数组写入本地文件
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with open(OUTPUT_FILENAME, 'w', encoding='utf-8') as f:
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json.dump(final_json_array, f, indent=4, ensure_ascii=False)
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print(f"\n🎉 成功将数据写入文件: {OUTPUT_FILENAME}")
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# --- 错误处理 ---
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# 将不同类型的错误分开捕获，可以提供更清晰的错误信息
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except requests.exceptions.HTTPError as errh:
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# 捕获HTTP错误，如 404 Not Found, 500 Internal Server Error
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print(f"❌ HTTP错误: {errh}")
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except requests.exceptions.ConnectionError as errc:
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# 捕获连接错误，如DNS查询失败、拒绝连接等
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print(f"❌ 连接错误: {errc}")
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except requests.exceptions.Timeout as errt:
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# 捕获请求超时
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print(f"❌ 请求超时: {errt}")
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except requests.exceptions.RequestException as err:
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# 捕获requests库可能抛出的其他所有异常
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print(f"❌ 请求发生未知错误: {err}")
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except json.JSONDecodeError:
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# 捕获JSON解析错误
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# 可能发生在 response.json() 或 json.loads()
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print("❌ JSON解析失败。从URL返回的数据或内层字符串不是有效的JSON格式。")
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# 如果需要调试，可以打印原始响应内容
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# print("原始响应内容:", response.text)
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except KeyError:
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# 捕获键错误
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print("❌ JSON结构不符合预期，找不到 'data' 或 'volc_bigtts' 键。")
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except Exception as e:
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# 捕获所有其他未预料到的异常
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print(f"❌ 发生未知错误: {e}")
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284
server/ext/index-tts-api.py
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284
server/ext/index-tts-api.py
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# 用法：
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# python ./indextts/index-tts-api.py
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# http://localhost:7899/synthesize?text=Hello world, this is a test using FastAPI&verbose=true&max_text_tokens_per_sentence=80&server_audio_prompt_path=johnny-v.wav
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import os
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import shutil
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import tempfile
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import time
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from typing import Optional
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import re # For sanitizing filenames/paths
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import uvicorn
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from fastapi import FastAPI, Query, HTTPException, BackgroundTasks
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from fastapi.responses import FileResponse
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# Removed File and UploadFile as we are not uploading anymore
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# Assuming infer.py is in the same directory or in PYTHONPATH
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from infer import IndexTTS
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# --- Configuration ---
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MODEL_CFG_PATH = "checkpoints/config.yaml"
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MODEL_DIR = "checkpoints"
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DEFAULT_IS_FP16 = True
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DEFAULT_USE_CUDA_KERNEL = None
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DEFAULT_DEVICE = None
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# Default local audio prompt, can be overridden by a query parameter
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DEFAULT_SERVER_AUDIO_PROMPT_PATH = "prompts/fdt-v.wav" # <-- CHANGE THIS TO YOUR ACTUAL DEFAULT PROMPT
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# Define a base directory from which user-specified prompts can be loaded
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# THIS IS A SECURITY MEASURE. Prompts outside this directory (and its subdirs) won't be allowed.
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ALLOWED_PROMPT_BASE_DIR = os.path.abspath("prompts") # Example: /app/prompts
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# --- Global TTS instance ---
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tts_model: Optional[IndexTTS] = None
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app = FastAPI(title="IndexTTS FastAPI Service")
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@app.on_event("startup")
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async def startup_event():
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global tts_model
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print("Loading IndexTTS model...")
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start_load_time = time.time()
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try:
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tts_model = IndexTTS(
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cfg_path=MODEL_CFG_PATH,
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model_dir=MODEL_DIR,
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is_fp16=DEFAULT_IS_FP16,
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device=DEFAULT_DEVICE,
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use_cuda_kernel=DEFAULT_USE_CUDA_KERNEL,
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)
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# Verify default prompt exists
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if not os.path.isfile(DEFAULT_SERVER_AUDIO_PROMPT_PATH):
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print(f"WARNING: Default server audio prompt file not found at: {DEFAULT_SERVER_AUDIO_PROMPT_PATH}")
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# Create the allowed prompts directory if it doesn't exist (optional, for convenience)
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if not os.path.isdir(ALLOWED_PROMPT_BASE_DIR):
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try:
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os.makedirs(ALLOWED_PROMPT_BASE_DIR, exist_ok=True)
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print(f"Created ALLOWED_PROMPT_BASE_DIR: {ALLOWED_PROMPT_BASE_DIR}")
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except Exception as e:
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print(f"WARNING: Could not create ALLOWED_PROMPT_BASE_DIR at {ALLOWED_PROMPT_BASE_DIR}: {e}")
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else:
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print(f"User-specified prompts will be loaded from: {ALLOWED_PROMPT_BASE_DIR}")
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except Exception as e:
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print(f"Error loading IndexTTS model: {e}")
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tts_model = None
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load_time = time.time() - start_load_time
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print(f"IndexTTS model loaded in {load_time:.2f} seconds.")
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if tts_model:
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print(f"Model ready on device: {tts_model.device}")
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else:
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print("Model FAILED to load.")
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async def cleanup_temp_dir(temp_dir_path: str):
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try:
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if os.path.exists(temp_dir_path):
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shutil.rmtree(temp_dir_path)
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print(f"Successfully cleaned up temporary directory: {temp_dir_path}")
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except Exception as e:
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print(f"Error cleaning up temporary directory {temp_dir_path}: {e}")
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def sanitize_path_component(component: str) -> str:
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"""Basic sanitization for a path component."""
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# Remove leading/trailing whitespace and dots
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component = component.strip().lstrip('.')
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# Replace potentially harmful characters or sequences
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component = re.sub(r'\.\.[/\\]', '', component) # Remove .. sequences
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component = re.sub(r'[<>:"|?*]', '_', component) # Replace illegal filename chars
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return component
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def get_safe_prompt_path(base_dir: str, user_path: Optional[str]) -> str:
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"""
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Constructs a safe path within the base_dir from a user-provided path.
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Prevents directory traversal.
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"""
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if not user_path:
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return "" # Or raise error if user_path is mandatory when called
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# Normalize user_path (e.g., handle mixed slashes, remove redundant ones)
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normalized_user_path = os.path.normpath(user_path)
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# Split the path into components and sanitize each one
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path_components = []
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head = normalized_user_path
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while True:
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head, tail = os.path.split(head)
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if tail:
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path_components.insert(0, sanitize_path_component(tail))
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elif head: # Handle case like "/path" or "path/" leading to empty tail
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path_components.insert(0, sanitize_path_component(head))
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break
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else: # Both head and tail are empty
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break
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if not head or head == os.sep or head == '.': # Stop if root or current dir
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break
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if not path_components:
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raise ValueError("Invalid or empty prompt path provided after sanitization.")
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# Join sanitized components. This prevents using absolute paths from user_path directly.
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# os.path.join will correctly use the OS's path separator.
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# The first component of user_path is NOT joined with base_dir if it's absolute.
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# We ensure user_path is treated as relative to base_dir.
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# So, we must ensure path_components doesn't represent an absolute path itself after sanitization.
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# The sanitize_path_component and os.path.normpath help, but the critical part is os.path.join(base_dir, *path_components)
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# Construct the full path relative to the base directory
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# *path_components will expand the list into arguments for join
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prospective_path = os.path.join(base_dir, *path_components)
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# Final check: ensure the resolved path is still within the base_dir
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# os.path.abspath resolves any '..' etc., in the prospective_path
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resolved_path = os.path.abspath(prospective_path)
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if not resolved_path.startswith(os.path.abspath(base_dir)):
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raise ValueError("Prompt path traversal attempt detected or path is outside allowed directory.")
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return resolved_path
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@app.api_route("/synthesize/", methods=["POST", "GET"], response_class=FileResponse)
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async def synthesize_speech(
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background_tasks: BackgroundTasks,
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text: str = Query(..., description="Text to synthesize."),
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# New parameter for specifying a server-side audio prompt path
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server_audio_prompt_path: Optional[str] = Query(None, description=f"Relative path to an audio prompt file on the server (within {ALLOWED_PROMPT_BASE_DIR}). If not provided, uses default."),
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verbose: bool = Query(False, description="Enable verbose logging."),
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max_text_tokens_per_sentence: int = Query(100, description="Max text tokens per sentence."),
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sentences_bucket_max_size: int = Query(4, description="Sentences bucket max size."),
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do_sample: bool = Query(True, description="Enable sampling."),
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top_p: float = Query(0.8, description="Top P for sampling."),
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top_k: int = Query(30, description="Top K for sampling."),
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temperature: float = Query(1.0, description="Temperature for sampling."),
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length_penalty: float = Query(0.0, description="Length penalty."),
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num_beams: int = Query(3, description="Number of beams for beam search."),
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repetition_penalty: float = Query(10.0, description="Repetition penalty."),
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max_mel_tokens: int = Query(600, description="Max mel tokens to generate.")
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):
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if tts_model is None:
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raise HTTPException(status_code=503, detail="TTS model is not loaded or failed to load.")
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temp_dir = tempfile.mkdtemp()
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actual_audio_prompt_to_use = "" # This will be the path on the server filesystem
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try:
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if server_audio_prompt_path:
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print(f"Client specified server_audio_prompt_path: {server_audio_prompt_path}")
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# Auto-complete .wav extension if missing
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if server_audio_prompt_path and not server_audio_prompt_path.lower().endswith(".wav"):
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print(f"server_audio_prompt_path '{server_audio_prompt_path}' does not end with .wav, appending it.")
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server_audio_prompt_path += ".wav"
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try:
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# Sanitize and resolve the user-provided path against the allowed base directory
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safe_path = get_safe_prompt_path(ALLOWED_PROMPT_BASE_DIR, server_audio_prompt_path)
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if os.path.isfile(safe_path):
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actual_audio_prompt_to_use = safe_path
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print(f"Using user-specified server prompt: {actual_audio_prompt_to_use}")
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else:
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await cleanup_temp_dir(temp_dir)
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raise HTTPException(status_code=404, detail=f"Specified server audio prompt not found or not a file: {safe_path} (original: {server_audio_prompt_path})")
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except ValueError as ve: # From get_safe_prompt_path for security violations
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await cleanup_temp_dir(temp_dir)
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raise HTTPException(status_code=400, detail=f"Invalid server_audio_prompt_path: {str(ve)}")
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else:
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print(f"Using default server audio prompt: {DEFAULT_SERVER_AUDIO_PROMPT_PATH}")
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if not os.path.isfile(DEFAULT_SERVER_AUDIO_PROMPT_PATH):
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await cleanup_temp_dir(temp_dir)
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raise HTTPException(status_code=500, detail=f"Default server audio prompt file not found: {DEFAULT_SERVER_AUDIO_PROMPT_PATH}. Please configure the server.")
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actual_audio_prompt_to_use = DEFAULT_SERVER_AUDIO_PROMPT_PATH
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# Copy the chosen prompt (either user-specified or default) to the temp_dir.
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# This keeps the subsequent logic (model interaction, cleanup) consistent.
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# It also means the original prompt files are not directly modified or locked.
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prompt_filename_for_temp = os.path.basename(actual_audio_prompt_to_use)
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temp_audio_prompt_path_in_job_dir = os.path.join(temp_dir, prompt_filename_for_temp)
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try:
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shutil.copy2(actual_audio_prompt_to_use, temp_audio_prompt_path_in_job_dir)
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except Exception as e:
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await cleanup_temp_dir(temp_dir)
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raise HTTPException(status_code=500, detail=f"Failed to copy audio prompt to temporary workspace: {str(e)}")
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output_filename = f"generated_speech_{int(time.time())}.wav"
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temp_output_path = os.path.join(temp_dir, output_filename)
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print(f"Synthesizing for text: '{text[:50]}...' with prompt (in temp): {temp_audio_prompt_path_in_job_dir}")
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print(f"Output will be saved to: {temp_output_path}")
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generation_kwargs = {
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"do_sample": do_sample,
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"top_p": top_p,
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"top_k": top_k,
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"temperature": temperature,
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"length_penalty": length_penalty,
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"num_beams": num_beams,
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"repetition_penalty": repetition_penalty,
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"max_mel_tokens": max_mel_tokens,
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}
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start_infer_time = time.time()
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returned_output_path = tts_model.infer_fast(
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audio_prompt=temp_audio_prompt_path_in_job_dir, # Use the copied prompt in temp dir
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text=text,
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output_path=temp_output_path,
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verbose=verbose,
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max_text_tokens_per_sentence=max_text_tokens_per_sentence,
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sentences_bucket_max_size=sentences_bucket_max_size,
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**generation_kwargs
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)
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infer_time = time.time() - start_infer_time
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print(f"Inference completed in {infer_time:.2f} seconds. Expected output: {temp_output_path}, Returned path: {returned_output_path}")
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if not os.path.exists(temp_output_path):
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print(f"ERROR: Output file {temp_output_path} was NOT found after inference call.")
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background_tasks.add_task(cleanup_temp_dir, temp_dir)
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raise HTTPException(status_code=500, detail="TTS synthesis failed to produce an output file.")
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print(f"Output file {temp_output_path} confirmed to exist.")
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background_tasks.add_task(cleanup_temp_dir, temp_dir)
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return FileResponse(
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path=temp_output_path,
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media_type="audio/wav",
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filename="synthesized_audio.wav"
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)
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except HTTPException:
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raise
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except Exception as e:
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print(f"An unexpected error occurred during synthesis: {e}")
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import traceback
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traceback.print_exc()
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if 'temp_dir' in locals() and os.path.exists(temp_dir):
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background_tasks.add_task(cleanup_temp_dir, temp_dir)
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raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(e)}")
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@app.get("/")
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async def read_root():
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return {"message": "IndexTTS FastAPI service is running. Use the /synthesize/ endpoint (GET or POST) to generate audio."}
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if __name__ == "__main__":
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if not os.path.isfile(DEFAULT_SERVER_AUDIO_PROMPT_PATH):
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print(f"CRITICAL WARNING: Default server audio prompt at '{DEFAULT_SERVER_AUDIO_PROMPT_PATH}' not found!")
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else:
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print(f"Default server audio prompt: {os.path.abspath(DEFAULT_SERVER_AUDIO_PROMPT_PATH)}")
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if not os.path.isdir(ALLOWED_PROMPT_BASE_DIR):
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print(f"WARNING: ALLOWED_PROMPT_BASE_DIR '{ALLOWED_PROMPT_BASE_DIR}' does not exist. Consider creating it or prompts specified by 'server_audio_prompt_path' may not be found.")
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else:
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print(f"User-specified prompts should be relative to: {os.path.abspath(ALLOWED_PROMPT_BASE_DIR)}")
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print(f"Attempting to use MODEL_DIR: {os.path.abspath(MODEL_DIR)}")
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print(f"Attempting to use MODEL_CFG_PATH: {os.path.abspath(MODEL_CFG_PATH)}")
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if not os.path.isdir(MODEL_DIR):
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print(f"ERROR: MODEL_DIR '{MODEL_DIR}' not found. Please check the path.")
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if not os.path.isfile(MODEL_CFG_PATH):
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print(f"ERROR: MODEL_CFG_PATH '{MODEL_CFG_PATH}' not found. Please check the path.")
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uvicorn.run(app, host="0.0.0.0", port=7899)
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