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+# 使用一个官方的、轻量的Python基础镜像
+FROM python:3.9-slim
+
+# --- 按照您的要求进行修改 ---
+# 在容器构建阶段，以root用户身份创建 /nltk_data 目录，并赋予 777 权限。
+# mkdir -p: 如果目录已存在也不会报错
+# chmod 777: 给予所有用户（包括之后运行应用的非root用户）读、写、执行的权限
+RUN mkdir -p /nltk_data && chmod 777 /nltk_data
+
+# 在容器内创建一个工作目录
+WORKDIR /code
+
+# 复制依赖文件到工作目录
+COPY ./requirements.txt /code/requirements.txt
+
+# 安装依赖。--no-cache-dir 参数可以减小镜像体积
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# 将当前目录下的所有文件（主要是app.py）复制到工作目录
+COPY ./ /code/
+
+# 暴露端口。Hugging Face Spaces 默认使用 7860 端口
+EXPOSE 7860
+
+# 容器启动时要执行的命令
+# 运行uvicorn服务器，监听所有网络接口（0.0.0.0），端口为7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import asyncio
+import io
+import nltk
+import logging
+import re
+import tempfile
+from fastapi import FastAPI, HTTPException, Response, Request
+from pydantic import BaseModel, Field
+from typing import List, Dict, Any
+
+import httpx
+from pydub import AudioSegment
+
+# --- 1. 配置 ---
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+WORKER_URLS = [
+    "https://snsbhg-1111.hf.space/tts", "https://snsbhg-111102.hf.space/tts",
+    "https://snsbhg-111103.hf.space/tts", "https://snsbhg-111104.hf.space/tts",
+    "https://snsbhg-111105.hf.space/tts", "https://11edx-111106.hf.space/tts",
+    "https://11edx-111107.hf.space/tts", "https://11edx-111108.hf.space/tts",
+    "https://11edx-111109.hf.space/tts", "https://11edx-111110.hf.space/tts",
+    "https://11edx-111111.hf.space/tts", "https://11edx-111112.hf.space/tts",
+    "https://11edx-111113.hf.space/tts", "https://11edx-111114.hf.space/tts",
+    "https://11edx-111115.hf.space/tts", "https://11edx-111116.hf.space/tts",
+    "https://11edx-111117.hf.space/tts", "https://11edx-111118.hf.space/tts",
+    "https://11edx-111119.hf.space/tts", "https://11edx-111120.hf.space/tts",
+]
+MAX_CONCURRENT_REQUESTS = 8
+
+# --- 2. Pydantic模型 ---
+class WeightsPaths(BaseModel):
+    sovits_path: str; gpt_path: str
+
+# --- START OF NEW SECTION: OpenAI Audio API Models ---
+class OpenAIAudioRequest(BaseModel):
+    model: str = "tts-1" # 兼容字段
+    input: str # 这是我们要合成的文本
+    voice: str = "alloy" # 兼容字段, 将来可用于选择不同的参考音频
+    response_format: str = Field(default="wav", alias="response_format")
+    speed: float = 1.0 # 兼容字段
+
+    class Config:
+        extra = "allow"
+        populate_by_name = True
+# --- END OF NEW SECTION ---
+
+
+# --- 3. 初始化和辅助函数 (所有这部分代码不变) ---
+app = FastAPI()
+client = httpx.AsyncClient(timeout=180.0, http2=True)
+try: nltk.data.find('tokenizers/punkt')
+except LookupError:
+    logging.info("NLTK 'punkt' tokenizer not found. Downloading..."); nltk.download('punkt', quiet=True); logging.info("'punkt' downloaded successfully.")
+
+def split_by_punctuation(text: str, max_len: int = 150):
+    # (此函数不变)
+    fragments = re.split(r'([,.:;!?。，、；：！？．…【】])', text); sentences = []
+    temp_frag = ""
+    for frag in fragments:
+        if frag in ",.:;!?。，、；：！？．…【】": temp_frag += frag; sentences.append(temp_frag); temp_frag = ""
+        else:
+            if temp_frag: sentences.append(temp_frag)
+            temp_frag = frag
+    if temp_frag: sentences.append(temp_frag)
+    chunks, current_chunk = [], ""
+    for sentence in sentences:
+        sentence = sentence.strip()
+        if not sentence: continue
+        if not current_chunk or len(current_chunk) + len(sentence) <= max_len: current_chunk += (" " if current_chunk else "") + sentence
+        else: chunks.append(current_chunk); current_chunk = sentence
+    while len(current_chunk) > max_len:
+        split_pos = -1
+        for punc in "，；。！？…【】 ":
+            pos = current_chunk.rfind(punc, 0, max_len)
+            if pos > -1: split_pos = pos; break
+        if split_pos == -1: split_pos = max_len -1
+        chunks.append(current_chunk[:split_pos+1]); current_chunk = current_chunk[split_pos+1:]
+    if current_chunk: chunks.append(current_chunk)
+    return [c.strip() for c in chunks if c.strip()]
+
+def sanitize_and_default_params(params: dict) -> dict:
+    # (此函数不变)
+    allowed_keys = {"text", "text_lang", "ref_audio_path", "prompt_lang", "prompt_text", "media_type", "streaming_mode"}
+    default_values = {"text_lang": "zh", "prompt_lang": "zh", "media_type": "wav", "streaming_mode": False}
+    sanitized_params = {key: params[key] for key in allowed_keys if key in params}
+    for key, default_value in default_values.items():
+        if key not in sanitized_params: sanitized_params[key] = default_value
+    required_keys = {"text", "ref_audio_path", "prompt_text"}
+    missing_keys = required_keys - set(sanitized_params.keys())
+    if missing_keys: raise ValueError(f"Missing required fields after sanitization: {', '.join(missing_keys)}")
+    logging.info(f"Sanitized params. Final keys sent to worker: {list(sanitized_params.keys())}")
+    return sanitized_params
+
+async def send_task_to_worker(worker_url, payload, index, semaphore):
+    # (此函数不变)
+    async with semaphore:
+        try:
+            final_payload = sanitize_and_default_params(payload)
+            logging.info(f"Task {index}: Sending chunk to {worker_url} (text length: {len(final_payload.get('text', ''))})")
+            response = await client.post(worker_url, json=final_payload, timeout=180.0)
+            if response.status_code == 200: logging.info(f"Task {index}: Successfully received audio data from {worker_url}"); return index, response.content
+            error_body = await response.text(); logging.error(f"Task {index}: Worker {worker_url} returned error status {response.status_code}. Body: {error_body}"); return index, None
+        except ValueError as ve: logging.error(f"Task {index}: Parameter validation failed before sending. Error: {ve}"); return index, None
+        except Exception as e: logging.error(f"Task {index}: Request to {worker_url} failed: {e}"); return index, None
+
+async def process_tts_request(request_data: dict):
+    # (此函数不变)
+    text = request_data.get("text", "")
+    logging.info(f"Processing new TTS request. Full text length: {len(text)}")
+    chunks = split_by_punctuation(text)
+    if not chunks: raise HTTPException(status_code=400, detail="Input text is empty or resulted in no chunks.")
+    if len(chunks) <= 1:
+        logging.info("Text resulted in one chunk. Forwarding to a single worker...")
+        payload = request_data; payload["text"] = chunks[0] if chunks else ""
+        semaphore = asyncio.Semaphore(1)
+        _, audio_bytes = await send_task_to_worker(WORKER_URLS[0], payload, 0, semaphore)
+        if audio_bytes: logging.info("Single chunk processed successfully. Returning audio."); return Response(content=audio_bytes, media_type=f"audio/{request_data.get('media_type', 'wav')}")
+        raise HTTPException(status_code=500, detail="Downstream worker failed to process the request.")
+    logging.info(f"Text split into {len(chunks)} chunks. Starting parallel processing with a limit of {MAX_CONCURRENT_REQUESTS} requests at a time...")
+    semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    tasks = []
+    base_payload = request_data; num_workers = len(WORKER_URLS)
+    for i, chunk in enumerate(chunks):
+        task_payload = {**base_payload, "text": chunk}; worker_url = WORKER_URLS[i % num_workers]
+        tasks.append(send_task_to_worker(worker_url, task_payload, i, semaphore))
+    results = await asyncio.gather(*tasks); results.sort(key=lambda x: x[0])
+    valid_audio_bytes_list = [audio for _, audio in results if audio]
+    if not valid_audio_bytes_list: raise HTTPException(status_code=500, detail="All downstream worker tasks failed, could not generate audio.")
+    logging.info(f"Starting to splice {len(valid_audio_bytes_list)} valid audio segments using a temporary file to save memory...")
+    with tempfile.NamedTemporaryFile(delete=True, suffix=".wav") as temp_f:
+        media_type = request_data.get("media_type", "wav")
+        first_segment = AudioSegment.from_file(io.BytesIO(valid_audio_bytes_list[0]), format=media_type)
+        first_segment.export(temp_f.name, format=media_type)
+        for i, audio_bytes in enumerate(valid_audio_bytes_list[1:]):
+            try:
+                combined_audio = AudioSegment.from_file(temp_f.name, format=media_type)
+                next_segment = AudioSegment.from_file(io.BytesIO(audio_bytes), format=media_type)
+                (combined_audio + next_segment).export(temp_f.name, format=media_type)
+            except Exception as e: logging.warning(f"Splicing failed for audio segment {i+1}. Skipping. Error: {e}")
+        temp_f.seek(0)
+        final_audio_content = temp_f.read()
+    logging.info("Audio splicing complete. Returning final audio file to user.")
+    return Response(content=final_audio_content, media_type=f"audio/{media_type}")
+
+
+# --- 4. API端点 ---
+@app.get("/")
+def read_root(): return {"status": "Master TTS Accelerator is running"}
+
+@app.post("/update-all-workers")
+async def update_all_workers_endpoint(paths: WeightsPaths):
+    # (此函数不变)
+    logging.info(f"Received request to update all workers. SOVITS='{paths.sovits_path}', GPT='{paths.gpt_path}'")
+    async def send_update_to_worker(worker_url, sovits_path, gpt_path):
+        base_url = worker_url.replace("/tts", "");
+        try:
+            sovits_resp = await client.get(f"{base_url}/set_sovits_weights", params={"weights_path": sovits_path}, timeout=60); sovits_resp.raise_for_status()
+            gpt_resp = await client.get(f"{base_url}/set_gpt_weights", params={"weights_path": gpt_path}, timeout=60); gpt_resp.raise_for_status()
+            return base_url, "Success"
+        except Exception as e: return base_url, f"Failed: {e}"
+    tasks = [send_update_to_worker(url, paths.sovits_path, paths.gpt_path) for url in WORKER_URLS]
+    results = await asyncio.gather(*tasks); status_report = {url: status for url, status in results}
+    return {"message": "Update commands sent to all workers.", "status_report": status_report}
+
+@app.post("/tts")
+async def tts_post_endpoint(request: Request):
+    # (此函数不变)
+    logging.info("--- HIT POST /tts endpoint (Sanitization Mode) ---")
+    try:
+        request_data = await request.json()
+        logging.info(f"Received native JSON payload with keys: {list(request_data.keys())}")
+        return await process_tts_request(request_data)
+    except Exception as e:
+        logging.error(f"Failed to parse JSON body or process request: {e}")
+        raise HTTPException(status_code=400, detail=f"Invalid JSON body or processing error: {e}")
+
+# --- START OF NEW SECTION: OpenAI Audio API Endpoint ---
+@app.post("/v1/audio/speech")
+async def openai_audio_speech_endpoint(request: OpenAIAudioRequest):
+    logging.info("--- HIT POST /v1/audio/speech endpoint (OpenAI Audio API Mode) ---")
+
+    # 1. 从请求中提取核心文本
+    text_to_speak = request.input
+    logging.info(f"Extracted text from OpenAI audio format: '{text_to_speak}'")
+    
+    # 2. TODO (可选): 将来可以根据 request.voice 的值选择不同的参考音频
+    # 例如: if request.voice == "shimmer": ref_audio = "path/to/shimmer.wav"
+
+    # 3. 构建默认的、完整的TTS参数包
+    # 这些是您的服务必需的、但OpenAI格式中没有的参数
+    tts_params = {
+        "text": text_to_speak,
+        "ref_audio_path": "/app/reference_audio/ref_shantianliang_1.wav", # 使用默认参考音频
+        "prompt_text": "这是一条参考音频，将此音频拖入参考内在添加文本即可合成音色", # 使用默认提示文本
+        "media_type": request.response_format, # 尊重客户端请求的格式
+        # 其他参数将由 sanitize_and_default_params 函数自动补齐
+    }
+
+    # 4. 调用核心处理逻辑
+    try:
+        return await process_tts_request(tts_params)
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        logging.error(f"An unexpected error occurred while processing OpenAI audio request: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error during TTS processing.")
+# --- END OF NEW SECTION ---

requirements.txt

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+fastapi
+uvicorn
+httpx[http2]
+pydub
+pydantic
+nltk