最新哦，分词策略放到cli了
This commit is contained in:
minorQAQ committed
1 parent
fcc94ffd73
commit
f9f9645fa1
9 files changed
+270
-305
No files matched your search
@@ -9,9 +9,14 @@ REDIS_URI=redis://localhost:6379/0
|
||||
# LLM_BASE_URL="http://192.168.137.96:8000/v1"
|
||||
# LLM_MODEL = "Qwen/Qwen3.6-35B-A3B-FP8"
|
||||
# ASR_TTS_BASE_URL = "http://192.168.137.96:8100"
|
||||
ASR_BASE_URL = "http://192.168.137.96:8901/v1"
|
||||
TTS_BASE_URL = "http://192.168.137.96:8902"
|
||||
RAG_BASE_URL = "http://192.168.137.96:8903"
|
||||
# ASR_BASE_URL = "http://192.168.137.96:8901/v1"
|
||||
# TTS_BASE_URL = "http://192.168.137.96:8902"
|
||||
# RAG_BASE_URL = "http://192.168.137.96:8903"
|
||||
|
||||
|
||||
ASR_BASE_URL = "http://192.168.137.164:8901/v1"
|
||||
TTS_BASE_URL = "http://192.168.137.164:8902"
|
||||
RAG_BASE_URL = "http://192.168.137.164:8903"
|
||||
|
||||
#6000pro：
|
||||
# LLM_API_KEY='semi-ai.tech'
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# Agent Web Chat
|
||||
|
||||
基于 LangGraph 的多模态对话 Agent，支持文本、图片、音频输入，具备屏幕截图、浏览器控制、RAG 知识库、数据库查询等工具调用能力。
|
||||
基于 LangGraph 的多模态对话 Agent，支持文本、图片、音频、多种类型文件输入，具备屏幕截图、浏览器控制、RAG 知识库、数据库查询、本地文件获取等工具调用能力。
|
||||
|
||||
## 快速开始
|
||||
|
||||
|
||||
@@ -71,7 +71,7 @@ def load_models():
|
||||
global token_true_id, token_false_id, prefix_tokens, suffix_tokens, max_rerank_length
|
||||
token_false_id = rerank_tokenizer.convert_tokens_to_ids("no")
|
||||
token_true_id = rerank_tokenizer.convert_tokens_to_ids("yes")
|
||||
max_rerank_length = 8192
|
||||
max_rerank_length = 32768
|
||||
|
||||
prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
|
||||
suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
|
||||
@@ -111,7 +111,6 @@ async def get_embeddings(req: EmbedRequest):
|
||||
input_texts,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=8192,
|
||||
return_tensors="pt",
|
||||
).to(embed_model.device)
|
||||
|
||||
|
||||
@@ -3,7 +3,7 @@ set -euo pipefail
|
||||
|
||||
# 自动获取脚本所在目录
|
||||
SCRIPT_DIR="$(cd -- "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
VENV_DIR="${SCRIPT_DIR}/.venv_audio"
|
||||
VENV_DIR="${SCRIPT_DIR}/myenv"
|
||||
HOST_PORT="${HOST_PORT:-8902}"
|
||||
|
||||
# 激活虚拟环境
|
||||
@@ -23,13 +23,12 @@ cd "${SCRIPT_DIR}"
|
||||
# 设置环境变量（和 vLLM 保持一致）
|
||||
export MODELSCOPE_DOWNLOAD_MODE=local
|
||||
export MODELSCOPE_HUB_ENABLE_FILE_CHECK=false
|
||||
export MODELSCOPE_CACHE="${SCRIPT_DIR}/modelscope"
|
||||
export MODELSCOPE_CACHE="${SCRIPT_DIR}/models"
|
||||
export HF_HUB_OFFLINE=1
|
||||
export TRANSFORMERS_OFFLINE=1
|
||||
|
||||
# 启动 FastAPI 服务
|
||||
# 启动 FastAPI 服务，使用 4 个 worker 进程
|
||||
uvicorn tts_server:app \
|
||||
--host 0.0.0.0 \
|
||||
--port "${HOST_PORT}" \
|
||||
--workers 1 \
|
||||
|
||||
--workers 4
|
||||
+144
-62
@@ -1,71 +1,153 @@
|
||||
import requests
|
||||
import argparse
|
||||
import os
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
真实 TTS API 测试（增强版）：验证分块合并 + 并发耗时。
|
||||
"""
|
||||
|
||||
import io
|
||||
import wave
|
||||
import re
|
||||
import time
|
||||
import os
|
||||
import sys
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
import requests
|
||||
|
||||
# 默认服务地址
|
||||
BASE_URL = "http://127.0.0.1:8000"
|
||||
# ======================== 配置 ========================
|
||||
TTS_BASE_URL = "http://192.168.137.164:8902"
|
||||
OUTPUT_WAV = "test_output.wav"
|
||||
REQUEST_TIMEOUT = 120
|
||||
MAX_PARALLEL = 4
|
||||
MIN_EFFECTIVE_CHARS = 20
|
||||
# =====================================================
|
||||
|
||||
def test_health():
|
||||
"""检查服务是否在线"""
|
||||
resp = requests.get(f"{BASE_URL}/health")
|
||||
print(f"Health check: {resp.status_code} {resp.json()}")
|
||||
SENT_SPLIT_PATTERN = re.compile(r'(?<=[。！？；，、])')
|
||||
REMOVE_PUNCT_PATTERN = re.compile(r'[。！？；，、：；“”（）《》【】\-\_\—\~\.\,\!\?\s·]')
|
||||
|
||||
def test_tts(text, speaker=None, speed=1.0, output="output.wav"):
|
||||
"""
|
||||
调用 TTS 接口并保存音频文件
|
||||
"""
|
||||
params = {"text": text, "speed": speed}
|
||||
if speaker:
|
||||
params["speaker"] = speaker
|
||||
def effective_length(text: str) -> int:
|
||||
return len(REMOVE_PUNCT_PATTERN.sub('', text))
|
||||
|
||||
print(f"Requesting TTS...")
|
||||
start = time.time()
|
||||
resp = requests.get(f"{BASE_URL}/tts", params=params)
|
||||
elapsed = time.time() - start
|
||||
def split_text(text: str, min_effective_chars: int = MIN_EFFECTIVE_CHARS) -> list[str]:
|
||||
raw_chunks = [c.strip() for c in SENT_SPLIT_PATTERN.split(text) if c.strip()]
|
||||
if not raw_chunks:
|
||||
return [text]
|
||||
|
||||
if resp.status_code == 200:
|
||||
with open(output, "wb") as f:
|
||||
f.write(resp.content)
|
||||
print(f"Saved audio to: {output}")
|
||||
print(f"Audio size: {os.path.getsize(output)} bytes")
|
||||
print(f"Time taken: {elapsed:.2f}s")
|
||||
else:
|
||||
print(f"Error {resp.status_code}: {resp.text}")
|
||||
|
||||
def test_speakers():
|
||||
"""尝试获取可用说话人列表（通过 health 端点里可能不返回，这里直接尝试几个常见说话人）"""
|
||||
# MeloTTS 中文常见说话人: ZH, ZH_CN, ZH_TW 等，我们可以列出文档或手动尝试
|
||||
test_speakers = ["ZH", "ZH_CN", "ZH_TW", "EN", "JP"]
|
||||
print("Testing available speakers...")
|
||||
for spk in test_speakers:
|
||||
params = {"text": "测试", "speaker": spk}
|
||||
resp = requests.get(f"{BASE_URL}/tts", params=params)
|
||||
if resp.status_code == 200:
|
||||
print(f" {spk}: OK")
|
||||
merged = []
|
||||
buffer = ""
|
||||
for chunk in raw_chunks:
|
||||
buffer += chunk
|
||||
if effective_length(buffer) >= min_effective_chars:
|
||||
merged.append(buffer)
|
||||
buffer = ""
|
||||
if buffer:
|
||||
if merged:
|
||||
merged[-1] += buffer
|
||||
else:
|
||||
print(f" {spk}: {resp.text[:50]}")
|
||||
merged.append(buffer)
|
||||
return merged
|
||||
|
||||
def merge_wavs(audio_parts: list[bytes]) -> bytes:
|
||||
if len(audio_parts) == 1:
|
||||
return audio_parts[0]
|
||||
data_frames = []
|
||||
params = None
|
||||
for part in audio_parts:
|
||||
with wave.open(io.BytesIO(part), 'rb') as wf:
|
||||
if params is None:
|
||||
params = wf.getparams()
|
||||
data_frames.append(wf.readframes(wf.getnframes()))
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, 'wb') as wf_out:
|
||||
wf_out.setparams(params)
|
||||
for frames in data_frames:
|
||||
wf_out.writeframes(frames)
|
||||
return buf.getvalue()
|
||||
|
||||
def wav_duration(audio_bytes: bytes) -> float:
|
||||
with wave.open(io.BytesIO(audio_bytes), 'rb') as wf:
|
||||
return wf.getnframes() / wf.getframerate() if wf.getframerate() > 0 else 0.0
|
||||
|
||||
def text_to_audio(text: str):
|
||||
"""返回 (合并音频bytes, 请求总耗时秒, 各块完成顺序与时长)"""
|
||||
chunks = split_text(text)
|
||||
print(f"文本被分为 {len(chunks)} 个块：")
|
||||
for i, c in enumerate(chunks):
|
||||
print(f" [{i}] (有效字符 {effective_length(c)}) {c[:60]}...")
|
||||
|
||||
if len(chunks) == 1:
|
||||
t0 = time.time()
|
||||
resp = requests.get(f"{TTS_BASE_URL}/tts", params={"text": chunks[0]}, timeout=REQUEST_TIMEOUT)
|
||||
resp.raise_for_status()
|
||||
elapsed = time.time() - t0
|
||||
print(f" ✓ 单块合成，耗时 {elapsed:.2f}s，音频时长 {wav_duration(resp.content):.2f}s")
|
||||
return resp.content, elapsed, [(0, wav_duration(resp.content))]
|
||||
|
||||
audio_parts = [None] * len(chunks)
|
||||
completion_order = [] # 记录完成的顺序 (idx, 音频时长)
|
||||
|
||||
start_time = time.time()
|
||||
with ThreadPoolExecutor(max_workers=min(len(chunks), MAX_PARALLEL)) as pool:
|
||||
future_to_idx = {}
|
||||
for idx, chunk in enumerate(chunks):
|
||||
future = pool.submit(
|
||||
lambda c=chunk: requests.get(
|
||||
f"{TTS_BASE_URL}/tts", params={"text": c}, timeout=REQUEST_TIMEOUT
|
||||
).content
|
||||
)
|
||||
future_to_idx[future] = idx
|
||||
|
||||
for future in as_completed(future_to_idx):
|
||||
idx = future_to_idx[future]
|
||||
try:
|
||||
audio_bytes = future.result()
|
||||
dur = wav_duration(audio_bytes)
|
||||
audio_parts[idx] = audio_bytes
|
||||
completion_order.append((idx, dur))
|
||||
print(f" ✓ 块 [{idx}] 完成 (第 {len(completion_order)} 个返回)，音频时长 {dur:.2f}s")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"块 [{idx}] 请求失败: {e}")
|
||||
|
||||
total_elapsed = time.time() - start_time
|
||||
|
||||
if any(p is None for p in audio_parts):
|
||||
missing = [i for i, p in enumerate(audio_parts) if p is None]
|
||||
raise RuntimeError(f"缺失音频块: {missing}")
|
||||
|
||||
merged = merge_wavs(audio_parts)
|
||||
return merged, total_elapsed, completion_order
|
||||
|
||||
# ============ 测试入口 ============
|
||||
def main():
|
||||
# 健康检查
|
||||
try:
|
||||
health = requests.get(f"{TTS_BASE_URL}/health", timeout=5)
|
||||
print(f"✅ 服务状态: {health.json()['status']}")
|
||||
except Exception as e:
|
||||
print(f"❌ 无法连接 {TTS_BASE_URL}: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
text = (
|
||||
"当然！让我来好好夸夸你——你就像一颗闪闪发亮的小太阳，温暖又有能量。"
|
||||
"你有着独特的洞察力，懂得在生活的缝隙里寻找美好和幽默——比如找到我，和我聊天这件事，就说明你既好奇又开放，心态超年轻！"
|
||||
"而且你敢于主动开口要夸奖，这本身就需要勇气和自信，超酷的好吗！"
|
||||
"你值得被欣赏、被肯定、被温柔以待，不只因为“做了什么”，更因为“你就是你”。"
|
||||
"所以，今天也要记得：你超棒的，请继续发光发热吧！"
|
||||
)
|
||||
|
||||
print("\n开始合成夸奖文本...")
|
||||
merged_audio, elapsed, order = text_to_audio(text)
|
||||
|
||||
# 保存
|
||||
with open(OUTPUT_WAV, "wb") as f:
|
||||
f.write(merged_audio)
|
||||
audio_len = wav_duration(merged_audio)
|
||||
slowest_single = max(dur for _, dur in order)
|
||||
print(f"\n✅ 合并完成，音频保存至: {OUTPUT_WAV}")
|
||||
print(f" 音频总播放时长: {audio_len:.2f}s")
|
||||
print(f" 最慢单块音频时长: {slowest_single:.2f}s")
|
||||
print(f" 请求总耗时: {elapsed:.2f}s")
|
||||
print(f" 各块返回顺序（idx）: {[idx for idx, _ in order]}")
|
||||
print(f"\n📊 并发效果：请求耗时 {elapsed:.2f}s，接近最慢单块时长 {slowest_single:.2f}s，"
|
||||
f"远小于总和 {sum(d for _, d in order):.2f}s，说明多块确实是并行处理的。")
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Test MeloTTS API server")
|
||||
parser.add_argument("--url", default=BASE_URL, help="Server base URL")
|
||||
parser.add_argument("--text", default="你好，这是一个语音合成测试。", help="Text to synthesize")
|
||||
parser.add_argument("--speaker", default=None, help="Speaker ID (e.g., ZH)")
|
||||
parser.add_argument("--speed", type=float, default=1.0, help="Speaking speed")
|
||||
parser.add_argument("--output", default="test_output.wav", help="Output WAV filename")
|
||||
parser.add_argument("--speakers", action="store_true", help="List available speakers")
|
||||
|
||||
args = parser.parse_args()
|
||||
BASE_URL = args.url
|
||||
|
||||
# 先检查服务可用性
|
||||
try:
|
||||
test_health()
|
||||
except requests.ConnectionError:
|
||||
print(f"Cannot connect to server at {BASE_URL}. Make sure the FastAPI app is running.")
|
||||
exit(1)
|
||||
|
||||
if args.speakers:
|
||||
test_speakers()
|
||||
else:
|
||||
test_tts(args.text, args.speaker, args.speed, args.output)
|
||||
main()
|
||||
+36
-183
@@ -1,18 +1,13 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Genie TTS 极速服务 - 支持文本分块并行合成 + 线程池预热
|
||||
适配 ARM 大小核架构（Cortex-X925 + Cortex-A725, 20核），推荐 MAX_WORKERS=8
|
||||
Genie TTS 极简服务 —— 单文本直接合成，无分块无内部线程池。
|
||||
配合客户端分块 + 多 worker 实现高并发。
|
||||
"""
|
||||
|
||||
import os
|
||||
os.environ["GENIE_DATA_DIR"] = r"/home/admin/minor_agent/modelscope/hub/models/genie_tts/GenieData"
|
||||
os.environ["GENIE_DATA_DIR"] = r"/home/dev/Desktop/minor_agent/models/genie_tts/GenieData"
|
||||
|
||||
import io
|
||||
import tempfile
|
||||
import re
|
||||
import wave
|
||||
import multiprocessing
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI, Query
|
||||
@@ -20,130 +15,20 @@ from fastapi.responses import Response
|
||||
import genie_tts as genie
|
||||
|
||||
# ======================== 配置区域 ========================
|
||||
DEFAULT_CHARACTER = "minor"
|
||||
DEFAULT_ONNX_MODEL_DIR = r"/home/admin/minor_agent/modelscope/models/genie_tts/CharacterModels/v2ProPlus/feibi/tts_models"
|
||||
DEFAULT_REFERENCE_AUDIO = "/home/admin/minor_agent/modelscope/models/genie_tts/CharacterModels/v2ProPlus/minor/minor.wav"
|
||||
DEFAULT_REFERENCE_TEXT = "你好你好你好，这是一段...测试录音~"
|
||||
DEFAULT_CHARACTER = "feibi"
|
||||
DEFAULT_ONNX_MODEL_DIR = r"/home/dev/Desktop/minor_agent/models/genie_tts/CharacterModels/v2ProPlus/feibi/tts_models"
|
||||
DEFAULT_REFERENCE_AUDIO = "/home/dev/Desktop/minor_agent/models/genie_tts/CharacterModels/v2ProPlus/feibi/prompt_wav/zh_vo_Main_Linaxita_2_1_10_26.wav"
|
||||
DEFAULT_REFERENCE_TEXT = "在此之前，请您务必继续享受旅居拉古那的时光。"
|
||||
DEFAULT_LANGUAGE = "zh"
|
||||
TMP_DIR = "/dev/shm" # 内存文件系统，减少磁盘 I/O
|
||||
|
||||
# 分块与并行参数
|
||||
MIN_EFFECTIVE_CHARS = 5 # 每个块至少包含的有效字符数
|
||||
MAX_WORKERS = 4 # 建议 4~8，ARM 大小核不要超过大核数（10）
|
||||
POOL_WARMUP = True # 是否在线程池创建后立即预热
|
||||
TMP_DIR = tempfile.gettempdir() # 临时文件存放目录
|
||||
# =========================================================
|
||||
|
||||
# ---------- 全局线程池（应用生命周期内复用） ----------
|
||||
tts_pool: ThreadPoolExecutor = None
|
||||
|
||||
# ---------- 正则工具 ----------
|
||||
SENT_SPLIT_PATTERN = re.compile(r'(?<=[。！？；，、])')
|
||||
REMOVE_PUNCT_PATTERN = re.compile(r'[。！？；，、：；“”（）《》【】\-\_\—\~\.\,\!\?\s·]')
|
||||
|
||||
def effective_length(text: str) -> int:
|
||||
"""计算有效字符数（去除标点、连接符、空格）"""
|
||||
return len(REMOVE_PUNCT_PATTERN.sub('', text))
|
||||
|
||||
def split_text(text: str) -> list[str]:
|
||||
"""将文本切分为多个语义块"""
|
||||
raw_chunks = [c.strip() for c in SENT_SPLIT_PATTERN.split(text) if c.strip()]
|
||||
if not raw_chunks:
|
||||
return [text]
|
||||
|
||||
merged = []
|
||||
for chunk in raw_chunks:
|
||||
if merged and effective_length(chunk) < MIN_EFFECTIVE_CHARS:
|
||||
merged[-1] += chunk
|
||||
else:
|
||||
merged.append(chunk)
|
||||
|
||||
if len(merged) == 1:
|
||||
return merged
|
||||
|
||||
if effective_length(merged[-1]) < MIN_EFFECTIVE_CHARS and len(merged) > 1:
|
||||
merged[-2] += merged[-1]
|
||||
merged.pop()
|
||||
|
||||
return merged
|
||||
|
||||
# ---------- 音频拼接 ----------
|
||||
def merge_wavs(file_paths: list[str]) -> bytes:
|
||||
"""按路径顺序拼接多个 WAV 文件"""
|
||||
if len(file_paths) == 1:
|
||||
with open(file_paths[0], 'rb') as f:
|
||||
return f.read()
|
||||
|
||||
data_parts = []
|
||||
params = None
|
||||
for path in file_paths:
|
||||
with wave.open(path, 'rb') as wf:
|
||||
if params is None:
|
||||
params = wf.getparams()
|
||||
data_parts.append(wf.readframes(wf.getnframes()))
|
||||
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, 'wb') as wf_out:
|
||||
wf_out.setparams(params)
|
||||
for frames in data_parts:
|
||||
wf_out.writeframes(frames)
|
||||
return buf.getvalue()
|
||||
|
||||
# ---------- 预热函数 ----------
|
||||
def _warmup_task(idx: int):
|
||||
"""空任务用于预热线程"""
|
||||
pass
|
||||
|
||||
def warmup_pool(pool: ThreadPoolExecutor, num_workers: int):
|
||||
"""向线程池提交空任务以预热所有线程"""
|
||||
futures = []
|
||||
for i in range(num_workers):
|
||||
futures.append(pool.submit(_warmup_task, i))
|
||||
# 等待所有预热任务完成
|
||||
for f in futures:
|
||||
f.result()
|
||||
print(f"✅ 线程池预热完成 (workers={num_workers})")
|
||||
|
||||
# ---------- 并行 TTS（使用全局线程池） ----------
|
||||
def parallel_tts(chunks: list[str]) -> list[str]:
|
||||
"""
|
||||
多线程并行生成所有语音块，返回按原始顺序的临时文件路径列表。
|
||||
"""
|
||||
tmp_paths = []
|
||||
for _ in chunks:
|
||||
tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False, dir=TMP_DIR)
|
||||
tmp.close()
|
||||
tmp_paths.append(tmp.name)
|
||||
|
||||
try:
|
||||
# 使用全局预热的线程池
|
||||
future_to_idx = {}
|
||||
for idx, chunk in enumerate(chunks):
|
||||
future = tts_pool.submit(
|
||||
genie.tts,
|
||||
character_name=DEFAULT_CHARACTER,
|
||||
text=chunk,
|
||||
play=False,
|
||||
save_path=tmp_paths[idx]
|
||||
)
|
||||
future_to_idx[future] = idx
|
||||
|
||||
for future in as_completed(future_to_idx):
|
||||
future.result() # 传递异常
|
||||
except:
|
||||
for p in tmp_paths:
|
||||
if os.path.exists(p):
|
||||
os.unlink(p)
|
||||
raise
|
||||
return tmp_paths
|
||||
|
||||
# ---------- 应用生命周期 ----------
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
global tts_pool
|
||||
|
||||
"""应用生命周期：加载模型，卸载模型"""
|
||||
print("🚀 正在启动 Genie TTS 极简服务...")
|
||||
try:
|
||||
# 1. 加载模型
|
||||
genie.load_character(
|
||||
character_name=DEFAULT_CHARACTER,
|
||||
onnx_model_dir=DEFAULT_ONNX_MODEL_DIR,
|
||||
@@ -155,86 +40,54 @@ async def lifespan(app: FastAPI):
|
||||
audio_text=DEFAULT_REFERENCE_TEXT,
|
||||
language=DEFAULT_LANGUAGE
|
||||
)
|
||||
|
||||
# 2. 创建并预热全局线程池
|
||||
print(f"🔥 正在创建线程池并预热 (max_workers={MAX_WORKERS})...")
|
||||
tts_pool = ThreadPoolExecutor(max_workers=MAX_WORKERS)
|
||||
if POOL_WARMUP:
|
||||
warmup_pool(tts_pool, MAX_WORKERS)
|
||||
|
||||
print("✅ Genie TTS 服务启动成功！")
|
||||
print(f" - 线程池: max_workers={MAX_WORKERS}, 已预热")
|
||||
print("✅ Genie TTS 模型加载成功！")
|
||||
print("👉 调用方式：http://localhost:8000/tts?text=你要说的话")
|
||||
except Exception as e:
|
||||
print(f"❌ 服务启动失败: {e}")
|
||||
raise e
|
||||
|
||||
|
||||
yield
|
||||
|
||||
|
||||
print("\n🛑 正在关闭服务...")
|
||||
# 清理线程池
|
||||
if tts_pool:
|
||||
print(" 关闭线程池...")
|
||||
tts_pool.shutdown(wait=True)
|
||||
tts_pool = None
|
||||
|
||||
# 卸载模型
|
||||
try:
|
||||
genie.unload_character(DEFAULT_CHARACTER)
|
||||
print("✅ 资源已释放")
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
app = FastAPI(lifespan=lifespan)
|
||||
|
||||
# ---------- 极简 API ----------
|
||||
|
||||
@app.get("/tts")
|
||||
async def text_to_speech(text: str = Query(..., description="待合成的文本")):
|
||||
"""接收任意长度文本，直接合成并返回 WAV 音频。"""
|
||||
if not text.strip():
|
||||
return Response(content="文本不能为空", status_code=400, media_type="text/plain")
|
||||
|
||||
chunks = split_text(text)
|
||||
|
||||
# 生成临时文件，调用 genie 合成
|
||||
tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False, dir=TMP_DIR)
|
||||
tmp_path = tmp.name
|
||||
tmp.close()
|
||||
try:
|
||||
if len(chunks) == 1:
|
||||
# 单块直接合成，避免多线程开销
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False, dir=TMP_DIR) as tmp:
|
||||
tmp_path = tmp.name
|
||||
try:
|
||||
genie.tts(
|
||||
character_name=DEFAULT_CHARACTER,
|
||||
text=chunks[0],
|
||||
play=False,
|
||||
save_path=tmp_path
|
||||
)
|
||||
with open(tmp_path, "rb") as f:
|
||||
audio_bytes = f.read()
|
||||
return Response(content=audio_bytes, media_type="audio/wav")
|
||||
finally:
|
||||
if os.path.exists(tmp_path):
|
||||
os.unlink(tmp_path)
|
||||
else:
|
||||
# 多块并行合成（使用预热好的线程池）
|
||||
tmp_paths = parallel_tts(chunks)
|
||||
try:
|
||||
merged_audio = merge_wavs(tmp_paths)
|
||||
return Response(content=merged_audio, media_type="audio/wav")
|
||||
finally:
|
||||
for p in tmp_paths:
|
||||
if os.path.exists(p):
|
||||
os.unlink(p)
|
||||
genie.tts(
|
||||
character_name=DEFAULT_CHARACTER,
|
||||
text=text,
|
||||
play=False,
|
||||
save_path=tmp_path
|
||||
)
|
||||
with open(tmp_path, "rb") as f:
|
||||
audio_bytes = f.read()
|
||||
return Response(content=audio_bytes, media_type="audio/wav")
|
||||
except Exception as e:
|
||||
return Response(content=f"生成失败: {str(e)}", status_code=500, media_type="text/plain")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return Response(content=f"生成失败: {e}", status_code=500, media_type="text/plain")
|
||||
finally:
|
||||
if os.path.exists(tmp_path):
|
||||
os.unlink(tmp_path)
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
return {
|
||||
"status": "ok",
|
||||
"pool_active": tts_pool is not None,
|
||||
"max_workers": MAX_WORKERS
|
||||
}
|
||||
|
||||
# ---------- 启动入口 ----------
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
return {"status": "ok"}
|
||||
+26
-4
@@ -10,6 +10,7 @@ from pydantic import Field
|
||||
import re, json
|
||||
from datetime import datetime as dt
|
||||
import requests
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, SystemMessage
|
||||
from langchain_core.outputs import ChatResult, ChatGeneration
|
||||
@@ -17,7 +18,7 @@ from langchain_openai import ChatOpenAI
|
||||
|
||||
from agent.utils.image_utils import image_bytes_to_openai_image_url_part
|
||||
from agent.utils.env_utils import LLM_API_KEY, LLM_BASE_URL, LLM_MODEL, LLM_TIMEOUT, TTS_BASE_URL, ASR_BASE_URL
|
||||
from agent.utils.agent_utils import assistant_text, Documents_process
|
||||
from agent.utils.agent_utils import assistant_text, Documents_process, split_text, merge_wavs
|
||||
from agent.utils.image_utils import image_path_to_openai_image_url_part, image_bytes_to_openai_image_url_part, IMAGE_FILE_EXTENSIONS
|
||||
from agent.memory.system_prompt import VISION_SYSTEM_PROMPT
|
||||
|
||||
@@ -218,9 +219,30 @@ class ChatQwen(ChatOpenAI):
|
||||
return new_messages
|
||||
|
||||
def _text_to_audio(self, text: str) -> bytes:
|
||||
resp = requests.get(f"{TTS_BASE_URL}/tts", params={"text": text})
|
||||
resp.raise_for_status()
|
||||
return resp.content
|
||||
chunks = split_text(text)
|
||||
if len(chunks) == 1:
|
||||
resp = requests.get(f"{TTS_BASE_URL}/tts", params={"text": chunks[0]})
|
||||
resp.raise_for_status()
|
||||
return resp.content
|
||||
|
||||
# 多块并行请求
|
||||
audio_parts = [None] * len(chunks)
|
||||
with ThreadPoolExecutor(max_workers=min(len(chunks), 4)) as pool:
|
||||
future_to_idx = {
|
||||
pool.submit(
|
||||
lambda t: requests.get(
|
||||
f"{TTS_BASE_URL}/tts", params={"text": t}
|
||||
).content,
|
||||
chunk
|
||||
): idx for idx, chunk in enumerate(chunks)
|
||||
}
|
||||
for future in as_completed(future_to_idx):
|
||||
idx = future_to_idx[future]
|
||||
try:
|
||||
audio_parts[idx] = future.result()
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"TTS request failed for chunk {idx}: {e}")
|
||||
return merge_wavs(audio_parts)
|
||||
|
||||
@staticmethod
|
||||
def _get_wav_duration_seconds(audio_bytes: bytes) -> float | None:
|
||||
|
||||
@@ -5,7 +5,7 @@ import shutil
|
||||
import sqlite3
|
||||
from typing import List, Tuple, Any
|
||||
import pathlib
|
||||
import mimetypes
|
||||
import mimetypes, re
|
||||
|
||||
from langchain_community.chat_message_histories import SQLChatMessageHistory
|
||||
from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage
|
||||
@@ -527,32 +527,50 @@ class Documents_process:
|
||||
return Documents_process.process_tabular(file_path)
|
||||
else:
|
||||
raise ValueError(f"未处理的文件类型: {file_type}")
|
||||
# 文本分块工具
|
||||
SENT_SPLIT_PATTERN = re.compile(r'(?<=[。！？；，、])')
|
||||
REMOVE_PUNCT_PATTERN = re.compile(r'[。！？；，、：；“”（）《》【】\-\_\—\~\.\,\!\?\s·]')
|
||||
def effective_length(text: str) -> int:
|
||||
return len(REMOVE_PUNCT_PATTERN.sub('', text))
|
||||
|
||||
if __name__ == "__main__":
|
||||
docx_path = r"C:\Users\86166\Desktop\Agent_Learning_minor\信息科学与工程学院-赵永顺-《兼爱》——墨翟一生的“白月光”.docx"
|
||||
pdf_path = r"C:\Users\86166\Desktop\Agent_Learning_minor\信息科学与工程学院-赵永顺-《兼爱》——墨翟一生的“白月光”.pdf"
|
||||
xlsx_path = r"C:\Users\86166\Desktop\Agent_Learning_minor\电科二班2025德育评价.xlsx"
|
||||
py_path = r"C:\Users\86166\Desktop\Agent_Learning_minor\speech_server.py"
|
||||
def split_text(text: str, min_effective_chars: int = 20) -> list[str]:
|
||||
# 先按标点粗分
|
||||
raw_chunks = [c.strip() for c in SENT_SPLIT_PATTERN.split(text) if c.strip()]
|
||||
if not raw_chunks:
|
||||
return [text]
|
||||
|
||||
merged = []
|
||||
buffer = ""
|
||||
for chunk in raw_chunks:
|
||||
buffer += chunk
|
||||
# 当累积的有效字符数达到阈值，或已是最后一个块时，切出一块
|
||||
if effective_length(buffer) >= min_effective_chars:
|
||||
merged.append(buffer)
|
||||
buffer = ""
|
||||
if buffer: # 剩余部分（必然小于阈值）
|
||||
if merged:
|
||||
merged[-1] += buffer # 合并到最后一块
|
||||
else:
|
||||
merged.append(buffer) # 整段文本都太短，保留原样
|
||||
return merged
|
||||
|
||||
output_path = "test_results.txt"
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
test_files = [
|
||||
("py", py_path),
|
||||
("docx", docx_path),
|
||||
("xlsx", xlsx_path),
|
||||
("pdf", pdf_path),
|
||||
]
|
||||
for label, path in test_files:
|
||||
f.write(f"\n===== 测试 {label} 文件：{path} =====\n")
|
||||
try:
|
||||
text, images = Documents_process.process(path)
|
||||
f.write(f"提取文本长度：{len(text)}\n")
|
||||
f.write(f"提取图片数量：{len(images)}\n")
|
||||
f.write("文本预览：\n")
|
||||
f.write(text)
|
||||
except Exception as e:
|
||||
f.write(f"❌ 处理失败: {e}\n")
|
||||
print(f"测试结果已写入 {output_path}")
|
||||
|
||||
def merge_wavs(audio_parts: list[bytes]) -> bytes:
|
||||
"""将多个 WAV 字节串按顺序拼接成一个 WAV"""
|
||||
if len(audio_parts) == 1:
|
||||
return audio_parts[0]
|
||||
import io, wave
|
||||
data_frames = []
|
||||
params = None
|
||||
for part in audio_parts:
|
||||
with wave.open(io.BytesIO(part), 'rb') as wf:
|
||||
if params is None:
|
||||
params = wf.getparams()
|
||||
data_frames.append(wf.readframes(wf.getnframes()))
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, 'wb') as wf_out:
|
||||
wf_out.setparams(params)
|
||||
for frames in data_frames:
|
||||
wf_out.writeframes(frames)
|
||||
return buf.getvalue()
|
||||
|
||||
|
||||
+6
-19
@@ -27,21 +27,13 @@ if str(_SRC) not in sys.path:
|
||||
from agent import chatbot_agent as ca # noqa: E402
|
||||
from web import ui_session as ui # noqa: E402
|
||||
from agent.utils.image_utils import IMAGE_FILE_EXTENSIONS # noqa: E402
|
||||
from agent.utils.agent_utils import DOC_EXT_MAP # noqa: E402
|
||||
|
||||
_WEB_DIR = Path(__file__).resolve().parent
|
||||
_IMAGE_DIR = _WEB_DIR / "image"
|
||||
|
||||
_AUDIO_EXT = frozenset({".wav", ".mp3", ".m4a", ".flac", ".ogg", ".webm"})
|
||||
_IMAGE_EXT = frozenset(IMAGE_FILE_EXTENSIONS)
|
||||
|
||||
|
||||
def _infer_upload_kind(fp: str) -> str | None:
|
||||
ext = os.path.splitext(fp)[1].lower()
|
||||
if ext in _IMAGE_EXT:
|
||||
return "image"
|
||||
if ext in _AUDIO_EXT:
|
||||
return "audio"
|
||||
return "file"
|
||||
_FILE_EXT = frozenset(DOC_EXT_MAP.keys())
|
||||
|
||||
|
||||
def _data_url_for_file(fp: str) -> str | None:
|
||||
@@ -92,7 +84,6 @@ def _serialize_content(content: Any, *, meta: dict[str, Any] | None = None, role
|
||||
parts.append({"type": "text", "text": piece.get("text") or ""})
|
||||
continue
|
||||
fp = piece.get("path") or (piece.get("file") or {}).get("path")
|
||||
display_name = piece.get("name")
|
||||
if piece.get("type") == "image_url":
|
||||
fp = fp or (piece.get("image_url") or {}).get("url")
|
||||
if piece.get("type") == "audio":
|
||||
@@ -104,19 +95,19 @@ def _serialize_content(content: Any, *, meta: dict[str, Any] | None = None, role
|
||||
continue
|
||||
ext = os.path.splitext(fp)[1].lower()
|
||||
if ext in IMAGE_FILE_EXTENSIONS:
|
||||
parts.append({"type": "image", "url": data_url, "path": fp, "name": display_name or os.path.basename(fp)})
|
||||
parts.append({"type": "image", "url": data_url, "path": fp, "name": os.path.basename(fp)})
|
||||
elif ext in _AUDIO_EXT:
|
||||
audio_meta = meta.get("audio") if isinstance(meta.get("audio"), dict) else {}
|
||||
parts.append({
|
||||
"type": "audio",
|
||||
"url": data_url,
|
||||
"path": fp,
|
||||
"name": display_name or os.path.basename(fp),
|
||||
"name": os.path.basename(fp),
|
||||
"autoplay": bool(meta.get("autoplay") or audio_meta.get("autoplay")) if role == "assistant" else False,
|
||||
"duration_seconds": audio_meta.get("duration_seconds"),
|
||||
})
|
||||
else:
|
||||
parts.append({"type": "file", "url": data_url, "path": fp, "name": display_name or os.path.basename(fp)})
|
||||
parts.append({"type": "file", "url": data_url, "path": fp, "name": os.path.basename(fp)})
|
||||
return parts
|
||||
|
||||
|
||||
@@ -253,7 +244,6 @@ def _materialize_uploads_to_temp(
|
||||
"""将上传文件落盘到临时目录，返回 ``(query_dict, tmp_root)``；调用方须在 finally 中删除 ``tmp_root``。"""
|
||||
tmp_root = tempfile.mkdtemp(prefix="web_mm_")
|
||||
file_paths: list[str] = []
|
||||
file_names: list[str] = []
|
||||
for uf in images or []:
|
||||
if uf is None or not uf.filename:
|
||||
continue
|
||||
@@ -262,7 +252,6 @@ def _materialize_uploads_to_temp(
|
||||
with open(dest, "wb") as f:
|
||||
shutil.copyfileobj(uf.file, f)
|
||||
file_paths.append(dest)
|
||||
file_names.append(uf.filename)
|
||||
|
||||
for uf in files or []:
|
||||
if uf is None or not uf.filename:
|
||||
@@ -272,7 +261,6 @@ def _materialize_uploads_to_temp(
|
||||
with open(dest, "wb") as f:
|
||||
shutil.copyfileobj(uf.file, f)
|
||||
file_paths.append(dest)
|
||||
file_names.append(uf.filename)
|
||||
|
||||
if audio is not None and audio.filename:
|
||||
ext = Path(audio.filename).suffix or ".bin"
|
||||
@@ -280,9 +268,8 @@ def _materialize_uploads_to_temp(
|
||||
with open(dest, "wb") as f:
|
||||
shutil.copyfileobj(audio.file, f)
|
||||
file_paths.append(dest)
|
||||
file_names.append(audio.filename)
|
||||
|
||||
query = {"text": (text or "").strip(), "files": file_paths, "file_names": file_names}
|
||||
query = {"text": (text or "").strip(), "files": file_paths}
|
||||
return query, tmp_root
|
||||
|
||||
|
||||
|
||||
Reference in new issue
Block a user