977 lines
34 KiB
Python
977 lines
34 KiB
Python
"""基于本地 FunASR 源码适配的实时 WebSocket 服务端。
|
||
|
||
参考源码：``runtime/python/websocket/funasr_wss_server.py``。
|
||
浏览器适配层使用 FunASR 在线 WebSocket 协议。离线 ASR、标点和进程内说话人验证均为可选项；
|
||
CAM++ 由独立辅助服务运行。
|
||
"""
|
||
|
||
import asyncio
|
||
import json
|
||
import websockets
|
||
import time
|
||
import numpy as np
|
||
import argparse
|
||
import ssl
|
||
import os
|
||
import wave
|
||
import functools
|
||
import re
|
||
from concurrent.futures import ThreadPoolExecutor
|
||
from scipy.spatial.distance import cosine
|
||
|
||
import torch # 保留不影响
|
||
|
||
|
||
def _bounded_env_float(name: str, default: float, minimum: float, maximum: float) -> float:
|
||
"""从环境变量读取并校验 FunASR 数值调优参数。"""
|
||
raw_value = os.getenv(name, str(default)).strip()
|
||
try:
|
||
value = float(raw_value)
|
||
except ValueError as exc:
|
||
raise ValueError(f"{name} must be a number; got {raw_value!r}") from exc
|
||
if not minimum <= value <= maximum:
|
||
raise ValueError(f"{name} must be between {minimum} and {maximum}; got {value}")
|
||
return value
|
||
|
||
|
||
def _positive_env_int(name: str, default: int) -> int:
|
||
"""读取正整数环境变量，参数无效时给出明确的启动错误。"""
|
||
raw_value = os.getenv(name, str(default)).strip()
|
||
try:
|
||
value = int(raw_value)
|
||
except ValueError as exc:
|
||
raise ValueError(f"{name} must be a positive integer; got {raw_value!r}") from exc
|
||
if value <= 0:
|
||
raise ValueError(f"{name} must be greater than zero; got {value}")
|
||
return value
|
||
|
||
|
||
def normalize_contextual_hotwords(value):
|
||
"""将会话热词拆成 Contextual Paraformer 接受的空格分隔文本。"""
|
||
if isinstance(value, (list, tuple)):
|
||
source = ",".join(str(item) for item in value if item is not None)
|
||
elif isinstance(value, str):
|
||
source = value
|
||
else:
|
||
return ""
|
||
terms = re.split(r"[,\uff0c\u3001;\uff1b\r\n]+", source)
|
||
return " ".join(dict.fromkeys(term.strip() for term in terms if term.strip()))
|
||
|
||
|
||
# 显式设置该值后，将关闭 FunASR 按静音时长动态调整阈值的机制，便于测试。
|
||
VAD_MAX_END_SILENCE_MS = _positive_env_int("FUNASR_VAD_MAX_END_SILENCE_MS", 800)
|
||
VAD_PARAGRAPH_MAX_END_SILENCE_MS = _positive_env_int(
|
||
"FUNASR_VAD_PARAGRAPH_MAX_END_SILENCE_MS", 5000
|
||
)
|
||
# 增大裕量可以让较弱的背景噪声更容易被判定为非语音。
|
||
VAD_SPEECH_NOISE_THRESHOLD = _bounded_env_float(
|
||
"FUNASR_VAD_SPEECH_NOISE_THRES", 0.6, 0.0, 1.0
|
||
)
|
||
|
||
|
||
def to_python(obj):
|
||
"""递归地把 numpy / torch 等类型转成纯 Python，可 JSON 序列化。"""
|
||
try:
|
||
import numpy as np # noqa
|
||
import torch # noqa
|
||
except Exception:
|
||
np = None
|
||
torch = None
|
||
|
||
if np is not None and isinstance(obj, np.generic):
|
||
return obj.item()
|
||
if np is not None and isinstance(obj, np.ndarray):
|
||
return obj.tolist()
|
||
if torch is not None and isinstance(obj, torch.Tensor):
|
||
return obj.cpu().tolist()
|
||
|
||
if isinstance(obj, dict):
|
||
return {k: to_python(v) for k, v in obj.items()}
|
||
if isinstance(obj, (list, tuple)):
|
||
return [to_python(v) for v in obj]
|
||
|
||
return obj
|
||
|
||
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("--host", type=str, default="0.0.0.0", required=False, help="host ip")
|
||
parser.add_argument("--port", type=int, default=10095, required=False, help="grpc server port")
|
||
|
||
parser.add_argument(
|
||
"--asr_model",
|
||
type=str,
|
||
default="",
|
||
help="Optional offline ASR model; empty means online-only mode.",
|
||
)
|
||
parser.add_argument("--asr_model_revision", type=str, default="v2.0.4", help="")
|
||
|
||
parser.add_argument(
|
||
"--asr_model_online",
|
||
type=str,
|
||
default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
|
||
help="model from modelscope",
|
||
)
|
||
parser.add_argument("--asr_model_online_revision", type=str, default="v2.0.4", help="")
|
||
|
||
parser.add_argument(
|
||
"--vad_model",
|
||
type=str,
|
||
default="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
|
||
help="model from modelscope",
|
||
)
|
||
parser.add_argument("--vad_model_revision", type=str, default="v2.0.4", help="")
|
||
|
||
parser.add_argument(
|
||
"--punc_model",
|
||
type=str,
|
||
default="",
|
||
help="model from modelscope",
|
||
)
|
||
parser.add_argument("--punc_model_revision", type=str, default="v2.0.4", help="")
|
||
|
||
parser.add_argument("--ngpu", type=int, default=1, help="0 for cpu, 1 for gpu")
|
||
parser.add_argument("--device", type=str, default="cuda", help="cuda, cpu")
|
||
parser.add_argument("--vad_device", type=str, default=None, help="Optional VAD device override")
|
||
parser.add_argument("--ncpu", type=int, default=4, help="cpu cores")
|
||
parser.add_argument(
|
||
"--enable_speaker_verification",
|
||
action="store_true",
|
||
help="Load native CAM++; disabled when CAM++ is hosted by the auxiliary service.",
|
||
)
|
||
|
||
parser.add_argument(
|
||
"--certfile",
|
||
type=str,
|
||
default="",
|
||
required=False,
|
||
help="certfile for ssl",
|
||
)
|
||
parser.add_argument(
|
||
"--keyfile",
|
||
type=str,
|
||
default="",
|
||
required=False,
|
||
help="keyfile for ssl",
|
||
)
|
||
|
||
# ====== 保存 2pass 离线阶段送入 ASR 的音频片段（排查 VAD 切分）======
|
||
parser.add_argument(
|
||
"--save_offline_segments",
|
||
action="store_true",
|
||
help="Save each offline (2pass) audio segment sent to offline ASR as wav for debugging VAD split.",
|
||
)
|
||
parser.add_argument(
|
||
"--save_offline_segments_dir",
|
||
type=str,
|
||
default="./offline_segments",
|
||
help="Directory to save offline wav segments when --save_offline_segments is enabled.",
|
||
)
|
||
|
||
# ====== 并发控制：核心新增 ======
|
||
parser.add_argument(
|
||
"--worker_threads",
|
||
type=int,
|
||
default=max(4, (os.cpu_count() or 4)),
|
||
help="ThreadPoolExecutor max_workers. Used to offload blocking inference so event loop won't be blocked.",
|
||
)
|
||
parser.add_argument("--concurrent_vad", type=int, default=4, help="Max concurrent VAD generate() calls.")
|
||
parser.add_argument("--concurrent_asr_online", type=int, default=4, help="Max concurrent streaming ASR generate() calls.")
|
||
# Contextual Paraformer 将本轮热词暂存在模型对象上，串行离线调用以隔离不同会话热词。
|
||
parser.add_argument("--concurrent_asr_offline", type=int, default=1, help="Max concurrent offline ASR generate() calls.")
|
||
parser.add_argument("--concurrent_punc", type=int, default=1, help="Max concurrent punctuation generate() calls.")
|
||
parser.add_argument("--concurrent_sv", type=int, default=1, help="Max concurrent speaker verification generate() calls.")
|
||
parser.add_argument(
|
||
"--speaker_db_reload_sec",
|
||
type=int,
|
||
default=5,
|
||
help="Reload speaker_db.json at most once every N seconds (avoid frequent disk IO).",
|
||
)
|
||
|
||
args = parser.parse_args()
|
||
|
||
websocket_users = set()
|
||
SPEAKER_DB_PATH = os.path.join(os.path.dirname(__file__), "speaker_db.json")
|
||
|
||
|
||
def _ensure_dir(p: str):
|
||
try:
|
||
os.makedirs(p, exist_ok=True)
|
||
except Exception:
|
||
pass
|
||
|
||
|
||
def _pcm_duration_ms(pcm_bytes: bytes, fs: int, ch: int = 1, sampwidth: int = 2) -> int:
|
||
"""根据 fs/ch/sampwidth 计算 PCM 时长，避免写死 16k -> 32 bytes/ms。"""
|
||
if not pcm_bytes:
|
||
return 0
|
||
bytes_per_ms = (fs * ch * sampwidth) / 1000.0
|
||
if bytes_per_ms <= 0:
|
||
return 0
|
||
return int(len(pcm_bytes) / bytes_per_ms)
|
||
|
||
|
||
def _safe_int(v, default):
|
||
try:
|
||
return int(v)
|
||
except Exception:
|
||
return default
|
||
|
||
|
||
# ========= 说话人数据库：使用缓存，避免每段音频都读取磁盘 =========
|
||
_SPEAKER_DB_CACHE = {}
|
||
_SPEAKER_DB_CACHE_TS = 0.0
|
||
|
||
|
||
def _load_speaker_db_sync():
|
||
if not os.path.exists(SPEAKER_DB_PATH):
|
||
return {}
|
||
try:
|
||
with open(SPEAKER_DB_PATH, "r", encoding="utf-8") as f:
|
||
data = json.load(f)
|
||
return data if isinstance(data, dict) else {}
|
||
except Exception:
|
||
return {}
|
||
|
||
|
||
def get_speaker_db_cached(now_ts: float, reload_sec: int):
|
||
global _SPEAKER_DB_CACHE, _SPEAKER_DB_CACHE_TS
|
||
if (now_ts - _SPEAKER_DB_CACHE_TS) >= max(1, int(reload_sec)):
|
||
_SPEAKER_DB_CACHE = _load_speaker_db_sync()
|
||
_SPEAKER_DB_CACHE_TS = now_ts
|
||
return _SPEAKER_DB_CACHE or {}
|
||
|
||
|
||
def _save_wav_sync(out_path: str, audio_bytes: bytes, fs: int, ch: int, sampwidth: int):
|
||
with wave.open(out_path, "wb") as wf:
|
||
wf.setnchannels(ch)
|
||
wf.setsampwidth(sampwidth)
|
||
wf.setframerate(fs)
|
||
wf.writeframes(audio_bytes)
|
||
|
||
|
||
def save_offline_wav_segment_sync(websocket, audio_bytes: bytes, reason: str = "offline"):
|
||
"""
|
||
保存离线阶段送入 ASR 的音频片段，方便人工试听排查 VAD 切分是否正确。
|
||
约定：audio_bytes 为 单声道 PCM16 little-endian（默认 16k）。
|
||
（注意：这是同步函数，外层会放线程池执行）
|
||
"""
|
||
if not getattr(websocket, "save_offline_segments", False):
|
||
return
|
||
if "2pass" not in (getattr(websocket, "mode", "") or ""):
|
||
return
|
||
if not audio_bytes:
|
||
return
|
||
|
||
fs = int(getattr(websocket, "audio_fs", 16000) or 16000)
|
||
ch = 1
|
||
sampwidth = 2 # PCM 使用 16 位采样宽度。
|
||
|
||
# 写入音频帧时保持 int16 采样对齐。
|
||
if len(audio_bytes) % 2 == 1:
|
||
audio_bytes = audio_bytes[:-1]
|
||
if not audio_bytes:
|
||
return
|
||
|
||
seg_idx = int(getattr(websocket, "offline_seg_idx", 0))
|
||
websocket.offline_seg_idx = seg_idx + 1
|
||
|
||
duration_ms = _pcm_duration_ms(audio_bytes, fs=fs, ch=ch, sampwidth=sampwidth)
|
||
|
||
base_dir = getattr(websocket, "offline_save_dir", args.save_offline_segments_dir)
|
||
_ensure_dir(base_dir)
|
||
|
||
wav_name = (getattr(websocket, "wav_name", "microphone") or "microphone").replace("/", "_")
|
||
ts = int(time.time() * 1000)
|
||
fname = f"{wav_name}_{ts}_seg{seg_idx:04d}_{reason}_{duration_ms}ms.wav"
|
||
out_path = os.path.join(base_dir, fname)
|
||
|
||
try:
|
||
_save_wav_sync(out_path, audio_bytes, fs=fs, ch=ch, sampwidth=sampwidth)
|
||
print(f"[SAVE_OFFLINE_SEG] {out_path} ({duration_ms} ms, {len(audio_bytes)} bytes)")
|
||
except Exception as e:
|
||
print(f"[SAVE_OFFLINE_SEG] failed: {e}")
|
||
|
||
|
||
print("model loading")
|
||
from funasr import AutoModel # noqa
|
||
|
||
# ====== 离线 ASR ======
|
||
# 在线部署时不加载离线模型，以节省内存。
|
||
model_asr = (
|
||
AutoModel(
|
||
model=args.asr_model,
|
||
model_revision=args.asr_model_revision,
|
||
ngpu=args.ngpu,
|
||
ncpu=args.ncpu,
|
||
device=args.device,
|
||
disable_pbar=True,
|
||
disable_log=True,
|
||
)
|
||
if args.asr_model
|
||
else None
|
||
)
|
||
|
||
# 流式 ASR
|
||
model_asr_streaming = AutoModel(
|
||
model=args.asr_model_online,
|
||
model_revision=args.asr_model_online_revision,
|
||
ngpu=args.ngpu,
|
||
ncpu=args.ncpu,
|
||
device=args.device,
|
||
disable_pbar=True,
|
||
disable_log=True,
|
||
)
|
||
|
||
# VAD
|
||
model_vad = AutoModel(
|
||
model=args.vad_model,
|
||
model_revision=args.vad_model_revision,
|
||
ngpu=args.ngpu if (args.vad_device or args.device).startswith("cuda") else 0,
|
||
ncpu=args.ncpu,
|
||
device=args.vad_device or args.device,
|
||
disable_pbar=True,
|
||
disable_log=True,
|
||
)
|
||
|
||
# 标点模型
|
||
if args.punc_model != "":
|
||
model_punc = AutoModel(
|
||
model=args.punc_model,
|
||
model_revision=args.punc_model_revision,
|
||
ngpu=args.ngpu,
|
||
ncpu=args.ncpu,
|
||
device=args.device,
|
||
disable_pbar=True,
|
||
disable_log=True,
|
||
)
|
||
else:
|
||
model_punc = None
|
||
|
||
# CAM++ 由辅助服务加载，避免在此进程中重复占用显存。
|
||
model_sv = (
|
||
AutoModel(
|
||
model="iic/speech_campplus_sv_zh-cn_16k-common",
|
||
ngpu=args.ngpu,
|
||
device=args.device,
|
||
disable_pbar=True,
|
||
disable_log=True,
|
||
)
|
||
if args.enable_speaker_verification
|
||
else None
|
||
)
|
||
|
||
print("model loaded! (now supports multi-client with non-blocking inference)")
|
||
|
||
|
||
# ====== 线程池 + 并发阈值（核心）======
|
||
EXECUTOR = ThreadPoolExecutor(max_workers=int(args.worker_threads))
|
||
|
||
SEM_VAD = asyncio.Semaphore(max(1, int(args.concurrent_vad)))
|
||
SEM_ASR_ONLINE = asyncio.Semaphore(max(1, int(args.concurrent_asr_online)))
|
||
SEM_ASR_OFFLINE = asyncio.Semaphore(max(1, int(args.concurrent_asr_offline)))
|
||
SEM_PUNC = asyncio.Semaphore(max(1, int(args.concurrent_punc)))
|
||
SEM_SV = asyncio.Semaphore(max(1, int(args.concurrent_sv)))
|
||
SEM_WAV = asyncio.Semaphore(max(1, 4)) # 保存 wav 一般不需要太大
|
||
|
||
|
||
async def run_blocking(fn, *a, sem: asyncio.Semaphore | None = None, **kw):
|
||
"""
|
||
把阻塞函数丢线程池执行，避免卡 event loop。
|
||
sem 用于限流（避免 GPU / 模型被打爆）。
|
||
"""
|
||
loop = asyncio.get_running_loop()
|
||
call = functools.partial(fn, *a, **kw)
|
||
if sem is None:
|
||
return await loop.run_in_executor(EXECUTOR, call)
|
||
async with sem:
|
||
return await loop.run_in_executor(EXECUTOR, call)
|
||
|
||
|
||
def _generate_sync(model, audio_or_text, status_dict):
|
||
# 注意：status_dict 中包含 cache，模型的 generate 调用会更新该缓存。
|
||
return model.generate(input=audio_or_text, **status_dict)
|
||
|
||
|
||
async def ws_reset(websocket):
|
||
print("ws reset now, total num is ", len(websocket_users))
|
||
|
||
websocket.status_dict_asr_online["cache"] = {}
|
||
websocket.status_dict_asr_online["is_final"] = True
|
||
websocket.status_dict_vad["cache"] = {}
|
||
websocket.status_dict_vad["is_final"] = True
|
||
websocket.status_dict_punc["cache"] = {}
|
||
|
||
await websocket.close()
|
||
|
||
|
||
async def clear_websocket():
|
||
for websocket in list(websocket_users):
|
||
await ws_reset(websocket)
|
||
websocket_users.clear()
|
||
|
||
|
||
async def ws_serve(websocket, path=None):
|
||
# 新版 websockets 不会传入 path 参数，这里兼容两种调用方式。
|
||
if path is None:
|
||
path = getattr(websocket, "path", None)
|
||
frames = []
|
||
frames_asr = []
|
||
frames_asr_online = []
|
||
pending_offline_audio = []
|
||
global websocket_users
|
||
websocket_users.add(websocket)
|
||
|
||
websocket.status_dict_asr = {} # hotword 等
|
||
websocket.status_dict_asr_online = {"cache": {}, "is_final": False}
|
||
# 将测试参数显式传给 FunASR，使其使用固定阈值。
|
||
websocket.status_dict_vad = {
|
||
"cache": {},
|
||
"is_final": False,
|
||
"max_end_silence_time": VAD_MAX_END_SILENCE_MS,
|
||
"speech_noise_thres": VAD_SPEECH_NOISE_THRESHOLD,
|
||
}
|
||
websocket.status_dict_punc = {"cache": {}}
|
||
|
||
websocket.chunk_interval = 10
|
||
websocket.sentence_strategy = 0
|
||
websocket.vad_pre_idx = 0
|
||
speech_start = False
|
||
speech_end_i = -1
|
||
online_needs_finalization = False
|
||
session_errors = []
|
||
|
||
websocket.wav_name = "microphone"
|
||
websocket.mode = "2pass"
|
||
websocket.is_speaking = True # ✅ 默认初始化，避免 AttributeError
|
||
|
||
# 保存离线片段
|
||
websocket.audio_fs = 16000
|
||
websocket.offline_seg_idx = 0
|
||
websocket.save_offline_segments = bool(args.save_offline_segments)
|
||
websocket.offline_save_dir = args.save_offline_segments_dir
|
||
if websocket.save_offline_segments:
|
||
_ensure_dir(websocket.offline_save_dir)
|
||
print(f"[SAVE_OFFLINE_SEG] enabled, dir={websocket.offline_save_dir}")
|
||
|
||
print("new user connected", flush=True)
|
||
|
||
def record_error(message):
|
||
if message not in session_errors:
|
||
session_errors.append(message)
|
||
|
||
async def finalize_online_segment():
|
||
nonlocal frames_asr_online, online_needs_finalization
|
||
|
||
if websocket.mode not in ("2pass", "online") or not online_needs_finalization:
|
||
return
|
||
|
||
websocket.status_dict_asr_online["is_final"] = True
|
||
try:
|
||
await async_asr_online(websocket, b"".join(frames_asr_online))
|
||
except Exception as e:
|
||
print("error in final asr streaming:", e)
|
||
record_error(f"online inference failed: {e}")
|
||
|
||
frames_asr_online = []
|
||
websocket.status_dict_asr_online["cache"] = {}
|
||
websocket.status_dict_asr_online["is_final"] = False
|
||
online_needs_finalization = False
|
||
|
||
async def finish_input(send_end_ack):
|
||
nonlocal frames, frames_asr, frames_asr_online, pending_offline_audio
|
||
nonlocal speech_start, speech_end_i, online_needs_finalization
|
||
|
||
await finalize_online_segment()
|
||
|
||
if websocket.mode in ("2pass", "offline"):
|
||
audio_in = b"".join(frames_asr)
|
||
if not audio_in:
|
||
audio_in = b"".join(pending_offline_audio)
|
||
|
||
if audio_in:
|
||
if websocket.save_offline_segments and audio_in:
|
||
try:
|
||
await run_blocking(
|
||
save_offline_wav_segment_sync,
|
||
websocket,
|
||
audio_in,
|
||
"not_speaking",
|
||
sem=SEM_WAV,
|
||
)
|
||
except Exception as e:
|
||
print("[SAVE_OFFLINE_SEG] async failed:", e)
|
||
|
||
try:
|
||
await async_asr(websocket, audio_in)
|
||
pending_offline_audio = []
|
||
except Exception as e:
|
||
print("error in final asr offline:", e)
|
||
record_error(f"offline inference failed: {e}")
|
||
|
||
errors = list(session_errors)
|
||
|
||
frames = []
|
||
frames_asr = []
|
||
frames_asr_online = []
|
||
pending_offline_audio = []
|
||
speech_start = False
|
||
speech_end_i = -1
|
||
online_needs_finalization = False
|
||
websocket.vad_pre_idx = 0
|
||
websocket.status_dict_asr_online["cache"] = {}
|
||
websocket.status_dict_vad["cache"] = {}
|
||
|
||
if send_end_ack:
|
||
acknowledgement = {
|
||
"mode": websocket.mode,
|
||
"wav_name": websocket.wav_name,
|
||
"is_final": not errors,
|
||
"is_end": True,
|
||
}
|
||
if errors:
|
||
acknowledgement["error"] = "; ".join(errors)
|
||
await websocket.send(
|
||
json.dumps(acknowledgement, ensure_ascii=False)
|
||
)
|
||
session_errors.clear()
|
||
elif errors:
|
||
raise RuntimeError("; ".join(errors))
|
||
|
||
try:
|
||
async for message in websocket:
|
||
# ========== 1) 先处理“文本配置消息” ==========
|
||
if isinstance(message, str):
|
||
try:
|
||
messagejson = json.loads(message)
|
||
except Exception as e:
|
||
print("bad json message:", e, message[:200])
|
||
continue
|
||
|
||
# 长时间音频会话中避免逐条消息写日志。
|
||
|
||
end_of_input = False
|
||
if "is_speaking" in messagejson:
|
||
websocket.is_speaking = bool(messagejson["is_speaking"])
|
||
websocket.status_dict_asr_online["is_final"] = (not websocket.is_speaking)
|
||
end_of_input = not websocket.is_speaking
|
||
|
||
if "chunk_interval" in messagejson:
|
||
websocket.chunk_interval = _safe_int(
|
||
messagejson["chunk_interval"], websocket.chunk_interval
|
||
)
|
||
|
||
if "sentence_strategy" in messagejson:
|
||
# 将腾讯前端的选项映射为 FunASR 的 VAD 端点时长。
|
||
strategy = _safe_int(messagejson["sentence_strategy"], 0)
|
||
websocket.sentence_strategy = strategy if strategy in (0, 1) else 0
|
||
websocket.status_dict_vad["max_end_silence_time"] = (
|
||
VAD_PARAGRAPH_MAX_END_SILENCE_MS
|
||
if websocket.sentence_strategy == 1
|
||
else VAD_MAX_END_SILENCE_MS
|
||
)
|
||
|
||
if "wav_name" in messagejson:
|
||
websocket.wav_name = messagejson.get("wav_name") or websocket.wav_name
|
||
|
||
if "chunk_size" in messagejson:
|
||
chunk_size = messagejson["chunk_size"]
|
||
if isinstance(chunk_size, str):
|
||
chunk_size = [x.strip() for x in chunk_size.split(",") if x.strip()]
|
||
websocket.status_dict_asr_online["chunk_size"] = [int(x) for x in chunk_size]
|
||
|
||
if "encoder_chunk_look_back" in messagejson:
|
||
websocket.status_dict_asr_online["encoder_chunk_look_back"] = messagejson[
|
||
"encoder_chunk_look_back"
|
||
]
|
||
|
||
if "decoder_chunk_look_back" in messagejson:
|
||
websocket.status_dict_asr_online["decoder_chunk_look_back"] = messagejson[
|
||
"decoder_chunk_look_back"
|
||
]
|
||
|
||
if "hotwords" in messagejson:
|
||
hotword_data = normalize_contextual_hotwords(messagejson["hotwords"])
|
||
if hotword_data and model_asr is None:
|
||
record_error("热词解码需要启用 FunASR Contextual Paraformer 最终识别模型")
|
||
if hotword_data:
|
||
# 普通 Paraformer 流式模型不具备上下文热词解码，热词只传给最终解码器。
|
||
websocket.status_dict_asr["hotword"] = hotword_data
|
||
else:
|
||
websocket.status_dict_asr.pop("hotword", None)
|
||
print(f"会话热词已更新: {hotword_data}")
|
||
|
||
if "mode" in messagejson:
|
||
requested_mode = messagejson["mode"]
|
||
if requested_mode and requested_mode not in ("online", "offline", "2pass"):
|
||
websocket.mode = requested_mode
|
||
record_error(f"unsupported mode: {requested_mode!r}")
|
||
else:
|
||
websocket.mode = requested_mode or websocket.mode
|
||
|
||
if "audio_fs" in messagejson:
|
||
websocket.audio_fs = _safe_int(messagejson["audio_fs"], 16000)
|
||
|
||
if end_of_input:
|
||
await finish_input(send_end_ack=bool(messagejson.get("is_end")))
|
||
|
||
continue
|
||
|
||
# ========== 2) 处理“二进制音频消息” ==========
|
||
if websocket.mode not in ("online", "offline", "2pass"):
|
||
continue
|
||
|
||
if "chunk_size" not in websocket.status_dict_asr_online:
|
||
print("[WARN] chunk_size not set yet, skip audio frame (send config first).")
|
||
record_error("audio frame discarded: chunk_size is not configured")
|
||
continue
|
||
|
||
try:
|
||
websocket.status_dict_vad["chunk_size"] = int(
|
||
websocket.status_dict_asr_online["chunk_size"][1] * 60 / websocket.chunk_interval
|
||
)
|
||
except Exception as e:
|
||
print("[WARN] set vad chunk_size failed:", e)
|
||
record_error(f"audio frame discarded: invalid VAD chunk_size: {e}")
|
||
continue
|
||
|
||
pcm = message
|
||
frames.append(pcm)
|
||
if websocket.mode in ("2pass", "offline"):
|
||
pending_offline_audio.append(pcm)
|
||
|
||
duration_ms = _pcm_duration_ms(pcm, fs=websocket.audio_fs, ch=1, sampwidth=2)
|
||
websocket.vad_pre_idx += duration_ms
|
||
|
||
# 在线 ASR
|
||
frames_asr_online.append(pcm)
|
||
if websocket.mode in ("2pass", "online"):
|
||
online_needs_finalization = True
|
||
websocket.status_dict_asr_online["is_final"] = (speech_end_i != -1)
|
||
|
||
if (len(frames_asr_online) % websocket.chunk_interval == 0) or websocket.status_dict_asr_online["is_final"]:
|
||
if websocket.mode in ("2pass", "online"):
|
||
audio_in = b"".join(frames_asr_online)
|
||
try:
|
||
await async_asr_online(websocket, audio_in)
|
||
except Exception as e:
|
||
print(f"error in asr streaming, {websocket.status_dict_asr_online}")
|
||
record_error(f"online inference failed: {e}")
|
||
frames_asr_online = []
|
||
|
||
if speech_start:
|
||
frames_asr.append(pcm)
|
||
|
||
# 在线 VAD
|
||
try:
|
||
speech_start_i, speech_end_i = await async_vad(websocket, pcm)
|
||
except Exception as e:
|
||
print("error in vad:", e)
|
||
record_error(f"vad inference failed: {e}")
|
||
speech_start_i, speech_end_i = -1, -1
|
||
|
||
# 将 FunASR VAD 的绝对音频时间发送给桥接层，用于裁掉首尾静音，
|
||
# 让 CAM++ 滑窗只分析当前 VAD 轮次，而不是整段会话的缓冲音频。
|
||
if speech_start_i != -1 or speech_end_i != -1:
|
||
await websocket.send(
|
||
json.dumps(
|
||
{
|
||
"event": "vad",
|
||
"speech_start_ms": speech_start_i if speech_start_i != -1 else None,
|
||
"speech_end_ms": speech_end_i if speech_end_i != -1 else None,
|
||
},
|
||
ensure_ascii=False,
|
||
)
|
||
)
|
||
|
||
if speech_start_i != -1:
|
||
speech_start = True
|
||
if duration_ms > 0:
|
||
beg_bias = (websocket.vad_pre_idx - speech_start_i) // duration_ms
|
||
else:
|
||
beg_bias = 0
|
||
frames_pre = frames[-beg_bias:] if beg_bias > 0 else []
|
||
frames_asr = []
|
||
frames_asr.extend(frames_pre)
|
||
|
||
# ========== 3) 2pass：离线阶段的触发位置 ==========
|
||
if (speech_end_i != -1) or (not websocket.is_speaking):
|
||
await finalize_online_segment()
|
||
|
||
if websocket.mode in ("2pass", "offline"):
|
||
audio_in = b"".join(frames_asr)
|
||
if not audio_in and speech_end_i != -1:
|
||
audio_in = b"".join(pending_offline_audio)
|
||
reason = "vad_end" if speech_end_i != -1 else "not_speaking"
|
||
|
||
# 在线程池保存 WAV，避免磁盘 I/O 阻塞事件循环。
|
||
if websocket.save_offline_segments and audio_in:
|
||
try:
|
||
await run_blocking(
|
||
save_offline_wav_segment_sync,
|
||
websocket,
|
||
audio_in,
|
||
reason,
|
||
sem=SEM_WAV,
|
||
)
|
||
except Exception as e:
|
||
print("[SAVE_OFFLINE_SEG] async failed:", e)
|
||
|
||
if audio_in:
|
||
try:
|
||
await async_asr(websocket, audio_in)
|
||
pending_offline_audio = []
|
||
except Exception as e:
|
||
print("error in asr offline:", e)
|
||
record_error(f"offline inference failed: {e}")
|
||
|
||
frames_asr = []
|
||
speech_start = False
|
||
frames_asr_online = []
|
||
websocket.status_dict_asr_online["cache"] = {}
|
||
websocket.status_dict_asr_online["is_final"] = False
|
||
online_needs_finalization = False
|
||
speech_end_i = -1
|
||
|
||
if not websocket.is_speaking:
|
||
websocket.vad_pre_idx = 0
|
||
frames = []
|
||
websocket.status_dict_vad["cache"] = {}
|
||
else:
|
||
frames = frames[-20:]
|
||
|
||
except websockets.ConnectionClosed:
|
||
print("ConnectionClosed...", websocket_users, flush=True)
|
||
await ws_reset(websocket)
|
||
if websocket in websocket_users:
|
||
websocket_users.remove(websocket)
|
||
except websockets.InvalidState:
|
||
print("InvalidState...")
|
||
try:
|
||
await ws_reset(websocket)
|
||
except Exception:
|
||
pass
|
||
websocket_users.discard(websocket)
|
||
except Exception as e:
|
||
print("Exception:", e)
|
||
try:
|
||
await ws_reset(websocket)
|
||
except Exception:
|
||
pass
|
||
if websocket in websocket_users:
|
||
websocket_users.remove(websocket)
|
||
|
||
|
||
# ===================== 推理：全部改为“线程池 + 限流” =====================
|
||
|
||
async def async_vad(websocket, audio_in: bytes):
|
||
# model_vad.generate 是阻塞操作，必须放到线程池执行。
|
||
out = await run_blocking(_generate_sync, model_vad, audio_in, websocket.status_dict_vad, sem=SEM_VAD)
|
||
segments_result = out[0].get("value", [])
|
||
|
||
speech_start = -1
|
||
speech_end = -1
|
||
|
||
if len(segments_result) == 0 or len(segments_result) > 1:
|
||
return speech_start, speech_end
|
||
if segments_result[0][0] != -1:
|
||
speech_start = segments_result[0][0]
|
||
if segments_result[0][1] != -1:
|
||
speech_end = segments_result[0][1]
|
||
return speech_start, speech_end
|
||
|
||
|
||
def _sv_and_match_sync(audio_in: bytes, reload_sec: int):
|
||
"""
|
||
同步执行：SV embedding + speaker_db 匹配
|
||
返回 (spk_name, best_score)
|
||
"""
|
||
spk_name = "unknown"
|
||
best_score = 0.0
|
||
|
||
sv_out = model_sv.generate(input=audio_in, embedding=True)[0]
|
||
embedding = sv_out["spk_embedding"][0].cpu().numpy()
|
||
|
||
now_ts = time.time()
|
||
local_speaker_db = get_speaker_db_cached(now_ts, reload_sec=reload_sec)
|
||
if local_speaker_db:
|
||
for name, ref_embedding in local_speaker_db.items():
|
||
if ref_embedding is None:
|
||
continue
|
||
arr = np.array(ref_embedding, dtype=np.float32)
|
||
similarity = 1.0 - cosine(embedding, arr)
|
||
print("sv similarity with {}: {}".format(name, similarity))
|
||
if similarity > best_score and similarity > 0.2:
|
||
best_score = similarity
|
||
spk_name = name
|
||
|
||
return spk_name, float(best_score)
|
||
|
||
|
||
async def async_asr(websocket, audio_in: bytes):
|
||
mode = "2pass-offline" if "2pass" in (websocket.mode or "") else websocket.mode
|
||
if model_asr is None:
|
||
raise RuntimeError("offline ASR is disabled; use FunASR online mode")
|
||
|
||
if len(audio_in) <= 0:
|
||
message = {
|
||
"mode": mode,
|
||
"text": "",
|
||
"wav_name": websocket.wav_name,
|
||
"is_final": True,
|
||
}
|
||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||
return
|
||
|
||
# 1) ASR（阻塞操作，在线程池执行）
|
||
rec_result_list = await run_blocking(
|
||
_generate_sync,
|
||
model_asr,
|
||
audio_in,
|
||
websocket.status_dict_asr,
|
||
sem=SEM_ASR_OFFLINE,
|
||
)
|
||
rec_result = rec_result_list[0]
|
||
|
||
print("offline_asr, raw:", rec_result)
|
||
print("offline_asr, keys:", rec_result.keys())
|
||
|
||
text = rec_result.get("text", "")
|
||
timestamp = rec_result.get("timestamp", None)
|
||
sentence_info = rec_result.get("sentence_info", None)
|
||
|
||
# 2) 声纹识别（阻塞，线程池执行）
|
||
spk_name = "unknown"
|
||
best_score = 0.0
|
||
try:
|
||
spk_name, best_score = await run_blocking(
|
||
_sv_and_match_sync,
|
||
audio_in,
|
||
int(args.speaker_db_reload_sec),
|
||
sem=SEM_SV,
|
||
)
|
||
except Exception as e:
|
||
print(f"声纹识别失败: {e}")
|
||
|
||
# 3) 标点（阻塞，线程池执行）
|
||
punc_array = None
|
||
if model_punc is not None and len(text) > 0:
|
||
try:
|
||
# 标点模型只处理文本，不处理音频。
|
||
punc_out = await run_blocking(
|
||
_generate_sync,
|
||
model_punc,
|
||
text,
|
||
websocket.status_dict_punc,
|
||
sem=SEM_PUNC,
|
||
)
|
||
punc_result = punc_out[0]
|
||
print("offline, after punc", punc_result)
|
||
|
||
if "text" in punc_result and punc_result["text"]:
|
||
text = punc_result["text"]
|
||
if "punc_array" in punc_result:
|
||
punc_array = punc_result["punc_array"]
|
||
except Exception as e:
|
||
print("punc failed:", e)
|
||
|
||
# 4) 构造最终消息
|
||
if len(text) > 0:
|
||
print("======offline final text:", text)
|
||
message = {
|
||
"mode": mode,
|
||
"spk_name": spk_name,
|
||
"spk_score": float(best_score),
|
||
"text": text,
|
||
"wav_name": websocket.wav_name,
|
||
"is_final": True,
|
||
}
|
||
if timestamp is not None:
|
||
message["timestamp"] = to_python(timestamp)
|
||
if sentence_info is not None:
|
||
message["sentence_info"] = to_python(sentence_info)
|
||
if punc_array is not None:
|
||
message["punc_array"] = to_python(punc_array)
|
||
|
||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||
else:
|
||
message = {
|
||
"mode": mode,
|
||
"spk_name": spk_name,
|
||
"spk_score": float(best_score),
|
||
"text": "",
|
||
"wav_name": websocket.wav_name,
|
||
"is_final": True,
|
||
}
|
||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||
|
||
|
||
async def async_asr_online(websocket, audio_in: bytes):
|
||
if len(audio_in) <= 0 and not websocket.status_dict_asr_online.get("is_final", False):
|
||
return
|
||
|
||
# 流式 generate 同样是阻塞操作，需要在线程池执行。
|
||
rec_out = await run_blocking(
|
||
_generate_sync,
|
||
model_asr_streaming,
|
||
audio_in,
|
||
websocket.status_dict_asr_online,
|
||
sem=SEM_ASR_ONLINE,
|
||
)
|
||
rec_result = rec_out[0]
|
||
|
||
# 2pass 模式下在线阶段只发送中间结果；最终结果交给离线阶段输出。
|
||
if websocket.mode == "2pass" and websocket.status_dict_asr_online.get("is_final", False):
|
||
return
|
||
|
||
is_final = bool(
|
||
websocket.status_dict_asr_online.get("is_final", False) or (not websocket.is_speaking)
|
||
)
|
||
# 即使最终解码没有新增字符，也必须显式发送 final 事件；桥接层依赖此事件
|
||
# 结束静音期间的轮次，避免最后一句一直停留在中间态。
|
||
if rec_result.get("text") or is_final:
|
||
mode = "2pass-online" if "2pass" in (websocket.mode or "") else websocket.mode
|
||
message = {
|
||
"mode": mode,
|
||
"text": rec_result.get("text", ""),
|
||
"wav_name": websocket.wav_name,
|
||
"is_final": is_final,
|
||
}
|
||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||
|
||
|
||
# ===================== 启动服务 =====================
|
||
|
||
async def main():
|
||
if len(args.certfile) > 0:
|
||
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
|
||
ssl_context.load_cert_chain(args.certfile, keyfile=args.keyfile)
|
||
server = await websockets.serve(
|
||
ws_serve,
|
||
args.host,
|
||
args.port,
|
||
subprotocols=["binary"],
|
||
ping_interval=None,
|
||
ssl=ssl_context,
|
||
)
|
||
else:
|
||
server = await websockets.serve(
|
||
ws_serve,
|
||
args.host,
|
||
args.port,
|
||
subprotocols=["binary"],
|
||
ping_interval=None,
|
||
)
|
||
|
||
print(f"WS server started at ws(s)://{args.host}:{args.port}")
|
||
await server.wait_closed()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
try:
|
||
asyncio.run(main())
|
||
finally:
|
||
try:
|
||
EXECUTOR.shutdown(wait=False, cancel_futures=True)
|
||
except Exception:
|
||
pass
|