语义分割
This commit is contained in:
朱潮
parent
f9e9c3c26d
commit
7a547322e3
36
poetry.lock
generated
36
poetry.lock
generated
@ -5302,25 +5302,19 @@ train = ["accelerate (>=0.20.3)", "datasets"]
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[[package]]
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name = "setuptools"
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version = "82.0.1"
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description = "Most extensible Python build backend with support for C/C++ extension modules"
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version = "70.3.0"
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description = "Easily download, build, install, upgrade, and uninstall Python packages"
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optional = false
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python-versions = ">=3.9"
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python-versions = ">=3.8"
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groups = ["main"]
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markers = "python_version >= \"3.13\""
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files = [
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{file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
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{file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
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{file = "setuptools-70.3.0-py3-none-any.whl", hash = "sha256:fe384da74336c398e0d956d1cae0669bc02eed936cdb1d49b57de1990dc11ffc"},
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{file = "setuptools-70.3.0.tar.gz", hash = "sha256:f171bab1dfbc86b132997f26a119f6056a57950d058587841a0082e8830f9dc5"},
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]
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[package.extras]
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check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.13.0) ; sys_platform != \"cygwin\""]
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core = ["importlib_metadata (>=6) ; python_version < \"3.10\"", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1) ; python_version < \"3.11\"", "wheel (>=0.43.0)"]
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cover = ["pytest-cov"]
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doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
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enabler = ["pytest-enabler (>=2.2)"]
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test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
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type = ["importlib_metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.develop (>=7.21) ; sys_platform != \"cygwin\"", "mypy (==1.18.*)", "pytest-mypy"]
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doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-ruff (>=0.3.2) ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
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[[package]]
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name = "shellingham"
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@ -6347,6 +6341,20 @@ files = [
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{file = "wcwidth-0.6.0.tar.gz", hash = "sha256:cdc4e4262d6ef9a1a57e018384cbeb1208d8abbc64176027e2c2455c81313159"},
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]
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[[package]]
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name = "webrtcvad"
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version = "2.0.10"
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description = "Python interface to the Google WebRTC Voice Activity Detector (VAD)"
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optional = false
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python-versions = "*"
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groups = ["main"]
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files = [
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{file = "webrtcvad-2.0.10.tar.gz", hash = "sha256:f1bed2fb25b63fb7b1a55d64090c993c9c9167b28485ae0bcdd81cf6ede96aea"},
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]
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[package.extras]
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dev = ["check-manifest", "memory_profiler", "nose", "psutil", "unittest2", "zest.releaser"]
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[[package]]
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name = "websockets"
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version = "15.0.1"
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@ -6983,4 +6991,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
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[metadata]
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lock-version = "2.1"
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python-versions = ">=3.12,<3.15"
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content-hash = "1461514ed1f9639f41f43ebb28f2a3fcd2d5a5dde954cd509c0ea7bf181e9bb6"
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content-hash = "c9c4f80cdbf7d6bce20f65f40b9adce05c5f4a830299de148fcd8482937bddb0"
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@ -39,7 +39,9 @@ dependencies = [
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"ragflow-sdk (>=0.23.0,<0.24.0)",
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"httpx (>=0.28.1,<0.29.0)",
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"wsgidav (>=4.3.3,<5.0.0)",
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"websockets (>=15.0.0,<16.0.0)"
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"websockets (>=15.0.0,<16.0.0)",
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"setuptools (<71)",
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"webrtcvad (>=2.0.10,<3.0.0)",
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]
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[tool.poetry.requires-plugins]
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@ -165,7 +165,7 @@ safetensors==0.7.0 ; python_version >= "3.12" and python_version < "3.15"
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scikit-learn==1.8.0 ; python_version >= "3.12" and python_version < "3.15"
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scipy==1.17.1 ; python_version >= "3.12" and python_version < "3.15"
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sentence-transformers==3.4.1 ; python_version >= "3.12" and python_version < "3.15"
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setuptools==82.0.1 ; python_version >= "3.13" and python_version < "3.15"
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setuptools==70.3.0 ; python_version >= "3.12" and python_version < "3.15"
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shellingham==1.5.4 ; python_version >= "3.12" and python_version < "3.15"
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six==1.17.0 ; python_version >= "3.12" and python_version < "3.15"
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sniffio==1.3.1 ; python_version >= "3.12" and python_version < "3.15"
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@ -203,6 +203,7 @@ uvloop==0.22.1 ; python_version >= "3.12" and python_version < "3.15"
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watchfiles==1.1.1 ; python_version >= "3.12" and python_version < "3.15"
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wcmatch==10.1 ; python_version >= "3.12" and python_version < "3.15"
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wcwidth==0.6.0 ; python_version >= "3.12" and python_version < "3.15"
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webrtcvad==2.0.10 ; python_version >= "3.12" and python_version < "3.15"
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websockets==15.0.1 ; python_version >= "3.12" and python_version < "3.15"
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wrapt==1.17.3 ; python_version >= "3.12" and python_version < "3.15"
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wsgidav==4.3.3 ; python_version >= "3.12" and python_version < "3.15"
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@ -4,13 +4,15 @@ import struct
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import uuid
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from typing import Optional, Callable, Awaitable
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import webrtcvad
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from services.streaming_asr_client import StreamingASRClient
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from services.streaming_tts_client import StreamingTTSClient
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from services.voice_utils import (
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StreamTagFilter,
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clean_markdown,
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stream_v3_agent,
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SENTENCE_END_RE,
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TTSSentenceSplitter,
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)
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from utils.settings import VOICE_LITE_SILENCE_TIMEOUT
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@ -63,7 +65,8 @@ class VoiceLiteSession:
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self._last_asr_emit_time: float = 0
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self._utterance_lock = asyncio.Lock()
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# VAD (Voice Activity Detection) state
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# VAD (Voice Activity Detection) via webrtcvad
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self._vad = webrtcvad.Vad(2) # aggressiveness 0-3 (2 = balanced)
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self._vad_speaking = False # Whether user is currently speaking
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self._vad_silence_start: float = 0 # When silence started
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self._vad_finish_task: Optional[asyncio.Task] = None
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@ -105,23 +108,52 @@ class VoiceLiteSession:
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await self._asr_client.close()
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# VAD configuration
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VAD_ENERGY_THRESHOLD = 500 # RMS energy threshold for voice detection
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VAD_SILENCE_DURATION = 1.5 # Seconds of silence before sending finish
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VAD_PRE_BUFFER_SIZE = 5 # Number of audio chunks to buffer before VAD triggers
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VAD_SOURCE_RATE = 24000 # Input audio sample rate
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VAD_TARGET_RATE = 16000 # webrtcvad supported sample rate
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VAD_FRAME_DURATION_MS = 30 # Frame duration for webrtcvad (10, 20, or 30 ms)
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_audio_chunk_count = 0
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@staticmethod
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def _calc_rms(pcm_data: bytes) -> float:
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"""Calculate RMS energy of 16-bit PCM audio."""
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if len(pcm_data) < 2:
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return 0.0
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def _resample_24k_to_16k(pcm_data: bytes) -> bytes:
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"""Downsample 16-bit PCM from 24kHz to 16kHz (ratio 3:2).
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Takes every 2 out of 3 samples (simple decimation).
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"""
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n_samples = len(pcm_data) // 2
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if n_samples == 0:
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return b''
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samples = struct.unpack(f'<{n_samples}h', pcm_data[:n_samples * 2])
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if not samples:
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return 0.0
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sum_sq = sum(s * s for s in samples)
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return (sum_sq / n_samples) ** 0.5
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# Pick samples at indices 0, 1.5, 3, 4.5, ... -> floor(i * 3/2) for output index i
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out_len = (n_samples * 2) // 3
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resampled = []
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for i in range(out_len):
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src_idx = (i * 3) // 2
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if src_idx < n_samples:
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resampled.append(samples[src_idx])
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return struct.pack(f'<{len(resampled)}h', *resampled)
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def _webrtcvad_detect(self, pcm_data: bytes) -> bool:
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"""Run webrtcvad on audio data. Returns True if voice is detected in any frame."""
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resampled = self._resample_24k_to_16k(pcm_data)
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frame_size = (self.VAD_TARGET_RATE * self.VAD_FRAME_DURATION_MS // 1000) * 2 # bytes per frame
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if len(resampled) < frame_size:
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return False
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# Check frames; return True if any frame has voice
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voice_frames = 0
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total_frames = 0
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for offset in range(0, len(resampled) - frame_size + 1, frame_size):
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frame = resampled[offset:offset + frame_size]
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total_frames += 1
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try:
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if self._vad.is_speech(frame, self.VAD_TARGET_RATE):
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voice_frames += 1
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except Exception:
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pass
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# Consider voice detected if at least one frame has speech
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return voice_frames > 0
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async def handle_audio(self, audio_data: bytes) -> None:
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"""Forward user audio to ASR with VAD gating. Lazy-connect on speech start."""
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@ -129,8 +161,7 @@ class VoiceLiteSession:
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return
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self._audio_chunk_count += 1
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rms = self._calc_rms(audio_data)
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has_voice = rms > self.VAD_ENERGY_THRESHOLD
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has_voice = self._webrtcvad_detect(audio_data)
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now = asyncio.get_event_loop().time()
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if has_voice:
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@ -142,7 +173,7 @@ class VoiceLiteSession:
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if not self._vad_speaking:
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# Speech just started — connect ASR
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self._vad_speaking = True
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logger.info(f"[VoiceLite] VAD: speech started (rms={rms:.0f}), connecting ASR...")
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logger.info(f"[VoiceLite] VAD: speech started (webrtcvad), connecting ASR...")
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try:
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await self._connect_asr()
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# Send buffered pre-speech audio
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@ -320,8 +351,8 @@ class VoiceLiteSession:
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await self._emit_status("thinking")
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accumulated_text = []
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sentence_buf = ""
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tag_filter = StreamTagFilter()
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splitter = TTSSentenceSplitter()
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tts_client = StreamingTTSClient(speaker=self._speaker)
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speaking = False
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@ -340,26 +371,20 @@ class VoiceLiteSession:
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passthrough = tag_filter.feed(chunk)
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if not passthrough:
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if tag_filter.answer_ended and sentence_buf:
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flush = clean_markdown(sentence_buf.strip())
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sentence_buf = ""
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if flush:
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if not speaking:
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await self._emit_status("speaking")
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speaking = True
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await self._send_tts(tts_client, flush)
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if tag_filter.answer_ended:
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for sentence in splitter.flush():
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sentence = clean_markdown(sentence)
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if sentence:
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if not speaking:
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await self._emit_status("speaking")
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speaking = True
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await self._send_tts(tts_client, sentence)
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continue
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sentence_buf += passthrough
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while True:
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match = SENTENCE_END_RE.search(sentence_buf)
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if not match:
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break
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end_pos = match.end()
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sentence = clean_markdown(sentence_buf[:end_pos].strip())
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sentence_buf = sentence_buf[end_pos:]
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# Feed raw passthrough to splitter (preserve newlines for splitting),
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# apply clean_markdown on output sentences
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for sentence in splitter.feed(passthrough):
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sentence = clean_markdown(sentence)
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if sentence:
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if not speaking:
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await self._emit_status("speaking")
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@ -367,12 +392,13 @@ class VoiceLiteSession:
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await self._send_tts(tts_client, sentence)
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# Handle remaining text
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remaining = clean_markdown(sentence_buf.strip())
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if remaining:
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if not speaking:
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await self._emit_status("speaking")
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speaking = True
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await self._send_tts(tts_client, remaining)
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for sentence in splitter.flush():
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sentence = clean_markdown(sentence)
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if sentence:
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if not speaking:
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await self._emit_status("speaking")
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speaking = True
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await self._send_tts(tts_client, sentence)
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# Log full agent result (not sent to frontend, already streamed)
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full_result = "".join(accumulated_text)
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@ -7,6 +7,150 @@ logger = logging.getLogger('app')
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SENTENCE_END_RE = re.compile(r'[。！？；\n.!?;]')
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# Emoji pattern: matches Unicode emoji without touching CJK characters
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_EMOJI_RE = re.compile(
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"["
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"\U0001F600-\U0001F64F" # emoticons
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"\U0001F300-\U0001F5FF" # symbols & pictographs
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"\U0001F680-\U0001F6FF" # transport & map
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"\U0001F1E0-\U0001F1FF" # flags
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"\U0001F900-\U0001F9FF" # supplemental symbols
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"\U0001FA00-\U0001FA6F" # chess symbols
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"\U0001FA70-\U0001FAFF" # symbols extended-A
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"\U00002702-\U000027B0" # dingbats
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"\U00002600-\U000026FF" # misc symbols
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"\U0000FE00-\U0000FE0F" # variation selectors
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"\U0000200D" # zero width joiner
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"\U000024C2" # Ⓜ enclosed letter
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"\U00002B50\U00002B55" # star, circle
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"\U000023CF\U000023E9-\U000023F3\U000023F8-\U000023FA" # media controls
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"\U0001F170-\U0001F251" # enclosed alphanumeric supplement
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"]+",
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flags=re.UNICODE,
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)
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# Strong sentence-ending punctuation (excluding \n which is handled separately)
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_STRONG_PUNCT_RE = re.compile(r'[。！？；.!?;～~]')
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# Soft punctuation (usable as split points when buffer is getting long)
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_SOFT_PUNCT_RE = re.compile(r'[，,：:、）) \t]')
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class TTSSentenceSplitter:
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"""
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Intelligent sentence splitter for TTS streaming.
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Rules (in priority order):
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1. Split on newlines unconditionally (LLM paragraph boundaries)
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2. Split on strong punctuation (。！？～ etc.) only if accumulated >= MIN_LENGTH
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3. If buffer reaches SOFT_THRESHOLD, also split on soft punctuation (，、etc.)
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4. If buffer reaches MAX_LENGTH, force split at best available position
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- Strip emoji from output (TTS cannot pronounce them)
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- On flush(), return any remaining text regardless of length
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"""
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MIN_LENGTH = 10 # Don't send sentences shorter than this
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SOFT_THRESHOLD = 30 # Start considering soft punctuation splits
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MAX_LENGTH = 80 # Force split even without punctuation
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def __init__(self):
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self._buf = ""
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def _clean_for_tts(self, text: str) -> str:
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"""Remove emoji and collapse whitespace."""
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text = _EMOJI_RE.sub("", text)
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text = re.sub(r'[ \t]+', ' ', text)
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return text.strip()
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def feed(self, chunk: str) -> list[str]:
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"""Feed a text chunk, return list of ready sentences (may be empty)."""
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self._buf += chunk
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results = []
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while self._buf:
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buf_len = len(self._buf)
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# 0. Newline split — highest priority
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nl_pos = self._buf.find('\n')
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if nl_pos >= 0:
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before = self._buf[:nl_pos]
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rest = self._buf[nl_pos:].lstrip('\n')
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||||
cleaned = self._clean_for_tts(before)
|
||||
if len(cleaned) >= self.MIN_LENGTH:
|
||||
# Long enough, emit as a sentence
|
||||
self._buf = rest
|
||||
results.append(cleaned)
|
||||
continue
|
||||
elif not rest:
|
||||
# No more text after newline, keep buffer and wait
|
||||
break
|
||||
else:
|
||||
# Too short — merge with next paragraph
|
||||
self._buf = before + rest
|
||||
continue
|
||||
|
||||
# 1. Try strong punctuation split — scan for the best split point
|
||||
best_end = -1
|
||||
for match in _STRONG_PUNCT_RE.finditer(self._buf):
|
||||
end_pos = match.end()
|
||||
candidate = self._buf[:end_pos]
|
||||
if len(candidate.strip()) >= self.MIN_LENGTH:
|
||||
best_end = end_pos
|
||||
break # Take the first valid (long enough) split
|
||||
# Short segment before this punct — skip and keep scanning
|
||||
|
||||
if best_end > 0:
|
||||
sentence = self._clean_for_tts(self._buf[:best_end])
|
||||
self._buf = self._buf[best_end:]
|
||||
if sentence:
|
||||
results.append(sentence)
|
||||
continue
|
||||
|
||||
# 2. Buffer getting long: try soft punctuation split
|
||||
if buf_len >= self.SOFT_THRESHOLD:
|
||||
best_soft = -1
|
||||
for m in _SOFT_PUNCT_RE.finditer(self._buf):
|
||||
pos = m.end()
|
||||
if pos >= self.MIN_LENGTH:
|
||||
best_soft = pos
|
||||
if pos >= self.SOFT_THRESHOLD:
|
||||
break
|
||||
if best_soft >= self.MIN_LENGTH:
|
||||
sentence = self._clean_for_tts(self._buf[:best_soft])
|
||||
self._buf = self._buf[best_soft:]
|
||||
if sentence:
|
||||
results.append(sentence)
|
||||
continue
|
||||
|
||||
# 3. Buffer too long: force split at MAX_LENGTH
|
||||
if buf_len >= self.MAX_LENGTH:
|
||||
split_at = self.MAX_LENGTH
|
||||
search_region = self._buf[self.MIN_LENGTH:self.MAX_LENGTH]
|
||||
last_space = max(search_region.rfind(' '), search_region.rfind('，'),
|
||||
search_region.rfind(','), search_region.rfind('、'))
|
||||
if last_space >= 0:
|
||||
split_at = self.MIN_LENGTH + last_space + 1
|
||||
|
||||
sentence = self._clean_for_tts(self._buf[:split_at])
|
||||
self._buf = self._buf[split_at:]
|
||||
if sentence:
|
||||
results.append(sentence)
|
||||
continue
|
||||
|
||||
# Not enough text yet, wait for more
|
||||
break
|
||||
|
||||
return results
|
||||
|
||||
def flush(self) -> list[str]:
|
||||
"""Flush remaining buffer. Call at end of stream."""
|
||||
results = []
|
||||
if self._buf.strip():
|
||||
sentence = self._clean_for_tts(self._buf)
|
||||
if sentence:
|
||||
results.append(sentence)
|
||||
self._buf = ""
|
||||
return results
|
||||
|
||||
|
||||
class StreamTagFilter:
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user