[端到端模拟验收]：完成全双工fake集成验证，包含回声抑制、打断链路和安全指标测试
This commit is contained in:
mkbk
@@ -0,0 +1,169 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import tempfile
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
|
||||
from owner_voice_pet.agent_memory import FakeMemoryManager, MemoryRecordInput, SQLiteMemoryManager
|
||||
from owner_voice_pet.full_duplex_audio import FakeWebRtcAudioProcessingProvider, RenderReferenceRingBuffer
|
||||
from owner_voice_pet.full_duplex_control import CancellationGraph, FullDuplexStateMachine
|
||||
from owner_voice_pet.full_duplex_response import (
|
||||
FakeStreamingLlmProvider,
|
||||
FakeStreamingTtsProvider,
|
||||
InterruptiblePlaybackQueue,
|
||||
LlmStreamEvent,
|
||||
SentenceSegmenter,
|
||||
)
|
||||
from owner_voice_pet.full_duplex_speech import FakeStreamingSttProvider, FakeVadProvider, InterruptionDetector, TranscriptEvent
|
||||
from owner_voice_pet.full_duplex_testing import (
|
||||
PerformanceMetricRecorder,
|
||||
build_fake_full_duplex_audio_fixture,
|
||||
diagnostics_contain_sensitive_data,
|
||||
sanitize_diagnostics,
|
||||
)
|
||||
from owner_voice_pet.models import Message, PipelineState
|
||||
from owner_voice_pet.tool_router import MemorySearchTool, ToolCallRequest, ToolContext, ToolRouter
|
||||
|
||||
|
||||
class FullDuplexIntegrationTests(unittest.TestCase):
|
||||
def test_fake_apm_echo_does_not_trigger_interruption(self) -> None:
|
||||
fixture = build_fake_full_duplex_audio_fixture()
|
||||
apm = FakeWebRtcAudioProcessingProvider()
|
||||
detector = InterruptionDetector(vad=FakeVadProvider(), min_speech_ms=20)
|
||||
render = fixture[0]
|
||||
apm.process_render(render)
|
||||
|
||||
processed_echo = apm.process_capture(fixture[0])
|
||||
decision = detector.accept(
|
||||
processed_echo,
|
||||
state=PipelineState.SPEAKING,
|
||||
stt_events=[TranscriptEvent("stable_partial", "助手", is_stable=True)],
|
||||
)
|
||||
|
||||
self.assertTrue(processed_echo.metadata["echo_suppressed"])
|
||||
self.assertFalse(decision.interrupted)
|
||||
|
||||
def test_speaking_interruption_cancels_response_and_returns_to_listening(self) -> None:
|
||||
machine = FullDuplexStateMachine()
|
||||
graph = CancellationGraph("turn")
|
||||
detector = InterruptionDetector(vad=FakeVadProvider(), min_speech_ms=200)
|
||||
machine.transition(PipelineState.LISTENING, event_type="start")
|
||||
machine.transition(PipelineState.THINKING, event_type="final_transcript")
|
||||
machine.transition(PipelineState.SPEAKING, event_type="first_tts_chunk")
|
||||
|
||||
detector.accept(
|
||||
build_fake_full_duplex_audio_fixture()[1],
|
||||
state=PipelineState.SPEAKING,
|
||||
stt_events=[TranscriptEvent("partial", "你", is_stable=False)],
|
||||
)
|
||||
decision = detector.accept(
|
||||
build_fake_full_duplex_audio_fixture()[2],
|
||||
state=PipelineState.SPEAKING,
|
||||
stt_events=[TranscriptEvent("stable_partial", "你好", is_stable=True)],
|
||||
)
|
||||
if decision.interrupted:
|
||||
graph.cancel_all("user interrupted")
|
||||
machine.transition(PipelineState.INTERRUPTED, event_type="interrupt_detected")
|
||||
machine.transition(PipelineState.LISTENING, event_type="buffered_user_audio")
|
||||
|
||||
self.assertTrue(decision.interrupted)
|
||||
self.assertTrue(graph.root.cancelled)
|
||||
self.assertEqual(machine.current_state, PipelineState.LISTENING)
|
||||
|
||||
def test_streaming_stt_llm_tts_playback_order(self) -> None:
|
||||
stt = FakeStreamingSttProvider(
|
||||
scripted_events=[
|
||||
[TranscriptEvent("partial", "你", is_stable=False)],
|
||||
[TranscriptEvent("stable_partial", "你好", is_stable=True)],
|
||||
],
|
||||
final_text="你好",
|
||||
)
|
||||
stt_session = stt.start_session("turn")
|
||||
llm = FakeStreamingLlmProvider([LlmStreamEvent("delta", "你好。"), LlmStreamEvent("finish", finish_reason="stop")])
|
||||
segmenter = SentenceSegmenter()
|
||||
tts_session = FakeStreamingTtsProvider().start_stream(voice="default", sample_rate=16000)
|
||||
playback = InterruptiblePlaybackQueue()
|
||||
render = RenderReferenceRingBuffer(capacity_ms=1000)
|
||||
graph = CancellationGraph("turn")
|
||||
event_order: list[str] = []
|
||||
|
||||
for frame in build_fake_full_duplex_audio_fixture()[1:3]:
|
||||
for event in stt_session.accept_audio(frame):
|
||||
event_order.append(event.kind)
|
||||
final = stt_session.finish()
|
||||
event_order.append(final.kind)
|
||||
for llm_event in llm.stream([Message("user", final.text, 1.0)], cancellation=graph.root):
|
||||
event_order.append(f"llm_{llm_event.kind}")
|
||||
if llm_event.text_delta:
|
||||
for sentence in segmenter.accept_delta(llm_event.text_delta):
|
||||
event_order.append("sentence")
|
||||
frames = tts_session.accept_text(sentence)
|
||||
event_order.append("tts")
|
||||
playback.enqueue(sentence, frames)
|
||||
result = playback.play_next(render_reference=render, cancellation=graph.root)
|
||||
event_order.append("playback")
|
||||
|
||||
self.assertEqual(
|
||||
event_order,
|
||||
["partial", "stable_partial", "final", "llm_delta", "sentence", "tts", "llm_finish", "playback"],
|
||||
)
|
||||
self.assertFalse(result.interrupted)
|
||||
self.assertEqual(playback.spoken.text, "你好。")
|
||||
self.assertEqual(render.frame_count, 1)
|
||||
|
||||
def test_memory_restart_and_tool_search_integration(self) -> None:
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
db_path = Path(tmp) / "memory.sqlite3"
|
||||
SQLiteMemoryManager(db_path).save(MemoryRecordInput("preference", "用户喜欢 Python"))
|
||||
restarted = SQLiteMemoryManager(db_path)
|
||||
router = ToolRouter({"memory.search": MemorySearchTool()})
|
||||
request = ToolCallRequest("1", "memory.search", {"query": "Python"}, "turn")
|
||||
decision = router.route(request, ToolContext(memory=restarted))
|
||||
result = router.execute(request, decision, ToolContext(memory=restarted))
|
||||
|
||||
self.assertEqual(result.status, "success")
|
||||
self.assertIn("用户喜欢 Python", result.output_text)
|
||||
|
||||
def test_tool_router_security_blocks_high_risk_fake_integration(self) -> None:
|
||||
router = ToolRouter({"memory.search": MemorySearchTool()})
|
||||
request = ToolCallRequest(
|
||||
"1",
|
||||
"memory.search",
|
||||
{"query": "账号"},
|
||||
"turn",
|
||||
natural_language_intent="上传账号资料",
|
||||
)
|
||||
|
||||
decision = router.route(request, ToolContext(memory=FakeMemoryManager()))
|
||||
|
||||
self.assertEqual(decision.action, "require_confirmation")
|
||||
self.assertEqual(decision.risk_level, "high")
|
||||
|
||||
def test_performance_metrics_are_sanitized(self) -> None:
|
||||
recorder = PerformanceMetricRecorder()
|
||||
recorder.record(
|
||||
"interrupt_latency",
|
||||
started_at_ms=100,
|
||||
finished_at_ms=250,
|
||||
payload={"api_key": "secret", "preview": "tp-" + "abcdefghijklmnop"},
|
||||
)
|
||||
|
||||
self.assertEqual(recorder.summary()["interrupt_latency"], 150)
|
||||
self.assertEqual(recorder.metrics[0].payload["api_key"], "[redacted]")
|
||||
self.assertFalse(diagnostics_contain_sensitive_data(recorder.metrics[0].payload))
|
||||
|
||||
def test_sanitize_diagnostics_removes_nested_sensitive_values(self) -> None:
|
||||
sanitized = sanitize_diagnostics(
|
||||
{
|
||||
"nested": {"authorization": "Bearer secret", "raw_audio": b"bytes"},
|
||||
"text": "normal",
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(sanitized["nested"]["authorization"], "[redacted]")
|
||||
self.assertEqual(sanitized["nested"]["raw_audio"], "[redacted]")
|
||||
self.assertFalse(diagnostics_contain_sensitive_data(sanitized))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user