[唤醒应答与快速端点]：完成唤醒后我在播报和本地VAD端点优化，包含缓冲清理、配置项和测试覆盖

2026-06-17 20:51:14 +08:00
parent 860c2909fc
commit 44f708a3dc
12 changed files with 224 additions and 7 deletions
@@ -10,6 +10,14 @@ OWNER_WAKE_PROVIDER=local_kws
 OWNER_WAKE_KEYWORDS_FILE=
 OWNER_WAKE_KWS_THRESHOLD=0.25
 OWNER_WAKE_KWS_SCORE=1.0
+OWNER_WAKE_ACK_TEXT=我在
+OWNER_POST_PLAYBACK_DRAIN_MS=250
+OWNER_VAD_PROVIDER=local
+OWNER_VAD_THRESHOLD=0.5
+OWNER_VAD_MIN_DURATION_MS=250
+OWNER_VAD_END_SILENCE_MS=350
+OWNER_VAD_NO_SPEECH_TIMEOUT_MS=5000
+OWNER_VAD_MAX_RECORDING_MS=12000
 OWNER_SPEECH_PROVIDER=cloud
 OWNER_ASR_MODEL=mimo-v2.5-asr
 OWNER_TTS_MODEL=mimo-v2.5-tts
@@ -13,7 +13,7 @@
 - `OWNER_SPEECH_PROVIDER=cloud|local`：选择云端语音模型或本地语音模型。
 - 默认云端语音模型：`mimo-v2.5-asr`、`mimo-v2.5-tts`。
 - 本地设备：`sounddevice` 读取麦克风，扬声器或 `afplay` 播放。
- 本地模型：`models/` 存放 `sherpa-onnx` VAD/STT 模型，目录不提交 Git。
+- 本地模型：`models/` 存放 `sherpa-onnx` wake/VAD/STT 模型，目录不提交 Git。
 - 临时上下文：同一次 `run-live` 进程内携带最近 user/assistant 历史，退出即清空。

 ## 首次准备
@@ -36,13 +36,21 @@ OWNER_WAKE_PROVIDER=local_kws
 OWNER_WAKE_KEYWORDS_FILE=
 OWNER_WAKE_KWS_THRESHOLD=0.25
 OWNER_WAKE_KWS_SCORE=1.0
+OWNER_WAKE_ACK_TEXT=我在
+OWNER_POST_PLAYBACK_DRAIN_MS=250
+OWNER_VAD_PROVIDER=local
+OWNER_VAD_THRESHOLD=0.5
+OWNER_VAD_MIN_DURATION_MS=250
+OWNER_VAD_END_SILENCE_MS=350
+OWNER_VAD_NO_SPEECH_TIMEOUT_MS=5000
+OWNER_VAD_MAX_RECORDING_MS=12000
 OWNER_SPEECH_PROVIDER=cloud
 OWNER_ASR_MODEL=mimo-v2.5-asr
 OWNER_TTS_MODEL=mimo-v2.5-tts
 OWNER_TTS_VOICE=mimo_default
 ```

-`OWNER_WAKE_PROVIDER=local_kws` 表示唤醒词“小杰小杰”由本地模型检测。`OWNER_SPEECH_PROVIDER=cloud` 只影响唤醒后的正式问题 ASR/TTS：它会把 VAD 切出来的用户问题片段发送到云端 ASR，不会上传连续麦克风流，也不会用云端判断唤醒。改成 `local` 时使用项目 `models/` 下的本地语音模型路径。
+`OWNER_WAKE_PROVIDER=local_kws` 表示唤醒词“小杰小杰”由本地模型检测。唤醒命中后会先本地播报 `OWNER_WAKE_ACK_TEXT=我在`，再开始听取问题。`OWNER_SPEECH_PROVIDER=cloud` 只影响唤醒后的正式问题 ASR/TTS：它会把 VAD 切出来的用户问题片段发送到云端 ASR，不会上传连续麦克风流，也不会用云端判断唤醒。改成 `local` 时使用项目 `models/` 下的本地语音模型路径。

 ## 本地模型

@@ -57,6 +65,8 @@ python3.11 scripts/download_speech_models.py --dir models

 本地唤醒关键词文件位于 `models/wake/keywords.txt`。如果真人唤醒不灵敏，可以先把 `.env` 中 `OWNER_WAKE_KWS_THRESHOLD` 调低，例如 `0.15`，再重新运行。

+如果唤醒后你已经停说但还长时间显示“录音中”，优先调小 `OWNER_VAD_END_SILENCE_MS`，例如 `250`；如果房间噪声较大，再略调高 `OWNER_VAD_THRESHOLD`，例如 `0.6`。
+
 ## 设备检查

 ```bash
@@ -11,6 +11,28 @@ The live runtime SHALL display the recognized user utterance text in the termina
 - **WHEN** STT returns empty text, punctuation-only text, or an invalid transcript
 - **THEN** the runtime SHALL NOT emit a misleading transcript as valid user input and SHALL recover to standby without invoking the LLM

+### Requirement: Wake acknowledgement before recording
+The live runtime SHALL provide an audible local acknowledgement after local wake detection and before it starts recording the user's formal question.
+
+#### Scenario: Wake is detected
+- **WHEN** the local wakeword provider detects “小杰小杰”
+- **THEN** the runtime SHALL play a short acknowledgement such as “我在” before entering the user utterance recording state
+
+#### Scenario: Acknowledgement playback finishes
+- **WHEN** the acknowledgement playback completes
+- **THEN** the runtime SHALL clear buffered microphone input captured during acknowledgement playback before starting VAD recording for the user's question
+
+### Requirement: Fast user utterance endpointing
+The live runtime SHALL use the project-local VAD model by default for user utterance endpoint detection and SHALL expose configurable silence timing so the recording stops promptly after the user stops speaking.
+
+#### Scenario: User stops speaking after wake
+- **WHEN** VAD observes the configured continuous silence duration after a started utterance
+- **THEN** the runtime SHALL close the utterance segment and proceed to STT without waiting for the maximum recording duration
+
+#### Scenario: Local VAD model is available
+- **WHEN** `OWNER_VAD_PROVIDER=local`
+- **THEN** the runtime SHALL use the project-local `sherpa-onnx` VAD model rather than a raw energy threshold for live user utterance endpointing
+
 ## MODIFIED Requirements

 ### Requirement: Wake word detection
@@ -56,7 +78,7 @@ The system SHALL provide a `run-live` command that performs real repeated voice

 #### Scenario: Live runtime completes two turns
 - **WHEN** the user wakes the system with “小杰小杰”, asks a question, hears the reply, then wakes it again and asks another question
- **THEN** the system SHALL complete local wake, recording, STT, transcript display, LLM, TTS, playback for both turns and SHALL return to standby after each turn
+- **THEN** the system SHALL complete local wake, audible acknowledgement, recording, STT, transcript display, LLM, TTS, playback for both turns and SHALL return to standby after each turn

 #### Scenario: Once mode completes one turn
 - **WHEN** the user runs `PYTHONPATH=src python3.11 -m owner_voice_pet run-live --once`
@@ -31,3 +31,10 @@
 - [ ] 4.3 执行真实 run-live 验收；前置条件：模型、设备、.env 齐全；验收标准：唤醒命中使用本地模型，终端显示转写结果；测试要点：单轮或两轮状态输出；优先级：P0；预计：60 分钟。
 - [x] 4.4 最终门禁；前置条件：全部实现完成；验收标准：compileall、unittest、security-check、model-check、device-check、OpenSpec strict 全通过；优先级：P0；预计：30 分钟。
 - [ ] 4.5 归档变更并提交；前置条件：4.4 通过；验收标准：主 spec 更新，archive 完成，最终 commit，`git status --short` 为空；测试要点：中文提交信息；优先级：P0；预计：20 分钟。
+
+## 5. 唤醒应答与快速端点修正
+
+- [x] 5.1 增加唤醒后本地语音应答；前置条件：本地 KWS 已可唤醒；验收标准：wake 命中后播放“我在”再进入录音；测试要点：fake runtime 播放顺序；优先级：P0；预计：45 分钟。
+- [x] 5.2 清理应答播放期间的麦克风缓冲；前置条件：5.1 完成；验收标准：应答音频不进入正式问题 VAD/STT；测试要点：transport flush 测试；优先级：P0；预计：45 分钟。
+- [x] 5.3 live 默认改用本地 `sherpa-onnx` VAD 并缩短静音端点；前置条件：模型已下载；验收标准：`.env` 可配置 VAD provider、静音结束时间和最大录音时长；测试要点：配置和 runtime 构造测试；优先级：P0；预计：45 分钟。
+- [x] 5.4 验证并提交“唤醒应答与快速端点”模块；前置条件：5.1 至 5.3 完成；验收标准：compileall、unittest、security-check、model-check、OpenSpec strict 通过后 commit；优先级：P0；预计：20 分钟。
@@ -57,6 +57,14 @@ def main(argv: list[str] | None = None) -> int:
                    "wake_keywords_file": str(config.wake_keywords_file) if config.wake_keywords_file else "",
                    "wake_kws_threshold": config.wake_kws_threshold,
                    "wake_kws_score": config.wake_kws_score,
+                    "wake_ack_text": config.wake_ack_text,
+                    "post_playback_drain_ms": config.post_playback_drain_ms,
+                    "vad_provider": config.vad_provider,
+                    "vad_threshold": config.vad_threshold,
+                    "vad_min_duration_ms": config.vad_min_duration_ms,
+                    "vad_end_silence_ms": config.vad_end_silence_ms,
+                    "vad_no_speech_timeout_ms": config.vad_no_speech_timeout_ms,
+                    "vad_max_recording_ms": config.vad_max_recording_ms,
                    "speech_provider": config.speech_provider,
                    "asr_model": config.asr_model,
                    "tts_model": config.tts_model,
@@ -144,6 +152,14 @@ def main(argv: list[str] | None = None) -> int:
                wake_keywords_file=config.wake_keywords_file,
                wake_kws_threshold=config.wake_kws_threshold,
                wake_kws_score=config.wake_kws_score,
+                wake_ack_text=config.wake_ack_text,
+                post_playback_drain_ms=config.post_playback_drain_ms,
+                vad_provider=config.vad_provider,
+                vad_threshold=config.vad_threshold,
+                vad_min_duration_ms=config.vad_min_duration_ms,
+                vad_end_silence_ms=config.vad_end_silence_ms,
+                vad_no_speech_timeout_ms=config.vad_no_speech_timeout_ms,
+                vad_max_recording_ms=config.vad_max_recording_ms,
                speech_provider=config.speech_provider,
                asr_model=config.asr_model,
                tts_model=config.tts_model,
@@ -24,6 +24,14 @@ class AppConfig:
    wake_keywords_file: Path | None = None
    wake_kws_threshold: float = 0.25
    wake_kws_score: float = 1.0
+    wake_ack_text: str = "我在"
+    post_playback_drain_ms: int = 250
+    vad_provider: str = "local"
+    vad_threshold: float = 0.5
+    vad_min_duration_ms: int = 250
+    vad_end_silence_ms: int = 350
+    vad_no_speech_timeout_ms: int = 5000
+    vad_max_recording_ms: int = 12000
    speech_provider: str = "cloud"
    asr_model: str = "mimo-v2.5-asr"
    tts_model: str = "mimo-v2.5-tts"
@@ -57,6 +65,14 @@ class AppConfig:
            wake_keywords_file=Path(value) if (value := get("WAKE_KEYWORDS_FILE")) else None,
            wake_kws_threshold=float(get("WAKE_KWS_THRESHOLD", "0.25") or "0.25"),
            wake_kws_score=float(get("WAKE_KWS_SCORE", "1.0") or "1.0"),
+            wake_ack_text=get("WAKE_ACK_TEXT", "我在") or "我在",
+            post_playback_drain_ms=int(get("POST_PLAYBACK_DRAIN_MS", "250") or "250"),
+            vad_provider=(get("VAD_PROVIDER", "local") or "local").lower(),
+            vad_threshold=float(get("VAD_THRESHOLD", "0.5") or "0.5"),
+            vad_min_duration_ms=int(get("VAD_MIN_DURATION_MS", "250") or "250"),
+            vad_end_silence_ms=int(get("VAD_END_SILENCE_MS", "350") or "350"),
+            vad_no_speech_timeout_ms=int(get("VAD_NO_SPEECH_TIMEOUT_MS", "5000") or "5000"),
+            vad_max_recording_ms=int(get("VAD_MAX_RECORDING_MS", "12000") or "12000"),
            speech_provider=(get("SPEECH_PROVIDER", "cloud") or "cloud").lower(),
            asr_model=get("ASR_MODEL", "mimo-v2.5-asr") or "mimo-v2.5-asr",
            tts_model=get("TTS_MODEL", "mimo-v2.5-tts") or "mimo-v2.5-tts",
@@ -152,6 +168,52 @@ class AppConfig:
                    "startup",
                )
            )
+        if self.post_playback_drain_ms < 0:
+            errors.append(
+                ProviderError(
+                    ErrorCode.CONFIG_MISSING_VALUE,
+                    "OWNER_POST_PLAYBACK_DRAIN_MS must be non-negative",
+                    False,
+                    "config",
+                    "startup",
+                )
+            )
+        if self.vad_provider not in {"local", "energy"}:
+            errors.append(
+                ProviderError(
+                    ErrorCode.CONFIG_MISSING_VALUE,
+                    "OWNER_VAD_PROVIDER must be local or energy",
+                    False,
+                    "config",
+                    "startup",
+                )
+            )
+        if self.vad_threshold <= 0:
+            errors.append(
+                ProviderError(
+                    ErrorCode.CONFIG_MISSING_VALUE,
+                    "OWNER_VAD_THRESHOLD must be positive",
+                    False,
+                    "config",
+                    "startup",
+                )
+            )
+        for name, value in {
+            "OWNER_VAD_MIN_DURATION_MS": self.vad_min_duration_ms,
+            "OWNER_VAD_END_SILENCE_MS": self.vad_end_silence_ms,
+            "OWNER_VAD_NO_SPEECH_TIMEOUT_MS": self.vad_no_speech_timeout_ms,
+            "OWNER_VAD_MAX_RECORDING_MS": self.vad_max_recording_ms,
+        }.items():
+            if value <= 0:
+                errors.append(
+                    ProviderError(
+                        ErrorCode.CONFIG_MISSING_VALUE,
+                        f"{name} must be positive",
+                        False,
+                        "config",
+                        "startup",
+                    )
+                )
        if not self.llm_base_url.startswith(("http://", "https://")):
            errors.append(
                ProviderError(
@@ -28,6 +28,9 @@ class AudioTransport(Protocol):
    def play_pcm(self, segment: AudioSegment, interrupt: bool = False) -> PlaybackResult:
        ...

+    def flush_input(self) -> int:
+        ...
+
    def stop(self) -> None:
        ...

@@ -12,7 +12,7 @@ from .protocols import AudioTransport, LlmProvider, SttProvider, TtsProvider, Wa
 from .stt import CloudAsrSttProvider, SherpaOnnxSttProvider, is_valid_transcript_text
 from .transport import SoundDeviceAudioTransport
 from .tts import CloudTtsProvider, MacSayTtsProvider, SentenceBuffer
-from .vad import EnergyVadProvider, VadRecorder
+from .vad import EnergyVadProvider, SherpaOnnxVadProvider, VadRecorder
 from .wakeword import SherpaOnnxKeywordWakeWordProvider


@@ -71,6 +71,7 @@ class LiveVoiceRuntime:
        llm: LlmProvider,
        tts: TtsProvider,
        context: ConversationContext,
+        ack_tts: TtsProvider | None = None,
        reporter: RuntimeReporter | None = None,
        sentence_buffer: SentenceBuffer | None = None,
    ) -> None:
@@ -81,6 +82,7 @@ class LiveVoiceRuntime:
        self.stt = stt
        self.llm = llm
        self.tts = tts
+        self.ack_tts = ack_tts or tts
        self.context = context
        self.reporter = reporter or TerminalRuntimeReporter()
        self.sentence_buffer = sentence_buffer or SentenceBuffer()
@@ -91,6 +93,8 @@ class LiveVoiceRuntime:
        self.vad_recorder.provider.load()
        self.stt.load()
        self.tts.load()
+        if self.ack_tts is not self.tts:
+            self.ack_tts.load()

    def run(self, *, once: bool = False, max_turns: int | None = None) -> RuntimeSummary:
        completed = 0
@@ -142,6 +146,9 @@ class LiveVoiceRuntime:
        if wake_error is not None:
            return wake_error
        self._state(PipelineState.SPEECH_DETECTING, "唤醒命中：请说出问题", turn_id=turn_id)
+        ack_error = self._acknowledge_wake(turn_id)
+        if ack_error is not None:
+            return ack_error
        user_segment = self._capture_segment(turn_id, state_message="录音中：正在听取问题")
        if isinstance(user_segment, ProviderError):
            return user_segment
@@ -186,6 +193,33 @@ class LiveVoiceRuntime:
                if isinstance(result, AudioSegment):
                    return result

+    def _acknowledge_wake(self, turn_id: int) -> ProviderError | None:
+        text = self.config.wake_ack_text.strip()
+        if not text:
+            self._drain_input_after_playback()
+            return None
+        try:
+            self._state(PipelineState.SPEAKING, f"应答中：{text}", turn_id=turn_id)
+            segment = self.ack_tts.synthesize(text)
+            playback = self.transport.play_pcm(segment)
+            if playback.error:
+                return playback.error
+            self._drain_input_after_playback()
+            return None
+        except ProviderError as exc:
+            return exc
+
+    def _drain_input_after_playback(self) -> None:
+        if self.config.post_playback_drain_ms <= 0:
+            return
+        self.transport.flush_input()
+        remaining_ms = self.config.post_playback_drain_ms
+        while remaining_ms > 0:
+            timeout_ms = min(50, remaining_ms)
+            self.transport.read_frames(timeout_ms=timeout_ms)
+            remaining_ms -= timeout_ms
+        self.transport.flush_input()
+
    def _reply_to_user(self, user_text: str, turn_id: int) -> TurnResult:
        self.context.append_user(user_text)
        self._state(PipelineState.THINKING, "思考中：正在生成回复", turn_id=turn_id)
@@ -220,6 +254,7 @@ class LiveVoiceRuntime:
        playback = self.transport.play_pcm(segment)
        if playback.error:
            raise playback.error
+        self._drain_input_after_playback()

    def _recover(self, error: ProviderError, turn_id: int) -> TurnResult:
        self.reporter.error(error.stage, error.code.value, error.message, turn_id=turn_id)
@@ -242,6 +277,10 @@ def build_live_runtime(config: AppConfig, reporter: RuntimeReporter | None = Non
    else:
        stt = SherpaOnnxSttProvider(str(config.speech_models_dir))
        tts = MacSayTtsProvider()
+    if config.vad_provider == "local":
+        vad_provider = SherpaOnnxVadProvider(config.speech_models_dir, threshold=config.vad_threshold)
+    else:
+        vad_provider = EnergyVadProvider()
    return LiveVoiceRuntime(
        config=config,
        transport=SoundDeviceAudioTransport(output_device=config.audio_output_device),
@@ -252,10 +291,17 @@ def build_live_runtime(config: AppConfig, reporter: RuntimeReporter | None = Non
            threshold=config.wake_kws_threshold,
            score=config.wake_kws_score,
        ),
-        vad_recorder=VadRecorder(EnergyVadProvider(), min_duration_ms=300, end_silence_ms=500, no_speech_timeout_ms=8000),
+        vad_recorder=VadRecorder(
+            vad_provider,
+            min_duration_ms=config.vad_min_duration_ms,
+            end_silence_ms=config.vad_end_silence_ms,
+            no_speech_timeout_ms=config.vad_no_speech_timeout_ms,
+            max_recording_ms=config.vad_max_recording_ms,
+        ),
        stt=stt,
        llm=OpenAICompatibleLlmProvider(config),
        tts=tts,
+        ack_tts=MacSayTtsProvider(),
        context=ConversationContext(
            max_messages=config.context_max_messages,
            max_chars=config.context_max_chars,
@@ -103,6 +103,11 @@ class MemoryAudioTransport:
        self.played_segments.append(segment)
        return PlaybackResult(True, segment.duration_ms)

+    def flush_input(self) -> int:
+        count = len(self._frames)
+        self._frames.clear()
+        return count
+
    def stop(self) -> None:
        self.started = False

@@ -290,6 +295,16 @@ class SoundDeviceAudioTransport:
                break
        return None

+    def flush_input(self) -> int:
+        count = 0
+        while not self._queue.empty():
+            try:
+                self._queue.get_nowait()
+                count += 1
+            except queue.Empty:
+                break
+        return count
+
    def health(self) -> TransportHealth:
        if self._sd is None:
            return TransportHealth(False, False, "sounddevice unavailable")
@@ -80,7 +80,7 @@ def make_runtime(texts: list[str], context: ConversationContext | None = None) -
    tts = SineTtsProvider()
    reporter = RecordingReporter()
    runtime = LiveVoiceRuntime(
-        config=AppConfig(llm_api_key="secret", speech_provider="cloud"),
+        config=AppConfig(llm_api_key="secret", speech_provider="cloud", post_playback_drain_ms=0),
        transport=transport,
        wakeword=KeywordWakeWordProvider(),
        vad_recorder=VadRecorder(EnergyVadProvider(), min_duration_ms=40, end_silence_ms=40),
@@ -101,8 +101,9 @@ class LiveRuntimeTests(unittest.TestCase):
        self.assertEqual(summary.completed_turns, 2)
        self.assertEqual(len(stt.calls), 2)
        self.assertEqual(len(llm.calls), 2)
-        self.assertEqual(len(transport.played_segments), 2)
+        self.assertEqual(len(transport.played_segments), 4)
        self.assertEqual(reporter.transcripts, ["第一问", "第二问"])
+        self.assertIn("应答中：我在", reporter.statuses)
        self.assertIn("恢复待机：可继续唤醒", reporter.statuses[-1])

    def test_temporary_context_is_sent_to_second_llm_call(self) -> None:
@@ -72,6 +72,14 @@ class ModelsConfigTests(unittest.TestCase):
            self.assertEqual(config.wake_provider, "local_kws")
            self.assertEqual(config.wake_kws_threshold, 0.25)
            self.assertEqual(config.wake_kws_score, 1.0)
+            self.assertEqual(config.wake_ack_text, "我在")
+            self.assertEqual(config.post_playback_drain_ms, 250)
+            self.assertEqual(config.vad_provider, "local")
+            self.assertEqual(config.vad_threshold, 0.5)
+            self.assertEqual(config.vad_min_duration_ms, 250)
+            self.assertEqual(config.vad_end_silence_ms, 350)
+            self.assertEqual(config.vad_no_speech_timeout_ms, 5000)
+            self.assertEqual(config.vad_max_recording_ms, 12000)
            self.assertEqual(config.speech_provider, "cloud")
            self.assertEqual(config.asr_model, "mimo-v2.5-asr")
            self.assertEqual(config.tts_model, "mimo-v2.5-tts")
@@ -90,6 +98,11 @@ class ModelsConfigTests(unittest.TestCase):
        errors = config.validate_basic()
        self.assertTrue(any("OWNER_WAKE_PROVIDER" in error.message for error in errors))

+    def test_vad_provider_must_be_local_or_energy(self) -> None:
+        config = AppConfig(vad_provider="invalid")
+        errors = config.validate_basic()
+        self.assertTrue(any("OWNER_VAD_PROVIDER" in error.message for error in errors))
+
    def test_missing_dotenv_uses_non_secret_defaults(self) -> None:
        config = AppConfig.from_dotenv("/tmp/owner-voice-pet-missing.env")
        self.assertEqual(config.llm_base_url, "https://token-plan-cn.xiaomimimo.com/v1")
@@ -42,6 +42,12 @@ class TransportTests(unittest.TestCase):
        self.assertFalse(result.played)
        self.assertEqual(result.error.code, ErrorCode.AUDIO_OUTPUT_DEVICE_MISSING)

+    def test_memory_transport_flushes_input_frames(self) -> None:
+        transport = MemoryAudioTransport([frame(1, 0), frame(2, 20)])
+        transport.start_input()
+        self.assertEqual(transport.flush_input(), 2)
+        self.assertEqual(transport.read_frames(10), [])
+
    def test_file_replay_roundtrip(self) -> None:
        frames = [frame(1, 0, {"wake": True}), frame(2, 20, {"speech": True})]
        with tempfile.TemporaryDirectory() as tmp:
@@ -76,6 +82,14 @@ class TransportTests(unittest.TestCase):
        self.assertEqual(frames[0].pcm, b"\x01\x00\x02\x00")
        self.assertEqual(frames[0].sample_rate, 16000)

+    def test_sounddevice_transport_flushes_queued_input(self) -> None:
+        fake = FakeSoundDevice()
+        transport = SoundDeviceAudioTransport(sounddevice_module=fake)
+        transport.start_input(sample_rate=16000, channels=1)
+        self.assertEqual(transport.flush_input(), 1)
+        self.assertEqual(transport.read_frames(0), [])
+        transport.stop()
+
    def test_sounddevice_transport_plays_pcm_to_raw_output_stream(self) -> None:
        fake = FakeSoundDevice()
        transport = SoundDeviceAudioTransport(sounddevice_module=fake)