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Changing the start speech time and adding logs.

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Filipi Fuchter
2025-04-16 07:55:20 -03:00
parent 2627cb6bf2
commit 650d4d9ee2
1 changed files with 8 additions and 6 deletions
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14
src/pipecat/audio/turn/base_smart_turn.py
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@@ -21,7 +21,7 @@ class EndOfTurnState(Enum):
STOP_SECS = 1
PRE_SPEECH_MS = 200
PRE_SPEECH_MS = 0
MAX_DURATION_SECONDS = 8 # Maximum duration for the smart turn model
@@ -32,7 +32,9 @@ class SmartTurnParams(BaseModel):
class BaseSmartTurn(ABC):
def __init__(self, *, sample_rate: Optional[int] = None, params: SmartTurnParams = SmartTurnParams()):
def __init__(
self, *, sample_rate: Optional[int] = None, params: SmartTurnParams = SmartTurnParams()
):
self._init_sample_rate = sample_rate
self._params = params
# settings variables
@@ -63,6 +65,7 @@ class BaseSmartTurn(ABC):
audio_int16 = np.frombuffer(buffer, dtype=np.int16)
# Divide by 32768 because we have signed 16-bit data.
audio_float32 = np.frombuffer(audio_int16, dtype=np.int16).astype(np.float32) / 32768.0
self._audio_buffer.append((time.time(), audio_float32))
state = EndOfTurnState.INCOMPLETE
if is_speech:
@@ -70,18 +73,15 @@ class BaseSmartTurn(ABC):
self._speech_triggered = True
if self._speech_start_time is None:
self._speech_start_time = time.time()
self._audio_buffer.append((time.time(), audio_float32))
logger.debug(f"Speech started at {self._speech_start_time}")
else:
if self._speech_triggered:
self._audio_buffer.append((time.time(), audio_float32))
self._silence_frames += 1
if self._silence_frames * self._chunk_size_ms >= self._stop_ms:
logger.debug("End of Turn complete due to stop_secs.")
state = EndOfTurnState.COMPLETE
self._clear()
else:
# Keep buffering some silence before potential speech starts
self._audio_buffer.append((time.time(), audio_float32))
# Keep the buffer size reasonable, assuming CHUNK is small
max_buffer_time = (
self._params.pre_speech_ms + self._stop_ms
@@ -103,6 +103,7 @@ class BaseSmartTurn(ABC):
return state
def _clear(self):
logger.debug("Clearing audio buffer...")
self._speech_triggered = False
self._audio_buffer = []
self._speech_start_time = None
@@ -156,6 +157,7 @@ class BaseSmartTurn(ABC):
logger.debug(f"Probability of complete: {result['probability']:.4f}")
logger.debug(f"Prediction took {(end_time - start_time) * 1000:.2f}ms seconds")
else:
logger.debug(f"params: {self._params}, stop_ms: {self._stop_ms}")
logger.debug("Captured empty audio segment, skipping prediction.")
return state