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"""
Voice Activity Detector for Flare
==================================
Detects speech/silence in audio streams
"""
import struct
from typing import Tuple
from datetime import datetime
from utils.logger import log_debug, log_warning
class VoiceActivityDetector:
"""Detect speech and silence in audio stream"""
def __init__(self,
energy_threshold: float = 500,
silence_threshold_ms: int = 2000,
sample_rate: int = 16000):
"""
Initialize VAD
Args:
energy_threshold: RMS energy threshold for speech detection
silence_threshold_ms: Milliseconds of silence before considering speech ended
sample_rate: Audio sample rate
"""
self.energy_threshold = energy_threshold
self.silence_threshold_ms = silence_threshold_ms
self.sample_rate = sample_rate
# State tracking
self.is_speaking = False
self.silence_start: Optional[datetime] = None
self.speech_start: Optional[datetime] = None
self.last_speech_time: Optional[datetime] = None
# Statistics
self.total_speech_chunks = 0
self.total_silence_chunks = 0
def process_chunk(self, audio_chunk: bytes) -> Tuple[bool, int]:
"""
Process audio chunk and detect speech/silence
Args:
audio_chunk: Raw PCM audio data (LINEAR16)
Returns:
Tuple of (is_speech, silence_duration_ms)
"""
try:
# Calculate RMS energy
rms_energy = self._calculate_rms_energy(audio_chunk)
is_speech = rms_energy > self.energy_threshold
now = datetime.utcnow()
if is_speech:
# Speech detected
if not self.is_speaking:
# Speech just started
self.is_speaking = True
self.speech_start = now
log_debug(f"🎤 Speech started (RMS: {rms_energy})")
self.last_speech_time = now
self.silence_start = None
self.total_speech_chunks += 1
return True, 0
else:
# Silence detected
self.total_silence_chunks += 1
if self.is_speaking:
# Was speaking, now silent
if self.silence_start is None:
self.silence_start = now
log_debug(f"🔇 Silence started (RMS: {rms_energy})")
# Calculate silence duration
silence_duration = (now - self.silence_start).total_seconds() * 1000
if silence_duration >= self.silence_threshold_ms:
# Speech has ended
self.is_speaking = False
log_debug(f"💬 Speech ended after {silence_duration:.0f}ms of silence")
return False, int(silence_duration)
else:
# Already in silence
return False, 0
except Exception as e:
log_warning(f"VAD processing error: {e}")
# On error, assume speech to avoid cutting off
return True, 0
def _calculate_rms_energy(self, audio_chunk: bytes) -> float:
"""Calculate RMS energy of audio chunk"""
try:
# Handle empty or invalid chunk
if not audio_chunk or len(audio_chunk) < 2:
return 0.0
# Ensure even number of bytes for 16-bit audio
if len(audio_chunk) % 2 != 0:
audio_chunk = audio_chunk[:-1]
# Convert bytes to int16 samples
num_samples = len(audio_chunk) // 2
samples = struct.unpack(f'{num_samples}h', audio_chunk)
if not samples:
return 0.0
# Calculate RMS
sum_squares = sum(s * s for s in samples)
rms = (sum_squares / len(samples)) ** 0.5
return rms
except Exception as e:
log_warning(f"RMS calculation error: {e}")
return 0.0
def reset(self):
"""Reset VAD state"""
self.is_speaking = False
self.silence_start = None
self.speech_start = None
self.last_speech_time = None
log_debug("🔄 VAD state reset")
def get_speech_duration(self) -> float:
"""Get current speech duration in seconds"""
if self.speech_start and self.is_speaking:
return (datetime.utcnow() - self.speech_start).total_seconds()
return 0.0
def get_silence_duration(self) -> float:
"""Get current silence duration in seconds"""
if self.silence_start and not self.is_speaking:
return (datetime.utcnow() - self.silence_start).total_seconds()
return 0.0
def get_stats(self) -> dict:
"""Get VAD statistics"""
return {
"is_speaking": self.is_speaking,
"speech_chunks": self.total_speech_chunks,
"silence_chunks": self.total_silence_chunks,
"speech_duration": self.get_speech_duration(),
"silence_duration": self.get_silence_duration(),
"energy_threshold": self.energy_threshold,
"silence_threshold_ms": self.silence_threshold_ms
} |