File size: 7,303 Bytes
c863b63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 | import numpy as np
from typing import Optional, Tuple
from dataclasses import dataclass
from ..config import settings, BUFFER_SAMPLES, WINDOW_SAMPLES
from .vad import VoiceActivityDetector, VADResult
from .buffer import RingBuffer
@dataclass
class ProcessedChunk:
audio: np.ndarray # Preprocessed audio (float32, normalized)
original_audio: np.ndarray # Original int16/float32 audio
vad_result: VADResult
timestamp_ms: int
chunk_index: int
window_id: int = 0 # Monotonic id of the model input window
class AudioProcessor:
"""
Audio preprocessing pipeline:
1. Convert int16 to float32 normalized [-1, 1]
2. Resample to 16kHz if needed
3. Voice Activity Detection
4. Normalization (peak/RMS)
5. Optional: High-pass filter for DC removal
"""
def __init__(
self,
target_sr: int = settings.sample_rate,
normalize: bool = True,
highpass_cutoff: float = 80.0,
vad: Optional[VoiceActivityDetector] = None,
):
self.target_sr = target_sr
self.normalize = normalize
self.highpass_cutoff = highpass_cutoff
self.vad = vad or VoiceActivityDetector()
self._chunk_index = 0
self._window_id = 0
self._highpass_state = None
def _int16_to_float32(self, audio: np.ndarray) -> np.ndarray:
"""Convert int16 PCM to float32 [-1, 1]."""
if audio.dtype == np.int16:
return audio.astype(np.float32) / 32768.0
elif audio.dtype == np.float32:
return audio
elif audio.dtype == np.float64:
return audio.astype(np.float32)
else:
return audio.astype(np.float32) / np.iinfo(audio.dtype).max
def _resample(self, audio: np.ndarray, orig_sr: int) -> np.ndarray:
"""Resample audio to target sample rate using linear interpolation."""
if orig_sr == self.target_sr:
return audio
n_out = int(len(audio) * self.target_sr / orig_sr)
if n_out == 0:
return np.zeros(0, dtype=audio.dtype)
# Linear interpolation
x_old = np.linspace(0.0, 1.0, len(audio), endpoint=False)
x_new = np.linspace(0.0, 1.0, n_out, endpoint=False)
resampled = np.interp(x_new, x_old, audio)
return resampled.astype(np.float32, copy=False)
def _highpass_filter(self, audio: np.ndarray) -> np.ndarray:
"""Apply high-pass filter for DC removal (1st order IIR)."""
if self.highpass_cutoff <= 0:
return audio
# 1st order high-pass: y[n] = alpha * (y[n-1] + x[n] - x[n-1])
# alpha = exp(-2*pi*fc/fs)
alpha = np.exp(-2 * np.pi * self.highpass_cutoff / self.target_sr)
if self._highpass_state is None:
self._highpass_state = np.zeros(2, dtype=np.float32) # [y_prev, x_prev]
y_prev, x_prev = self._highpass_state
output = np.empty_like(audio)
for i, x in enumerate(audio):
y = alpha * (y_prev + x - x_prev)
output[i] = y
y_prev, x_prev = y, x
self._highpass_state = np.array([y_prev, x_prev], dtype=np.float32)
return output
def _normalize_audio(self, audio: np.ndarray, method: str = "peak") -> np.ndarray:
"""Normalize audio to prevent clipping and ensure consistent levels."""
if method == "peak":
peak = np.max(np.abs(audio))
if peak > 0:
return audio / peak * 0.95
elif method == "rms":
rms = np.sqrt(np.mean(audio ** 2))
if rms > 0:
target_rms = 0.1
return audio * (target_rms / rms)
return audio
def _pad_to_window(self, audio: np.ndarray, n: int = WINDOW_SAMPLES) -> np.ndarray:
"""Pad or crop to the model input window length."""
if len(audio) > n:
return audio[:n]
if len(audio) < n:
return np.pad(audio, (0, n - len(audio)))
return audio
def process_chunk(
self,
audio: np.ndarray,
sample_rate: int = None,
timestamp_ms: int = None,
) -> ProcessedChunk:
"""
Process a single audio chunk through the full pipeline
(one-shot / non-buffered path).
"""
import time
if timestamp_ms is None:
timestamp_ms = int(time.time() * 1000)
self._window_id += 1
window_id = self._window_id
orig_sr = sample_rate or self.target_sr
original = audio.copy()
# 1. Convert to float32
audio = self._int16_to_float32(audio)
# 2. Resample if needed
audio = self._resample(audio, orig_sr)
# 3. High-pass filter
audio = self._highpass_filter(audio)
# 4. VAD
vad_result = self.vad.process(audio)
# 5. Normalize
if self.normalize:
audio = self._normalize_audio(audio, method="peak")
# 6. Ensure full model window size
audio = self._pad_to_window(audio)
chunk = ProcessedChunk(
audio=audio.astype(np.float32),
original_audio=original,
vad_result=vad_result,
timestamp_ms=timestamp_ms,
chunk_index=self._chunk_index,
window_id=window_id,
)
self._chunk_index += 1
return chunk
def process_stream_chunk(
self,
audio: np.ndarray,
buffer: RingBuffer,
sample_rate: int = None,
) -> Tuple[Optional[ProcessedChunk], bool]:
"""
Process streaming audio: write to buffer, extract the latest full model
window if ready. Returns (ProcessedChunk, window_ready).
"""
import time
timestamp_ms = int(time.time() * 1000)
orig_sr = sample_rate or self.target_sr
original = audio.copy()
# Convert and resample
audio = self._int16_to_float32(audio)
audio = self._resample(audio, orig_sr)
# Write to ring buffer
buffer.write(audio)
# Check if we have a full processing window
if not buffer.is_ready_for_inference():
return None, False
# Get the latest full model window / rolling step
window = buffer.get_processing_window()
if window is None:
return None, False
self._window_id += 1
window_id = self._window_id
# High-pass filter on window
window = self._highpass_filter(window)
# VAD on window
vad_result = self.vad.process(window)
# Normalize
if self.normalize:
window = self._normalize_audio(window, method="peak")
# Ensure exact model window size
window = self._pad_to_window(window)
chunk = ProcessedChunk(
audio=window.astype(np.float32),
original_audio=original,
vad_result=vad_result,
timestamp_ms=timestamp_ms,
chunk_index=self._chunk_index,
window_id=window_id,
)
self._chunk_index += 1
return chunk, True
def reset(self) -> None:
"""Reset processor state for new stream."""
self.vad.reset()
self._highpass_state = None
self._chunk_index = 0
self._window_id = 0 |