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import numpy as np
from scipy.signal import welch
import os
import time
import threading
# --- HOST COMMUNICATION ---
import __main__
try:
BaseNode = __main__.BaseNode
QtGui = __main__.QtGui
PA_INSTANCE = getattr(__main__, "PA_INSTANCE", None)
except AttributeError:
class BaseNode:
def get_blended_input(self, name, mode): return None
import PyQt6.QtGui as QtGui
PA_INSTANCE = None
try:
import pyaudio
HAS_PYAUDIO = True
except ImportError:
HAS_PYAUDIO = False
try:
from pydub import AudioSegment
HAS_PYDUB = True
except ImportError:
HAS_PYDUB = False
class AudioPlayerNode(BaseNode):
"""
Audio Player & Analyzer (Auto-Pause Fix).
Now intelligently pauses audio if the simulation loop stops.
"""
NODE_CATEGORY = "Input"
NODE_TITLE = "Audio Player (Realtime)"
NODE_COLOR = QtGui.QColor(255, 50, 100)
def __init__(self):
super().__init__()
self.inputs = {
'volume': 'signal',
'playback_pos': 'signal',
'pause': 'signal' # Manual pause
}
self.outputs = {
'spectrum': 'spectrum',
'raw_signal': 'signal',
'band_power': 'image'
}
self.band_names = ["Sub", "Bass", "Mid", "High", "Air"]
for i in range(5):
self.outputs[f'band_{i+1}_{self.band_names[i]}'] = 'signal'
self.file_path = "music.mp3"
self.gain = 1.0
self.audio_data = None
self.sample_rate = 44100
self.play_head = 0
self.stream = None
self.is_playing = False
# --- AUTO-PAUSE LOGIC ---
self.last_step_time = time.time()
self.last_spectrum = np.zeros(16)
self.last_5bands = np.zeros(5)
self.spec_edges = np.logspace(np.log10(20), np.log10(20000), 17)
self.five_edges = [20, 60, 250, 2000, 6000, 20000]
self.load_audio()
def update(self):
self.load_audio()
def load_audio(self):
self.stop_stream()
if not os.path.exists(self.file_path):
print(f"Audio file not found: {self.file_path}")
return
try:
print(f"Loading {self.file_path}...")
if HAS_PYDUB:
seg = AudioSegment.from_file(self.file_path)
self.sample_rate = seg.frame_rate
samples = np.array(seg.get_array_of_samples())
if seg.channels == 2:
samples = samples.reshape((-1, 2)).mean(axis=1)
if seg.sample_width == 2:
samples = samples.astype(np.float32) / 32768.0
elif seg.sample_width == 4:
samples = samples.astype(np.float32) / 2147483648.0
self.audio_data = samples
self.start_stream()
else:
print("Pydub not installed.")
except Exception as e:
print(f"Error loading audio: {e}")
def start_stream(self):
if not HAS_PYAUDIO or PA_INSTANCE is None:
return
def callback(in_data, frame_count, time_info, status):
if self.audio_data is None:
return (None, pyaudio.paComplete)
# --- AUTO-PAUSE CHECK ---
# If step() hasn't been called in > 200ms, assume Host is Paused
if time.time() - self.last_step_time > 0.2:
# Return silence but keep stream alive (Pause)
return (np.zeros(frame_count, dtype=np.float32).tobytes(), pyaudio.paContinue)
if self.play_head + frame_count >= len(self.audio_data):
self.play_head = 0
data = self.audio_data[self.play_head : self.play_head + frame_count]
self.play_head += frame_count
out_data = (data * self.gain).astype(np.float32)
return (out_data.tobytes(), pyaudio.paContinue)
try:
self.stream = PA_INSTANCE.open(
format=pyaudio.paFloat32,
channels=1,
rate=self.sample_rate,
output=True,
stream_callback=callback,
frames_per_buffer=1024
)
self.stream.start_stream()
self.is_playing = True
except Exception as e:
print(f"Failed to start stream: {e}")
def stop_stream(self):
if self.stream:
try:
self.stream.stop_stream()
self.stream.close()
except: pass
self.stream = None
self.is_playing = False
def step(self):
# Update heartbeat timestamp so audio thread knows we are running
self.last_step_time = time.time()
vol = self.get_blended_input('volume', 'sum')
if vol is not None: self.gain = float(vol)
if self.audio_data is None: return
# Analysis
window_size = int(self.sample_rate * 0.05)
current_pos = self.play_head
if current_pos + window_size > len(self.audio_data):
chunk = self.audio_data[current_pos:]
else:
chunk = self.audio_data[current_pos : current_pos + window_size]
if len(chunk) < 64: return
freqs, psd = welch(chunk, fs=self.sample_rate, nperseg=len(chunk))
# 16-Band
spec = np.zeros(16)
for i in range(16):
mask = (freqs >= self.spec_edges[i]) & (freqs < self.spec_edges[i+1])
if np.sum(mask) > 0: spec[i] = np.mean(psd[mask])
if np.max(spec) > 0: spec /= np.max(spec)
self.last_spectrum = spec
# 5-Band
bands = np.zeros(5)
for i in range(5):
mask = (freqs >= self.five_edges[i]) & (freqs < self.five_edges[i+1])
if np.sum(mask) > 0: bands[i] = np.mean(psd[mask])
bands = np.log1p(bands * 1000)
if np.max(bands) > 0: bands /= np.max(bands)
self.last_5bands = bands
def get_output(self, port_name):
if port_name == 'spectrum': return self.last_spectrum
elif port_name == 'raw_signal': return float(np.mean(self.last_spectrum))
elif port_name.startswith('band_'):
try:
idx = int(port_name.split('_')[1]) - 1
return float(self.last_5bands[idx])
except: pass
elif port_name == 'band_power':
h, w = 64, 128
img = np.zeros((h, w), dtype=np.float32)
bar_w = w // 16
for i, val in enumerate(self.last_spectrum):
height = int(val * (h-1))
img[h-height:, i*bar_w:(i+1)*bar_w] = 1.0
return img
return None
def get_config_options(self):
return [
("Audio File", "file_path", self.file_path, "file_open"),
("Gain", "gain", self.gain, "float")
]
def close(self):
self.stop_stream() |