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try:
from ..gui.signal import *
from ..utils.db import *
from ..utils.telemetry import my_tracer, os_name
except ImportError:
from gui.signal import *
from utils.db import *
from utils.telemetry import my_tracer, os_name
import numpy as np
import sounddevice as sd
import soundfile as sf
import scipy.io.wavfile as wavfile
import soundcard as sc
import threading
import time
samplerate = 48000 # Updated samplerate for better quality
channels = 1
recording = False
audio_data = None
user_id = load_user_id()
os_name_ = os_name()
the_input_box_pre = ""
import queue
# Initialize a queue to keep the last N audio levels (rolling window)
audio_levels = queue.Queue(maxsize=10) # Adjust size as needed
def calculate_dynamic_threshold():
"""Calculate a dynamic threshold based on recent audio levels."""
if audio_levels.qsize() == 0:
return 0.01 # Default threshold if no data is available
else:
# Calculate the average of the last N audio levels
return np.mean(list(audio_levels.queue)) * 2 # Adjust multiplier as needed
silence_start_time = None
auto_stop_recording = True
def start_recording(take_system_audio, buttonhandler):
"""Start recording audio from microphone and/or system sound.
"""
with my_tracer.start_span("start_recording") as span:
span.set_attribute("user_id", user_id)
span.set_attribute("os_name", os_name_)
global the_input_box_pre
from ..gpt_computer_assistant import the_input_box, the_main_window
the_input_box_pre = the_input_box.toPlainText()
the_main_window.update_from_thread("Click again when recording is done")
global recording, audio_data, silence_start_time, auto_stop_recording
recording = True
audio_data = np.array([], dtype="float32")
print("Recording started...")
threshold = 0.01 # Define the threshold for stopping the recording
silence_duration = 2 # Duration in seconds to consider as silence before stopping
silence_start_time = None
recording_start_time = time.time() # Record the start time of the recording
auto_stop_recording = is_auto_stop_recording_setting_active()
def callback(indata, frames, time_info, status):
global audio_data, recording, silence_start_time, auto_stop_recording
current_level = np.max(np.abs(indata))
# Add the current level to the queue
if audio_levels.full():
audio_levels.get() # Remove the oldest level if the queue is full
audio_levels.put(current_level)
# Calculate dynamic threshold based on recent audio levels
dynamic_threshold = calculate_dynamic_threshold()
if recording:
audio_data = np.append(audio_data, indata)
# Check if the audio is below the dynamic threshold
if current_level < dynamic_threshold and auto_stop_recording:
if silence_start_time is None:
silence_start_time = time.time() # Mark the start of silence
# Ensure recording has been ongoing for at least 3 seconds before considering auto-stop
elif (time.time() - silence_start_time) > silence_duration and (time.time() - recording_start_time) > 3:
recording = False
buttonhandler.recording = False
else:
silence_start_time = None
def record_audio():
with my_tracer.start_span("record_audio") as span:
span.set_attribute("user_id", user_id)
span.set_attribute("os_name", os_name_)
global recording
mics = sc.all_microphones(include_loopback=True)
default_mic = mics[0]
data = []
with default_mic.recorder(samplerate=148000) as mic:
print("Recording...")
while recording:
frame = mic.record(numframes=4096)
data.append(frame)
data = np.concatenate(data, axis=0)
data_int16 = (data * 32767).astype("int16")
wavfile.write(system_sound_location, 148000, data_int16)
if take_system_audio:
recording_thread = threading.Thread(target=record_audio)
recording_thread.start()
with sd.InputStream(callback=callback, channels=channels, samplerate=samplerate):
while recording:
sd.sleep(100)
if not recording:
sf.write(mic_record_location, audio_data, samplerate)
print("Audio saved as voice_input.wav")
signal_handler.recording_stopped.emit()
def stop_recording():
"""Stop recording audio."""
global recording
recording = False
print("Recording stopped")
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