avm/hassio-addons
1
0
Fork 0
mirror of https://github.com/alexbelgium/hassio-addons.git synced 2026-01-11 18:31:02 +01:00
Code Issues Packages Projects Releases Wiki Activity

Update DOCS.md

Browse Source
This commit is contained in:
Alexandre
2024-10-17 10:53:28 +02:00
committed by GitHub
parent 7057bc0af7
commit 09332f1b73
1 changed files with 104 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

145
birdnet-pi/DOCS.md
View File

@@ -128,7 +128,7 @@ if [ -f "$HOME/focusrite.sh" ]; then
fi
if [ -f "$HOME/autogain.py" ]; then
python autogain.py >/tmp/log_autogain 2>/tmp/log_autogain_error &
"$HOME/autogain.py" >/tmp/log_autogain 2>/tmp/log_autogain_error &
fi
```
@@ -392,18 +392,26 @@ Add this content in "$HOME/autogain.py" && chmod +x "$HOME/autogain.py"
"""
Microphone Gain Adjustment Script
This script captures audio from an RTSP stream, processes it to calculate the RMS
within the 2000-4000 Hz frequency band, and adjusts the microphone gain based on
predefined noise thresholds and trends.
This script captures audio from an RTSP stream using GStreamer if available,
or falls back to ffmpeg if GStreamer is not installed. The script processes
the audio to calculate the RMS within the 2000-4000 Hz frequency band and
adjusts the microphone gain based on predefined noise thresholds and trends.
Dependencies:
- numpy
- scipy
- ffmpeg (installed and accessible in PATH)
- ffmpeg or GStreamer (installed and accessible in PATH)
- amixer (for microphone gain control)
Author: OpenAI ChatGPT
Date: 2024-04-27
Changelog:
- 2024-04-27: Initial version of the script that captures RTSP stream, calculates RMS, and adjusts gain.
- 2024-10-17: Added support for GStreamer as the preferred RTSP stream capture tool, with ffmpeg as a fallback.
Implemented a debug log system to provide detailed logs with timestamps.
Added trend-based microphone gain adjustment and noise RMS calculation.
Enhanced error handling for both GStreamer and ffmpeg.
"""
import subprocess
@@ -436,7 +444,6 @@ DEBUG = 1
# -----------------------------------------------------------------------
def debug(msg):
"""
Prints debug messages if DEBUG mode is enabled.
@@ -458,7 +465,6 @@ def get_gain_db(mic_name):
cmd = ['amixer', 'sget', mic_name]
try:
output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode()
# Regex to find patterns like [30.00dB]
match = re.search(r'\[(-?\d+(\.\d+)?)dB\]', output)
if match:
gain_db = float(match.group(1))
@@ -490,10 +496,79 @@ def set_gain_db(mic_name, gain_db):
return False
def calculate_noise_rms(rtsp_url, bandpass_sos, num_bins=5):
def check_gstreamer_available():
"""
Captures audio from an RTSP stream, applies a bandpass filter, divides the
audio into segments, and calculates the RMS of the quietest segment.
Checks if GStreamer is available on the system.
"""
try:
subprocess.check_call(['which', 'gst-launch-1.0'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
debug("GStreamer is available.")
return True
except subprocess.CalledProcessError:
debug("GStreamer is not available.")
return False
def calculate_noise_rms_gstream(rtsp_url, bandpass_sos, num_bins=5):
"""
Captures audio using GStreamer from an RTSP stream, applies a bandpass filter,
divides the audio into segments, and calculates the RMS of the quietest segment.
:param rtsp_url: The RTSP stream URL.
:param bandpass_sos: Precomputed bandpass filter coefficients (Second-Order Sections).
:param num_bins: Number of segments to divide the audio into.
:return: The RMS amplitude of the quietest segment as a float, or None on failure.
"""
cmd = [
'gst-launch-1.0',
'rtspsrc', f'location={rtsp_url}', '!',
'decodebin', '!', 'audioconvert', '!',
'audioresample', '!', 'audio/x-raw,rate=32000,channels=1',
'!', 'filesink', 'location=/dev/stdout'
]
try:
debug(f"Starting GStreamer audio capture from {rtsp_url}")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
if process.returncode != 0:
debug(f"GStreamer failed with error: {stderr.decode()}")
return None
audio = np.frombuffer(stdout, dtype=np.int16).astype(np.float32) / 32768.0
debug(f"Captured {len(audio)} samples from GStreamer.")
if len(audio) == 0:
debug("No audio data captured.")
return None
filtered = sosfilt(bandpass_sos, audio)
debug("Applied bandpass filter to audio data.")
total_samples = len(filtered)
bin_size = total_samples // num_bins
if bin_size == 0:
debug("Bin size is 0; insufficient audio data.")
return 0.0
trimmed_filtered = filtered[:bin_size * num_bins]
segments = trimmed_filtered.reshape(num_bins, bin_size)
debug(f"Divided audio into {num_bins} bins of {bin_size} samples each.")
rms_values = np.sqrt(np.mean(segments ** 2, axis=1))
min_rms = rms_values.min()
debug(f"Minimum RMS value among segments: {min_rms}")
return min_rms
except Exception as e:
debug(f"Exception during noise RMS calculation with GStreamer: {e}")
return None
def calculate_noise_rms_ffmpeg(rtsp_url, bandpass_sos, num_bins=5):
"""
Captures audio using FFmpeg from an RTSP stream, applies a bandpass filter,
divides the audio into segments, and calculates the RMS of the quietest segment.
:param rtsp_url: The RTSP stream URL.
:param bandpass_sos: Precomputed bandpass filter coefficients (Second-Order Sections).
@@ -515,27 +590,23 @@ def calculate_noise_rms(rtsp_url, bandpass_sos, num_bins=5):
]
try:
debug(f"Starting audio capture from {rtsp_url}")
debug(f"Starting FFmpeg audio capture from {rtsp_url}")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
if process.returncode != 0:
debug(f"ffmpeg failed with error: {stderr.decode()}")
debug(f"FFmpeg failed with error: {stderr.decode()}")
return None
# Convert raw PCM data to numpy array
audio = np.frombuffer(stdout, dtype=np.int16).astype(np.float32) / 32768.0
debug(f"Captured {len(audio)} samples from audio stream.")
debug(f"Captured {len(audio)} samples from FFmpeg.")
if len(audio) == 0:
debug("No audio data captured.")
return None
# Apply bandpass filter
filtered = sosfilt(bandpass_sos, audio)
debug("Applied bandpass filter to audio data.")
# Divide into num_bins
total_samples = len(filtered)
bin_size = total_samples // num_bins
@@ -543,26 +614,30 @@ def calculate_noise_rms(rtsp_url, bandpass_sos, num_bins=5):
debug("Bin size is 0; insufficient audio data.")
return 0.0
trimmed_length = bin_size * num_bins
trimmed_filtered = filtered[:trimmed_length]
trimmed_filtered = filtered[:bin_size * num_bins]
segments = trimmed_filtered.reshape(num_bins, bin_size)
debug(f"Divided audio into {num_bins} bins of {bin_size} samples each.")
# Calculate RMS for each segment
rms_values = np.sqrt(np.mean(segments ** 2, axis=1))
debug(f"Calculated RMS values for each segment: {rms_values}")
# Return the minimum RMS value
min_rms = rms_values.min()
debug(f"Minimum RMS value among segments: {min_rms}")
return min_rms
except Exception as e:
debug(f"Exception during noise RMS calculation: {e}")
debug(f"Exception during noise RMS calculation with FFmpeg: {e}")
return None
def calculate_noise_rms(rtsp_url, bandpass_sos, num_bins=5):
"""
Attempts to capture audio from an RTSP stream using GStreamer first,
and falls back to FFmpeg if GStreamer is unavailable.
"""
if check_gstreamer_available():
return calculate_noise_rms_gstream(rtsp_url, bandpass_sos, num_bins)
else:
return calculate_noise_rms_ffmpeg(rtsp_url, bandpass_sos, num_bins)
def main():
"""
Main loop that continuously monitors background noise and adjusts microphone gain.
@@ -570,15 +645,13 @@ def main():
TREND_COUNT = 0
PREVIOUS_TREND = 0
# Precompute the bandpass filter coefficients
LOWCUT = 2000 # Lower frequency bound in Hz
HIGHCUT = 8000 # Upper frequency bound in Hz
FILTER_ORDER = 5 # Order of the Butterworth filter
LOWCUT = 2000
HIGHCUT = 4000
FILTER_ORDER = 5
sos = butter(FILTER_ORDER, [LOWCUT, HIGHCUT], btype='band', fs=44100, output='sos')
debug("Precomputed Butterworth bandpass filter coefficients.")
# Set the microphone gain to the maximum gain at the start
success = set_gain_db(MICROPHONE_NAME, MAX_GAIN_DB)
if success:
print(f"Microphone gain set to {MAX_GAIN_DB} dB at start.")
@@ -594,16 +667,9 @@ def main():
time.sleep(60)
continue
if not isinstance(min_rms, (float, int)):
print(f"Invalid noise RMS output detected: {min_rms}. Retrying in 1 minute...")
time.sleep(60)
continue
# Print the final converted RMS amplitude (only once)
print(f"Converted RMS Amplitude: {min_rms}")
debug(f"Current background noise (RMS amplitude): {min_rms}")
# Determine the noise trend
if min_rms > NOISE_THRESHOLD_HIGH:
CURRENT_TREND = 1
elif min_rms < NOISE_THRESHOLD_LOW:
@@ -635,7 +701,6 @@ def main():
if TREND_COUNT >= TREND_COUNT_THRESHOLD:
if CURRENT_TREND == 1:
# Decrease gain by 1 dB
NEW_GAIN_DB = CURRENT_GAIN_DB - DECREASE_GAIN_STEP_DB
if NEW_GAIN_DB < MIN_GAIN_DB:
NEW_GAIN_DB = MIN_GAIN_DB
@@ -646,7 +711,6 @@ def main():
else:
print("Failed to set new gain.")
elif CURRENT_TREND == -1:
# Increase gain by 5 dB
NEW_GAIN_DB = CURRENT_GAIN_DB + INCREASE_GAIN_STEP_DB
if NEW_GAIN_DB > MAX_GAIN_DB:
NEW_GAIN_DB = MAX_GAIN_DB
@@ -660,7 +724,6 @@ def main():
else:
debug("No gain adjustment needed.")
# Sleep for 1 minute before the next iteration
time.sleep(60)