FreeDATA/modem/audio.py
2023-12-30 21:47:16 +01:00

330 lines
11 KiB
Python

"""
Gather information about audio devices.
"""
import atexit
import multiprocessing
import crcengine
import sounddevice as sd
import structlog
import numpy as np
import queue
import threading
atexit.register(sd._terminate)
log = structlog.get_logger("audio")
# crc algorithm for unique audio device names
crc_algorithm = crcengine.new("crc16-ccitt-false") # load crc16 library
def get_audio_devices():
"""
return list of input and output audio devices in own process to avoid crashes of portaudio on raspberry pi
also uses a process data manager
"""
# we need to run this on Windows for multiprocessing support
# multiprocessing.freeze_support()
# multiprocessing.get_context("spawn")
# we need to reset and initialize sounddevice before running the multiprocessing part.
# If we are not doing this at this early point, not all devices will be displayed
#sd._terminate()
#sd._initialize()
# log.debug("[AUD] get_audio_devices")
with multiprocessing.Manager() as manager:
proxy_input_devices = manager.list()
proxy_output_devices = manager.list()
# print(multiprocessing.get_start_method())
proc = multiprocessing.Process(
target=fetch_audio_devices, args=(proxy_input_devices, proxy_output_devices)
)
proc.start()
proc.join()
# additional logging for audio devices
# log.debug("[AUD] get_audio_devices: input_devices:", list=f"{proxy_input_devices}")
# log.debug("[AUD] get_audio_devices: output_devices:", list=f"{proxy_output_devices}")
return list(proxy_input_devices), list(proxy_output_devices)
def device_crc(device) -> str:
crc_hwid = crc_algorithm(bytes(f"{device['name']}.{device['hostapi']}", encoding="utf-8"))
crc_hwid = crc_hwid.to_bytes(2, byteorder="big")
crc_hwid = crc_hwid.hex()
return crc_hwid
def fetch_audio_devices(input_devices, output_devices):
"""
get audio devices from portaudio
Args:
input_devices: proxy variable for input devices
output_devices: proxy variable for output devices
Returns:
"""
devices = sd.query_devices(device=None, kind=None)
for index, device in enumerate(devices):
# Use a try/except block because Windows doesn't have an audio device range
try:
name = device["name"]
# Ignore some Flex Radio devices to make device selection simpler
if name.startswith("DAX RESERVED") or name.startswith("DAX IQ"):
continue
max_output_channels = device["max_output_channels"]
max_input_channels = device["max_input_channels"]
except KeyError:
continue
except Exception as err:
print(err)
max_input_channels = 0
max_output_channels = 0
if max_input_channels > 0:
hostapi_name = sd.query_hostapis(device['hostapi'])['name']
new_input_device = {"id": device_crc(device),
"name": device['name'],
"api": hostapi_name,
"native_index":index}
# check if device not in device list
if new_input_device not in input_devices:
input_devices.append(new_input_device)
if max_output_channels > 0:
hostapi_name = sd.query_hostapis(device['hostapi'])['name']
new_output_device = {"id": device_crc(device),
"name": device['name'],
"api": hostapi_name,
"native_index":index}
# check if device not in device list
if new_output_device not in output_devices:
output_devices.append(new_output_device)
# FreeData uses the crc as id inside the configuration
# SD lib uses a numerical id which is essentially an
# index of the device within the list
# returns (id, name)
def get_device_index_from_crc(crc, isInput: bool):
try:
in_devices = []
out_devices = []
fetch_audio_devices(in_devices, out_devices)
if isInput:
detected_devices = in_devices
else:
detected_devices = out_devices
for i, dev in enumerate(detected_devices):
if dev['id'] == crc:
return (dev['native_index'], dev['name'])
except Exception as e:
log.warning(f"Audio device {crc} not detected ", devices=detected_devices, isInput=isInput)
return [None, None]
def test_audio_devices(input_id: str, output_id: str) -> list:
test_result = [False, False]
try:
result = get_device_index_from_crc(input_id, True)
if result is None:
# in_dev_index, in_dev_name = None, None
raise ValueError(f"[Audio-Test] Invalid input device index {input_id}.")
else:
in_dev_index, in_dev_name = result
sd.check_input_settings(
device=in_dev_index,
channels=1,
dtype="int16",
samplerate=48000,
)
test_result[0] = True
except (sd.PortAudioError, ValueError) as e:
log.warning(f"[Audio-Test] Input device error ({input_id}):", e=e)
test_result[0] = False
try:
result = get_device_index_from_crc(output_id, False)
if result is None:
# out_dev_index, out_dev_name = None, None
raise ValueError(f"[Audio-Test] Invalid output device index {output_id}.")
else:
out_dev_index, out_dev_name = result
sd.check_output_settings(
device=out_dev_index,
channels=1,
dtype="int16",
samplerate=48000,
)
test_result[1] = True
except (sd.PortAudioError, ValueError) as e:
log.warning(f"[Audio-Test] Output device error ({output_id}):", e=e)
test_result[1] = False
sd._terminate()
sd._initialize()
return test_result
def set_audio_volume(datalist: np.ndarray, dB: float) -> np.ndarray:
"""
Scale values for the provided audio samples by dB.
:param datalist: Audio samples to scale
:type datalist: np.ndarray
:param dB: Decibels to scale samples, constrained to the range [-50, 50]
:type dB: float
:return: Scaled audio samples
:rtype: np.ndarray
"""
try:
dB = float(dB)
except ValueError as e:
print(f"[MDM] Changing audio volume failed with error: {e}")
dB = 0.0 # 0 dB means no change
# Clip dB value to the range [-50, 50]
dB = np.clip(dB, -30, 20)
# Ensure datalist is an np.ndarray
if not isinstance(datalist, np.ndarray):
print("[MDM] Invalid data type for datalist. Expected np.ndarray.")
return datalist
# Convert dB to linear scale
scale_factor = 10 ** (dB / 20)
# Scale samples
scaled_data = datalist * scale_factor
# Clip values to int16 range and convert data type
return np.clip(scaled_data, -32768, 32767).astype(np.int16)
RMS_COUNTER = 0
CHANNEL_BUSY_DELAY = 0
def calculate_fft(data, fft_queue, states) -> None:
"""
Calculate an average signal strength of the channel to assess
whether the channel is "busy."
"""
# Initialize dbfs counter
# rms_counter = 0
# https://gist.github.com/ZWMiller/53232427efc5088007cab6feee7c6e4c
# Fast Fourier Transform, 10*log10(abs) is to scale it to dB
# and make sure it's not imaginary
global RMS_COUNTER, CHANNEL_BUSY_DELAY
try:
fftarray = np.fft.rfft(data)
# Set value 0 to 1 to avoid division by zero
fftarray[fftarray == 0] = 1
dfft = 10.0 * np.log10(abs(fftarray))
# get average of dfft
avg = np.mean(dfft)
# Detect signals which are higher than the
# average + 10 (+10 smoothes the output).
# Data higher than the average must be a signal.
# Therefore we are setting it to 100 so it will be highlighted
# Have to do this when we are not transmitting so our
# own sending data will not affect this too much
if not states.isTransmitting():
dfft[dfft > avg + 15] = 100
# Calculate audio dbfs
# https://stackoverflow.com/a/9763652
# calculate dbfs every 50 cycles for reducing CPU load
RMS_COUNTER += 1
if RMS_COUNTER > 5:
d = np.frombuffer(data, np.int16).astype(np.float32)
# calculate RMS and then dBFS
# https://dsp.stackexchange.com/questions/8785/how-to-compute-dbfs
# try except for avoiding runtime errors by division/0
try:
rms = int(np.sqrt(np.max(d ** 2)))
if rms == 0:
raise ZeroDivisionError
audio_dbfs = 20 * np.log10(rms / 32768)
states.set("audio_dbfs", audio_dbfs)
except Exception as e:
states.set("audio_dbfs", -100)
RMS_COUNTER = 0
# Convert data to int to decrease size
dfft = dfft.astype(int)
# Create list of dfft
dfftlist = dfft.tolist()
# Reduce area where the busy detection is enabled
# We want to have this in correlation with mode bandwidth
# TODO This is not correctly and needs to be checked for correct maths
# dfftlist[0:1] = 10,15Hz
# Bandwidth[Hz] / 10,15
# narrowband = 563Hz = 56
# wideband = 1700Hz = 167
# 1500Hz = 148
# 2700Hz = 266
# 3200Hz = 315
# slot
slot = 0
slot1 = [0, 65]
slot2 = [65,120]
slot3 = [120, 176]
slot4 = [176, 231]
slot5 = [231, len(dfftlist)]
slotbusy = [False,False,False,False,False]
# Set to true if we should increment delay count; else false to decrement
addDelay=False
for range in [slot1, slot2, slot3, slot4, slot5]:
range_start = range[0]
range_end = range[1]
# define the area, we are detecting busy state
slotdfft = dfft[range_start:range_end]
# Check for signals higher than average by checking for "100"
# If we have a signal, increment our channel_busy delay counter
# so we have a smoother state toggle
if np.sum(slotdfft[slotdfft > avg + 15]) >= 200 and not states.isTransmitting():
addDelay=True
slotbusy[slot]=True
#states.channel_busy_slot[slot] = True
# increment slot
slot += 1
states.set_channel_slot_busy(slotbusy)
if addDelay:
# Limit delay counter to a maximum of 200. The higher this value,
# the longer we will wait until releasing state
states.set_channel_busy_condition_traffic(True)
CHANNEL_BUSY_DELAY = min(CHANNEL_BUSY_DELAY + 10, 200)
else:
# Decrement channel busy counter if no signal has been detected.
CHANNEL_BUSY_DELAY = max(CHANNEL_BUSY_DELAY - 1, 0)
# When our channel busy counter reaches 0, toggle state to False
if CHANNEL_BUSY_DELAY == 0:
states.set_channel_busy_condition_traffic(False)
# erase queue if greater than 3
if fft_queue.qsize() >= 1:
fft_queue = queue.Queue()
fft_queue.put(dfftlist[:315]) # 315 --> bandwidth 3200
except Exception as err:
print(f"[MDM] calculate_fft: Exception: {err}")