[init]
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= Redilysis = Redis + Audio Analysis
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Redilysis sends audio analysis to a redis install. What's the use? Using that information for multiple visualizations, of course!
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== Installation
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```python
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git clone https://git.interhacker.space/tmplab/redilysis.git
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cd redilysis
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pip install -r requirements.txt
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python3 redilysis.py --help
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```
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== Guide
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There are two available modes.
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**One is the slow mode with BPM recognition:**
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python3 redilysis.py -m bpm -s 1 -f 44100
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Pushes following keys in redis:
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* onset
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* bpm
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* beats
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**The other is a fast mode with spectrogram analysis**
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python3 redilysis.py -m spectrum -s 0.1 -f 4410
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Pushes following keys in redis:
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* rms
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* spectrum
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* tuning
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"""
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Sends live audio analysis to the terminal.
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Based on musicinformationretrieval.com/realtime_spectrogram.py
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For more examples using PyAudio:
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https://github.com/mwickert/scikit-dsp-comm/blob/master/sk_dsp_comm/pyaudio_helper.py
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"""
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import argparse
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import json
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import librosa
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import numpy
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import os
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import pyaudio
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import redis
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import time
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def list_devices():
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# List all audio input devices
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p = pyaudio.PyAudio()
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i = 0
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n = p.get_device_count()
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print("\nFound {} devices\n".format(n))
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print (" {} {}".format('ID', 'Device name'))
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while i < n:
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dev = p.get_device_info_by_index(i)
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if dev['maxInputChannels'] > 0:
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print (" {} {}".format(i, dev['name']))
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i += 1
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# Define default variables.
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_BAND_RANGE = 96
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_CHANNELS = 1
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_ENERGY_THRESHOLD = 0.4
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_FRAMES_PER_BUFFER = 4410
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_N_FFT = 4096
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_RATE = 44100
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_SAMPLING_FREQUENCY = 0.1
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# Argument parsing
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parser = argparse.ArgumentParser(prog='realtime_redis')
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parser.add_argument('--list-devices','-L', action='store_true', help='Which devices are detected by pyaudio')
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parser.add_argument('--mode','-m', required=False, default='spectrum', choices=['spectrum', 'bpm'], type=str, help='Which mode to use. Default=spectrum')
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parser.add_argument('--device','-d', required=False, type=int, help='Which pyaudio device to use')
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parser.add_argument('--frames','-f', required=False, default=4410, type=int, help='How many frames per buffer. Default={}'.format(_FRAMES_PER_BUFFER))
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parser.add_argument('--sampling-frequency','-s', required=False, default=0.1, type=float, help='Which frequency, in seconds. Default={}f '.format(_SAMPLING_FREQUENCY))
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parser.add_argument('--channels','-c', required=False, default=_CHANNELS, type=int, help='How many channels. Default={} '.format(_CHANNELS))
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parser.add_argument('--rate','-r', required=False, default=44100, type=int, help='Which rate. Default={} '.format(_RATE))
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parser.add_argument('--energy-threshold','-e', required=False, default=0.4, type=float, help='Which energy triggers spectrum detection flag. Default={} '.format(_ENERGY_THRESHOLD))
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args = parser.parse_args()
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# Set real variables
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BAND_RANGE = _BAND_RANGE
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CHANNELS = args.channels
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DEVICE = args.device
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ENERGY_THRESHOLD = args.energy_threshold
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FRAMES_PER_BUFFER = args.frames
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LIST_DEVICES = args.list_devices
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MODE = args.mode
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N_FFT = _N_FFT
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RATE = args.rate
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SAMPLING_FREQUENCY = args.sampling_frequency
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# Define the frequency range of the log-spectrogram.
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F_LO = librosa.note_to_hz('C2')
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F_HI = librosa.note_to_hz('C9')
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M = librosa.filters.mel(RATE, N_FFT, BAND_RANGE, fmin=F_LO, fmax=F_HI)
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r = redis.Redis(
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host='localhost',
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port=6379)
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# Early exit to list devices
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if( LIST_DEVICES ):
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list_devices()
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os._exit(1)
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p = pyaudio.PyAudio()
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# global
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bpm = 120.0
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def m_bpm(audio_data):
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"""
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This function saves slow analysis to redis
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* beat
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"""
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global bpm
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# Get RMS
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rms = librosa.feature.rmse( audio_data )
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onset = librosa.onset.onset_detect(
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y=audio_data,
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sr=RATE)
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new_bpm, beats = librosa.beat.beat_track(
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y=audio_data,
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sr=RATE,
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trim=False,
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start_bpm=bpm,
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units="time"
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)
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print ( bpm, new_bpm)
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# Save spectrum
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r.set( 'onset', json.dumps( onset.tolist() ) )
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r.set( 'bpm', json.dumps( new_bpm ) )
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r.set( 'beats', json.dumps( beats.tolist() ) )
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bpm = new_bpm
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return True
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def m_spectrum(audio_data):
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"""
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This function saves fast analysis to redis
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* spectrum
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* RMS
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"""
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# Compute real FFT.
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x_fft = numpy.fft.rfft(audio_data, n=N_FFT)
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# Compute mel spectrum.
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melspectrum = M.dot(abs(x_fft))
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# Get RMS
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rms = librosa.feature.rmse( S=melspectrum, frame_length=FRAMES_PER_BUFFER )
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# Initialize output characters to display.
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bit_list = [0]*BAND_RANGE
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count = 0
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highest_index = -1
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highest_value = 0
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for i in range(BAND_RANGE):
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val = melspectrum[i]
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# If this is the highest tune, record it
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if( val > highest_value ) :
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highest_index = i
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highest_value = val
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# If there is energy in this frequency, mark it
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if val > ENERGY_THRESHOLD:
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count += 1
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bit_list[i] = 1
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# Save to redis
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r.set( 'rms', "{}".format(rms.tolist()) )
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r.set( 'spectrum', json.dumps( bit_list ) )
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r.set( 'tuning', highest_index )
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return True
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def callback(in_data, frame_count, time_info, status):
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audio_data = numpy.fromstring(in_data, dtype=numpy.float32)
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start = time.time()
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if MODE == 'spectrum':
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m_spectrum(audio_data)
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elif MODE == 'bpm':
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m_bpm( audio_data)
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else:
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print( "Unknown mode. Exiting")
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os._exit(2)
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end = time.time()
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print ("\rLoop took {:.2}s on {}s ".format(end - start, SAMPLING_FREQUENCY), end="")
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return (in_data, pyaudio.paContinue)
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print( "\n\nRunning! Using mode {}.\n\n".format(MODE))
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stream = p.open(format=pyaudio.paFloat32,
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channels=CHANNELS,
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rate=RATE,
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input=True, # Do record input.
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output=False, # Do not play back output.
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frames_per_buffer=FRAMES_PER_BUFFER,
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input_device_index = DEVICE,
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stream_callback=callback)
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stream.start_stream()
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while stream.is_active():
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time.sleep(SAMPLING_FREQUENCY)
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stream.stop_stream()
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stream.close()
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p.terminate()
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@ -0,0 +1,5 @@
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Redilysis
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librosa=0.6.1
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numpy=1.14.2
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pyaudio
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redis
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