#! /usr/bin/python2.7 """ Sends live audio analysis to the terminal. Based on musicinformationretrieval.com/realtime_spectrogram.py For more examples using PyAudio: https://github.com/mwickert/scikit-dsp-comm/blob/master/sk_dsp_comm/pyaudio_helper.py """ from __future__ import print_function import argparse import json import librosa import math import numpy import os import pyaudio import redis import statistics import sys import time def debug(*args, **kwargs): if( verbose == False ): return print(*args, file=sys.stderr, **kwargs) # Define default variables. BAND_OCTAVES = 10 # 12 * 9 octaves _BAND_TONES = BAND_OCTAVES * 12 # octaves * notes per octave _CHANNELS = 1 _FRAMES_PER_BUFFER = 4410 _N_FFT = 4096 _RATE = 44100 _SAMPLING_FREQUENCY = 0.1 _BPM_MIN=10 _BPM_MAX=400 # Argument parsing parser = argparse.ArgumentParser(prog='realtime_redis') # Standard Args parser.add_argument("-v","--verbose",action="store_true",help="Verbose") # Redis Args parser.add_argument("-i","--ip",help="IP address of the Redis server ",default="127.0.0.1",type=str) parser.add_argument("-p","--port",help="Port of the Redis server ",default="6379",type=str) # Audio Capture Args parser.add_argument('--list-devices','-L', action='store_true', help='Which devices are detected by pyaudio') parser.add_argument('--mode','-m', required=False, default='spectrum', choices=['spectrum', 'bpm'], type=str, help='Which mode to use. Default=spectrum') parser.add_argument('--device','-d', required=False, type=int, help='Which pyaudio device to use') parser.add_argument('--sampling-frequency','-s', required=False, default=0.1, type=float, help='Which frequency, in seconds. Default={}f '.format(_SAMPLING_FREQUENCY)) parser.add_argument('--channels','-c', required=False, default=_CHANNELS, type=int, help='How many channels. Default={} '.format(_CHANNELS)) parser.add_argument('--rate','-r', required=False, default=44100, type=int, help='The audio capture rate in Hz. Default={} '.format(_RATE)) parser.add_argument('--frames','-f', required=False, default=4410, type=int, help='How many frames per buffer. Default={}'.format(_FRAMES_PER_BUFFER)) # BPM Mode Args parser.add_argument('--bpm-min', required=False, default=_BPM_MIN, type=int, help='BPM mode only. The low BPM threshold. Default={} '.format(_BPM_MIN)) parser.add_argument('--bpm-max', required=False, default=_BPM_MAX, type=int, help='BPM mode only. The high BPM threshold. Default={} '.format(_BPM_MAX)) args = parser.parse_args() # global bpm = 120.0 start = 0 # Set real variables F_LO = librosa.note_to_hz('C0') F_HI = librosa.note_to_hz('C10') BAND_TONES = _BAND_TONES N_FFT = _N_FFT CHANNELS = args.channels DEVICE = args.device FRAMES_PER_BUFFER = int(args.rate * args.sampling_frequency ) LIST_DEVICES = args.list_devices MODE = args.mode RATE = args.rate SAMPLING_FREQUENCY = args.sampling_frequency bpm_min = args.bpm_min bpm_max = args.bpm_max ip = args.ip port = args.port verbose = args.verbose melFilter = librosa.filters.mel(RATE, N_FFT, BAND_TONES, fmin=F_LO, fmax=F_HI) r = redis.Redis( host=ip, port=port) # Early exit to list devices # As it may crash later if not properly configured # def list_devices(): # List all audio input devices p = pyaudio.PyAudio() i = 0 n = p.get_device_count() print("\nFound {} devices\n".format(n)) print(" {} {}".format('ID', 'Device name')) while i < n: dev = p.get_device_info_by_index(i) if dev['maxInputChannels'] > 0: print(" {} {}".format(i, dev['name'])) i += 1 if( LIST_DEVICES ): list_devices() os._exit(1) def m_bpm(audio_data): """ This function saves slow analysis to redis * bpm * beat """ global bpm global start # Detect tempo / bpm new_bpm, beats = librosa.beat.beat_track( y = audio_data, sr = RATE, trim = False, #start_bpm = bpm, units = "time" ) ''' new_bpm = librosa.beat.tempo(y = audio_data, sr=RATE)[0] ''' # Correct the eventual octave error if new_bpm < bpm_min or new_bpm > bpm_max: found = False octaveErrorList = [ 0.5, 2, 0.3333, 3 ] for key,factor in enumerate(octaveErrorList): correction = new_bpm * factor if correction > bpm_min and correction < bpm_max: debug( "Corrected high/low bpm:{} to:{}".format(new_bpm, correction)) new_bpm = correction found = True break if found == False: if new_bpm < bpm_min : new_bpm = bpm_min else : new_bpm = bpm_max debug("new_bpm:{}".format(new_bpm)) ''' How to guess the next beats based on the data sent to redis ~~ A Dirty Graph ~~ |start end| Capture |........................| BPM detect+Redis set || Client Redis get | Time |........................||.............| ---SAMPLING_FREQUENCY---- - < TIME-START Read Delay --------------- < 2*SAMPLING_FREQUENCY - PTTL Delay ----------------------------------------- Beats |last beat . known ...b....b....b....b....b. . passed (...b....b....b.) . guessed (..b....b....b....b... Next Beat Calculation b....b....b....b.|..b Beats |last beat 0 1 2 3 4 Redis: key bpm_sample_interval visual |........................| key bpm_delay visual |.........................| ''' bpm = new_bpm bpm_sample_interval = SAMPLING_FREQUENCY * 1000 bpm_delay = (SAMPLING_FREQUENCY + time.time() - start ) * 1000 pexpireat = int( 2 * bpm_sample_interval); # Save to Redis r.set( 'bpm', round(bpm,2), px = pexpireat ) r.set( 'bpm_sample_interval', bpm_sample_interval ) r.set( 'bpm_delay', bpm_delay ) r.set( 'beats', json.dumps( beats.tolist() ) ) #debug( "pexpireat:{}".format(pexpireat)) debug( "bpm:{} bpm_delay:{} bpm_sample_interval:{} beats:{}".format(bpm,bpm_delay,bpm_sample_interval,beats) ) return True def m_spectrum(audio_data): """ This function saves fast analysis to redis """ # Compute real FFT. fft = numpy.fft.rfft(audio_data, n=N_FFT) # Compute mel spectrum. melspectrum = melFilter.dot(abs(fft)) # Initialize output characters to display. spectrum_120 = [0]*BAND_TONES spectrum_10 = [0]*BAND_OCTAVES spectrum_oct = [[] for i in range(10)] # Assign values for i in range(BAND_TONES): val = round(melspectrum[i],2) spectrum_120[i] = val key = int(math.floor( i / 12 )) spectrum_oct[key].append(val) for i in range(BAND_OCTAVES): spectrum_10[i] = round(sum( spectrum_oct[i] ) / len( spectrum_oct[i]),2) # Get RMS #rms = librosa.feature.rms( S=melspectrum ) rms = librosa.feature.rms( y=audio_data ).tolist()[0] rms_avg = round(sum(rms) / len(rms),2) # Save to redis #debug( 'spectrum_120:{} '.format(spectrum_120)) #debug( 'spectrum_10:{}'.format(spectrum_10)) #debug( 'rms:{}'.format(rms_avg)) if len(spectrum_120): r.set( 'spectrum_120', json.dumps( spectrum_120 ) ) if len(spectrum_10): r.set( 'spectrum_10', json.dumps( spectrum_10 ) ) if rms : r.set( 'rms', "{}".format(rms_avg) ) return True def callback(in_data, frame_count, time_info, status): audio_data = numpy.fromstring(in_data, dtype=numpy.float32) global start start = time.time() if MODE == 'spectrum': m_spectrum(audio_data) elif MODE == 'bpm': m_bpm( audio_data) else: debug( "Unknown mode. Exiting") os._exit(2) end = time.time() debug ("\rLoop took {:.2}s on {}s ".format(end - start, SAMPLING_FREQUENCY)) return (in_data, pyaudio.paContinue) debug( "\n\nRunning! Using mode {}.\n\n".format(MODE)) p = pyaudio.PyAudio() stream = p.open(format=pyaudio.paFloat32, channels=CHANNELS, rate=RATE, input=True, # Do record input. output=False, # Do not play back output. frames_per_buffer=FRAMES_PER_BUFFER, input_device_index = DEVICE, stream_callback=callback) stream.start_stream() while stream.is_active(): time.sleep(SAMPLING_FREQUENCY) stream.stop_stream() stream.close() p.terminate()