Add beat_callback parameter for streaming mode

README.md

@@ -66,6 +66,8 @@ thread: To decide whether accomplish the inference at the main thread or another
 
 device: Type of device being used. Cuda or cpu (by default).
 
+beat_callback: A function, which is called when a beat is detected. It is called with True for a downbeat and with False for a normal beat. Only available in streaming mode. Defaults to printing _*beat!_ for a downbeat and _beat!_ for a non-downbeat.
+
 Installation command:
 ---------------------
 

setup.py

@@ -20,8 +20,8 @@
 REQUIRED_PACKAGES = [
     'numpy',
     'cython',
-    'librosa>=0.8.0',
-    'numba==0.54.1', # Manually specified here as librosa incorrectly states that it is compatible with the latest version of numba although 0.50.0 is not compatible. 
+    'librosa>=0.10.1',
+    'numba>=0.58.1', # Manually specified here as librosa incorrectly states that it is compatible with the latest version of numba although 0.50.0 is not compatible. 
     'scipy',
     'mido>=1.2.6',
     'pytest',

src/BeatNet/BeatNet.py

@@ -48,7 +48,7 @@ class BeatNet:
     '''
 
 
-    def __init__(self, model, mode='online', inference_model='PF', plot=[], thread=False, device='cpu'):
+    def __init__(self, model, mode='online', inference_model='PF', plot=[], thread=False, device='cpu', beat_callback=lambda is_downbeat: print("*beat!" if is_downbeat else "beat!")):
         self.model = model
         self.mode = mode
         self.inference_model = inference_model
@@ -64,7 +64,7 @@ def __init__(self, model, mode='online', inference_model='PF', plot=[], thread=F
         self.proc = LOG_SPECT(sample_rate=self.log_spec_sample_rate, win_length=self.log_spec_win_length,
                              hop_size=self.log_spec_hop_length, n_bands=[24], mode = self.mode)
         if self.inference_model == "PF":                 # instantiating a Particle Filter decoder - Is Chosen for online inference
-            self.estimator = particle_filter_cascade(beats_per_bar=[], fps=50, plot=self.plot, mode=self.mode)
+            self.estimator = particle_filter_cascade(beats_per_bar=[], fps=50, plot=self.plot, mode=self.mode, beat_callback=beat_callback)
         elif self.inference_model == "DBN":                # instantiating an HMM decoder - Is chosen for offline inference
             self.estimator = DBNDownBeatTrackingProcessor(beats_per_bar=[2, 3, 4], fps=50)
         else:

src/BeatNet/particle_filtering_cascade.py

@@ -125,7 +125,8 @@ def __init__(self, beats_per_bar=[], particle_size=PARTICLE_SIZE, down_particle_
                  min_bpm=MIN_BPM, max_bpm=MAX_BPM, num_tempi=NUM_TEMPI, min_beats_per_bar=MIN_BEAT_PER_BAR,
                  max_beats_per_bar=MAX_BEAT_PER_BAR, offset=OFFSET, ig_threshold=IG_THRESHOLD, lambda_b=LAMBDA_B,
                  lambda_d=LAMBDA_D, observation_lambda_b=OBSERVATION_LAMBDA_B, observation_lambda_d=OBSERVATION_LAMBDA_D,
-                 fps=None, plot=False, mode=None, **kwargs):
+                 fps=None, plot=False, mode=None, beat_callback=lambda is_downbeat: print("*beat!" if is_downbeat else "beat!"),
+                 **kwargs):
         self.particle_size = particle_size
         self.down_particle_size = down_particle_size
         self.particle_filter = []
@@ -141,6 +142,7 @@ def __init__(self, beats_per_bar=[], particle_size=PARTICLE_SIZE, down_particle_
         self.offset = offset
         self.ig_threshold = ig_threshold
         self.mode = mode
+        self.beat_callback = beat_callback
         # convert timing information to construct a beat state space
         min_interval = 60. * fps / max_bpm
         max_interval = 60. * fps / min_bpm
@@ -284,11 +286,11 @@ def process(self, activations):
                 if self.down_max in self.st2.first_states[0] and self.path[-1][1] !=1 and both_activations[i][1]>0.4:
                     self.path = np.append(self.path, [[self.offset + self.counter * self.T, 1]], axis=0)
                     if self.mode == 'stream' or self.mode == 'realtime':
-                        print("*beat!")
+                        self.beat_callback(True)
                 elif (activations[i]>0.4) :
                     self.path = np.append(self.path, [[self.offset + self.counter * self.T, 2]], axis=0)
                     if self.mode == 'stream' or self.mode == 'realtime':
-                        print("beat!")
+                        self.beat_callback(False)
                     #librosa.clicks(times=None, frames=None, sr=22050, hop_length=512, click_freq=440.0, click_duration=0.1, click=None, length=None)
                 if 'downbeat_particles' in self.plot:
                     self.downbeat_particles_plot()