Hi,Great work! I realize the parameter of the 'model'. I test the model on the same wav but they get different result.

from BeatNet.BeatNet import BeatNet
import  numpy as np
def get_bpm(inp):
    begin = inp[0][0]
    durations = []
    for line in inp[1:]:
        durations.append(line[0] - begin)
        begin = line[0]
    return 60 / np.mean(durations)

for i in range(1,4):
    estimator = BeatNet(i, mode='offline', inference_model='PF', thread=False)

    Output = estimator.process("bpm_tes1.wav")
    # print(Output)
    print(i, get_bpm(Output))

Would it be possible now to update the dependency of Numba to a later version that supports numpy 1.23? Without it, type hints of ndarray is limited to just that, ndarray, when IDEs infer variable types. The later versions allow inferring ndarray[shape, type] such as ndarray[(Any, 3), float] which is very useful for code readability.

I'm aware of the restriction from librosa that was commented back in 2021, so just wondering if theit misleading (fake) support was fixed?

Feel free to close this if it's still not possible. Thanks.

Hello,

I can run with below modification.

def activation_extractor_stream(self): # TODO: ''' Streaming window Given the training input window's origin set to center, this streaming data formation causes 0.084 (s) delay compared to the trained model that needs to be fixed. '''
with torch.no_grad():

                hop = self.stream.read(self.log_spec_hop_length,exception_on_overflow = False)

Hey, thanks for your sharing. It is a great work.

I found that when I use 16000Hz audio I get worse results than 22050Hz.（audio from the same music） Inputs are all automatically resampled to 22050 Hz. How can I do better when I only have low quality audio of 16000Hz.

Hey, thanks for your sharing your work. It is very appreciated!

After a clean installation I wasn't able to run the example code provided. I tried digging around the code to see if I could fix it for you.

The error I kept getting was

RuntimeError: Invalid device string: 'cuda:cpu'

This code is the problem:

I changed it to this:

and I got it working

Here is the code.

    def change_device(self, device=None):
        """
        Change the device and load the model onto the new device.

        Parameters
        ----------
        device : string or None, optional (default None)
          Device to load model onto
        """
        if device is None:
            # If the function is called without a device, use the current device
            device = self.device
        elif not torch.cuda.is_available():
            device = torch.device('cpu')
        else:
        # Create the appropriate device object
            device = torch.device(f'cuda:{device}')

        # Change device field
        self.device = device
        # Load the transcription model onto the device
        self.to(self.device)

I could clean the code a little and open a pull request with this change, if you are open to that. In any case, maybe this issue will help others to make your great project work on their computer.

Cheers.

I've been wondering if anyone has got this awesome looking package working on M1 - I cannot for the life of me figure out which versions of numba and llvm to use. Brew doesn't let you install a version of llvm compatible with the versions of numba specified.

I wonder if I should checkout the repo, change some of the version requirements and cross my fingers 😂

Alternatively is this possible inside docker - will portaudio work inside a docker container?

I used numpy==1.20.3 ,but it reported that ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject,I tried to upgrade numpy, but numba 0.54.1 requires numpy<1.21,>=1.17, but you have numpy 1.23.1 which is incompatible. and reported ImportError: Numba needs NumPy 1.20 or less

Running offline mode on audio file produces beat and downbeat array. But I can't access any intermediate state space result. I want to get a scalar bpm value or bpm posterior.

This is just a basic demo script that was useful for me while testing the model for different audios and configs.

Providing a --embed flag will create a blank video clip based on the music that displays the beat & downbeat activations accordingly.

If you find this useful I can add corresponding docs.