Implement beatnet

[kobakos]

https://github.com/axinc-ai/ailia-models/issues/1450

[kyakuno]

@kobakos beatnetのonnxをいただくことは可能でしょうか？

[kyakuno]

モデルアップロード済み。 https://storage.googleapis.com/ailia-models/beatnet/beatnet_1.onnx

[kyakuno]

madmomで下記のエラーになる。

Traceback (most recent call last):
  File "/Users/kyakuno/Desktop/ailia/ailia-models-ax/audio_processing/beatnet/beatnet.py", line 4, in <module>
    from madmom.features import DBNDownBeatTrackingProcessor
  File "/usr/local/lib/python3.11/site-packages/madmom/__init__.py", line 24, in <module>
    from . import audio, evaluation, features, io, ml, models, processors, utils
  File "/usr/local/lib/python3.11/site-packages/madmom/audio/__init__.py", line 27, in <module>
    from . import comb_filters, filters, signal, spectrogram, stft
  File "madmom/audio/comb_filters.pyx", line 15, in init madmom.audio.comb_filters
  File "/usr/local/lib/python3.11/site-packages/madmom/processors.py", line 23, in <module>
    from collections import MutableSequence
ImportError: cannot import name 'MutableSequence' from 'collections' (/usr/local/Homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/collections/__init__.py)

[kyakuno]

これが原因みたい。 https://github.com/CPJKU/madmom/issues/535

[kyakuno]

@kobakos madmomをlibrosaのstftなどに置き換え可能でしょうか？

[kyakuno]

madmom

from madmom.audio.signal import SignalProcessor, FramedSignalProcessor
from madmom.audio.stft import ShortTimeFourierTransformProcessor
from madmom.audio.spectrogram import (
    FilteredSpectrogramProcessor, LogarithmicSpectrogramProcessor,
    SpectrogramDifferenceProcessor)
from madmom.processors import ParallelProcessor, SequentialProcessor

# feature extractor that extracts magnitude spectrogram and its differences  

class LOG_SPECT(FeatureModule):
    def __init__(self, num_channels=1, sample_rate=22050, win_length=2048, hop_size=512, n_bands=[12], mode='online'):
        sig = SignalProcessor(num_channels=num_channels, win_length=win_length, sample_rate=sample_rate)
        self.sample_rate = sample_rate
        self.hop_length = hop_size
        self.num_channels = num_channels
        multi = ParallelProcessor([])
        frame_sizes = [win_length]  
        num_bands = n_bands  
        for frame_size, num_bands in zip(frame_sizes, num_bands):
            if mode == 'online' or mode == 'offline':
                frames = FramedSignalProcessor(frame_size=frame_size, hop_size=hop_size) 
            else:   # for real-time and streaming modes 
                frames = FramedSignalProcessor(frame_size=frame_size, hop_size=hop_size, num_frames=4) 
            stft = ShortTimeFourierTransformProcessor()  # caching FFT window
            filt = FilteredSpectrogramProcessor(
                num_bands=num_bands, fmin=30, fmax=17000, norm_filters=True)
            spec = LogarithmicSpectrogramProcessor(mul=1, add=1)
            diff = SpectrogramDifferenceProcessor(
                diff_ratio=0.5, positive_diffs=True, stack_diffs=np.hstack)
            # process each frame size with spec and diff sequentially
            multi.append(SequentialProcessor((frames, stft, filt, spec, diff)))
        # stack the features and process everything sequentially
        self.pipe = SequentialProcessor((sig, multi, np.hstack))

    def process_audio(self, audio):
        feats = self.pipe(audio)
        return feats.T

chatgptでlibrosaにしてもらったもの（動作未確認）

import numpy as np
import librosa
import librosa.display

class LOG_SPECT:
    def __init__(self, sample_rate=22050, win_length=2048, hop_length=512, n_mels=128):
        self.sample_rate = sample_rate
        self.win_length = win_length
        self.hop_length = hop_length
        self.n_mels = n_mels

    def process_audio(self, audio_path):
        y, sr = librosa.load(audio_path, sr=self.sample_rate)

        # Calculate the short-time Fourier transform (STFT)
        stft = librosa.stft(y, n_fft=self.win_length, hop_length=self.hop_length)

        # Get the magnitude spectrogram
        mag_spec = np.abs(stft)

        # Convert to mel scale
        mel_spec = librosa.feature.melspectrogram(S=mag_spec, sr=sr, n_mels=self.n_mels)

        # Convert to log scale
        log_mel_spec = librosa.power_to_db(mel_spec)

        # Compute the first order difference (delta)
        delta = librosa.feature.delta(log_mel_spec)

        # Combine the log mel spectrogram and its delta
        log_mel_spec_with_delta = np.vstack([log_mel_spec, delta])

        return log_mel_spec_with_delta.T

# 使用例
# feature_extractor = LOG_SPECT()
# features = feature_extractor.process_audio('path/to/audio/file.wav')

[kobakos]

エラーのもとになっているfeatures.pyはLOG_SPECTだけじゃなくてDBNDownBeatTrackingProcessorやparticle_filtering_cascadeを使うときにも必要なので、LOG_SPECTの修正だけではDBNと粒子フィルタどちらの推論モードでも依然エラーは出てしまいます。必要なコードだけmadmomからとってきて同じディレクトリに入れることができないかとも思いましたがCythonが使われているのでこれも簡単ではなさそう。 GitHubにある実装ではインポートの問題も解消されているのですが、pipのやつは古いままになっているのでrequirements.txtによる対応は難しそうです。