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ensemble.py

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+# coding: utf-8
+__author__ = 'Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/'
+
+import os
+import librosa
+import soundfile as sf
+import numpy as np
+import argparse
+
+def stft(wave, nfft, hl):
+    wave_left = np.asfortranarray(wave[0])
+    wave_right = np.asfortranarray(wave[1])
+    spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)
+    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)
+    spec = np.asfortranarray([spec_left, spec_right])
+    return spec
+
+def istft(spec, hl, length):
+    spec_left = np.asfortranarray(spec[0])
+    spec_right = np.asfortranarray(spec[1])
+    wave_left = librosa.istft(spec_left, hop_length=hl, length=length)
+    wave_right = librosa.istft(spec_right, hop_length=hl, length=length)
+    wave = np.asfortranarray([wave_left, wave_right])
+    return wave
+
+def absmax(a, *, axis):
+    dims = list(a.shape)
+    dims.pop(axis)
+    indices = list(np.ogrid[tuple(slice(0, d) for d in dims)])
+    argmax = np.abs(a).argmax(axis=axis)
+    insert_pos = (len(a.shape) + axis) % len(a.shape)
+    indices.insert(insert_pos, argmax)
+    return a[tuple(indices)]
+
+def absmin(a, *, axis):
+    dims = list(a.shape)
+    dims.pop(axis)
+    indices = list(np.ogrid[tuple(slice(0, d) for d in dims)])
+    argmax = np.abs(a).argmin(axis=axis)
+    insert_pos = (len(a.shape) + axis) % len(a.shape)
+    indices.insert(insert_pos, argmax)
+    return a[tuple(indices)]
+
+def lambda_max(arr, axis=None, key=None, keepdims=False):
+    idxs = np.argmax(key(arr), axis)
+    if axis is not None:
+        idxs = np.expand_dims(idxs, axis)
+        result = np.take_along_axis(arr, idxs, axis)
+        if not keepdims:
+            result = np.squeeze(result, axis=axis)
+        return result
+    else:
+        return arr.flatten()[idxs]
+
+def lambda_min(arr, axis=None, key=None, keepdims=False):
+    idxs = np.argmin(key(arr), axis)
+    if axis is not None:
+        idxs = np.expand_dims(idxs, axis)
+        result = np.take_along_axis(arr, idxs, axis)
+        if not keepdims:
+            result = np.squeeze(result, axis=axis)
+        return result
+    else:
+        return arr.flatten()[idxs]
+
+def average_waveforms(pred_track, weights, algorithm):
+    """
+    :param pred_track: shape = (num, channels, length)
+    :param weights: shape = (num, )
+    :param algorithm: One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft
+    :return: averaged waveform in shape (channels, length)
+    """
+    pred_track = np.array(pred_track)
+    final_length = pred_track.shape[-1]
+
+    mod_track = []
+    for i in range(pred_track.shape[0]):
+        if algorithm == 'avg_wave':
+            mod_track.append(pred_track[i] * weights[i])
+        elif algorithm in ['median_wave', 'min_wave', 'max_wave']:
+            mod_track.append(pred_track[i])
+        elif algorithm in ['avg_fft', 'min_fft', 'max_fft', 'median_fft']:
+            spec = stft(pred_track[i], nfft=2048, hl=1024)
+            if algorithm in ['avg_fft']:
+                mod_track.append(spec * weights[i])
+            else:
+                mod_track.append(spec)
+    pred_track = np.array(mod_track)
+
+    if algorithm in ['avg_wave']:
+        pred_track = pred_track.sum(axis=0)
+        pred_track /= np.array(weights).sum().T
+    elif algorithm in ['median_wave']:
+        pred_track = np.median(pred_track, axis=0)
+    elif algorithm in ['min_wave']:
+        pred_track = lambda_min(pred_track, axis=0, key=np.abs)
+    elif algorithm in ['max_wave']:
+        pred_track = lambda_max(pred_track, axis=0, key=np.abs)
+    elif algorithm in ['avg_fft']:
+        pred_track = pred_track.sum(axis=0)
+        pred_track /= np.array(weights).sum()
+        pred_track = istft(pred_track, 1024, final_length)
+    elif algorithm in ['min_fft']:
+        pred_track = lambda_min(pred_track, axis=0, key=np.abs)
+        pred_track = istft(pred_track, 1024, final_length)
+    elif algorithm in ['max_fft']:
+        pred_track = absmax(pred_track, axis=0)
+        pred_track = istft(pred_track, 1024, final_length)
+    elif algorithm in ['median_fft']:
+        pred_track = np.median(pred_track, axis=0)
+        pred_track = istft(pred_track, 1024, final_length)
+    return pred_track
+
+def ensemble_files(args):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--files", type=str, required=True, nargs='+', help="Path to all audio-files to ensemble")
+    parser.add_argument("--type", type=str, default='avg_wave', help="One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft")
+    parser.add_argument("--weights", type=float, nargs='+', help="Weights to create ensemble. Number of weights must be equal to number of files")
+    parser.add_argument("--output", default="res.wav", type=str, help="Path to wav file where ensemble result will be stored")
+    args = parser.parse_args(args) if isinstance(args, list) else parser.parse_args()
+
+    print('Ensemble type: {}'.format(args.type))
+    print('Number of input files: {}'.format(len(args.files)))
+    if args.weights is not None:
+        weights = args.weights
+    else:
+        weights = np.ones(len(args.files))
+    print('Weights: {}'.format(weights))
+    print('Output file: {}'.format(args.output))
+    
+    data = []
+    for f in args.files:
+        if not os.path.isfile(f):
+            print('Error. Can\'t find file: {}. Check paths.'.format(f))
+            return None
+        print('Reading file: {}'.format(f))
+        wav, sr = librosa.load(f, sr=None, mono=False)
+        print("Waveform shape: {} sample rate: {}".format(wav.shape, sr))
+        data.append(wav)
+    
+    data = np.array(data)
+    res = average_waveforms(data, weights, args.type)
+    print('Result shape: {}'.format(res.shape))
+    sf.write(args.output, res.T, sr, 'FLOAT')
+    return args.output
+
+if __name__ == "__main__":
+    ensemble_files(None)