ValueError: 层顺序的输入 0 与层不兼容：：预期 min_ndim=4，发现 ndim=2。收到完整形状：[无，2584]

Question

我正在从事一个将人声部分与音频隔离的项目。我使用的是 DSD100 数据集，但是为了进行测试，我使用的是 DSD100subset数据集，我只使用了混音和人声。我的这项工作基于这篇文章

首先，我处理音频以提取频谱图并将其放在一个列表中，所有音频形成四个列表（trainMixed、trainVocals、testMixed、testVocals）。像这样：

def to_spec(wav, n_fft=1024, hop_length=256):
    return librosa.stft(wav, n_fft=n_fft, hop_length=hop_length)

def prepareData(filename, sr=22050, hop_length=256, n_fft=1024):
  audio_wav = librosa.load(filename, sr=sr, mono=True, duration=30)[0]
  audio_spec=to_spec(audio_wav, n_fft=n_fft, hop_length=hop_length)
  audio_spec_mag = np.abs(audio_spec)
  maxVal = np.max(audio_spec_mag)

  return audio_spec_mag, maxVal


# FOR EVERY LIST (trainMixed, trainVocals, testMixed, testVocals)
trainMixed = []
trainMixedNum = 0
for (root, dirs, files) in walk('./Dev-subset-mix/Dev/'):
  for d in dirs:
    filenameMix = './Dev-subset-mix/Dev/'+d+'/mixture.wav'
    spec_mag, maxVal = prepareData(filenameMix, n_fft=1024, hop_length=256)
    trainMixed.append(spec_mag/maxVal)

接下来我构建模型：

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.layers.advanced_activations import LeakyReLU

model = Sequential()
model.add(Conv2D(16, (3,3), padding='same', input_shape=(513, 25, 1)))
model.add(LeakyReLU())
model.add(Conv2D(16, (3,3), padding='same'))
model.add(LeakyReLU())
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Dropout(0.25))
model.add(Conv2D(16, (3,3), padding='same'))
model.add(LeakyReLU())
model.add(Conv2D(16, (3,3), padding='same'))
model.add(LeakyReLU())
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64))
model.add(LeakyReLU())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=keras.losses.binary_crossentropy, optimizer=sgd, metrics=['accuracy'])

并运行模型：

model.fit(trainMixed, trainVocals,epochs=10, validation_data=(testMixed, testVocals))

但我得到了这个结果：

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__
        self.name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:158 assert_input_compatibility
        ' input tensors. Inputs received: ' + str(inputs))

    ValueError: Layer sequential_1 expects 1 inputs, but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 2584) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 2584) dtype=float32>]

我是这个话题的新手，感谢您提前提供的帮助。

Answer 1

将输入数据指定给 Keras 的fit()函数可能是一个问题。我建议使用 atf.data.Dataset作为输入来fit()喜欢这样：

import tensorflow as tf

train_data = tf.data.Dataset.from_tensor_slices((trainMixed, trainVocals))
valid_data = tf.data.Dataset.from_tensor_slices((testMixed, testVocals))

model.fit(train_data, epochs=10, validation_data=valid_data)

然后，您还可以在 TF 数据集上使用shuffle()和batch()之类的函数。

编辑：您的输入形状似乎也不正确。在input_shape你第CONV层指定的(513, 25, 1)，所以输入应该是形状的批张量(batch_size, 513, 25, 1)，而你输入的形状(batch_size, 2584)。因此，您需要重塑并可能将输入剪切为指定的形状，或指定一个新的形状。