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This code produced the following error model = maest(arch="discogs-maest-30s-pw-73e-ts") signal, sr = librosa.load(song_file, sr =16000) activations, labels = model.predict_labels(signal)
File ~/MAEST/models/maest.py:830, in MAEST.forward(self, x, transformer_block, return_self_attention)
827 self.init_melspectrogram_extractor()
829 # extract melspec from raw audio
--> 830 x = self.melspectrogram_extractor(x)
831 # normalize
832 x = (x - DISCOGS_MEAN) / (DISCOGS_STD * 2)
File ~/MAEST/models/helpers/melspectrogram_extractor.py:70, in MelSpectrogramExtractor.call(self, audio)
68 # set vector input and connect the network
69 vector_input = VectorInput(audio)
---> 70 vector_input.data >> self.frameCutter.signal
72 run(vector_input)
73 mel_bands = np.array(self.pool['mel_bands'])
File /opt/miniconda3/envs/jaage/lib/python3.8/site-packages/essentia/streaming.py:59, in _StreamConnector.rshift(left, right)
55 if not right.input_algo.hasInput(right.name):
56 raise NameError('The '%s' algorithm does not have a sink called '%s''
57 %(right.input_algo.name(), right.name))
---> 59 _essentia.connect(left.output_algo, left.name,
60 right.input_algo, right.name)
62 # update connections
63 left.output_algo.connections[left].append(right)
TypeError: While connecting VectorInput::data to FrameCutter::signal:
You cannot connect more than one Source to a Sink: FrameCutter::signal is already connected to VectorInput::data`
The text was updated successfully, but these errors were encountered:
Hi @GeorgeMcIntire,
Your script ran without errors on my Centos 7.5 server using a new Conda environment created following the installation steps. These are my input audio specs:
Channels : 2
Sample Rate : 44100
Precision : 16-bit
Duration : 00:02:09.02 = 5689728 samples = 9676.41 CDDA sectors
File Size : 5.22M
Bit Rate : 324k
Sample Encoding: MPEG audio (layer I, II or III)
Can you provide additional details? How did you install the dependencies? What type of audio are you using?
This code produced the following error
model = maest(arch="discogs-maest-30s-pw-73e-ts")
signal, sr = librosa.load(song_file, sr =16000)
activations, labels = model.predict_labels(signal)
TypeError Traceback (most recent call last)
Cell In[12], line 1
----> 1 activations, labels = model.predict_labels(signal)
File ~/MAEST/models/maest.py:881, in MAEST.predict_labels(self, x)
880 def predict_labels(self, x):
--> 881 logits = self.forward(x)[0]
882 activations = nn.functional.softmax(logits, dim=-1)
883 activations = torch.mean(activations, dim=0)
File ~/MAEST/models/maest.py:830, in MAEST.forward(self, x, transformer_block, return_self_attention)
827 self.init_melspectrogram_extractor()
829 # extract melspec from raw audio
--> 830 x = self.melspectrogram_extractor(x)
831 # normalize
832 x = (x - DISCOGS_MEAN) / (DISCOGS_STD * 2)
File ~/MAEST/models/helpers/melspectrogram_extractor.py:70, in MelSpectrogramExtractor.call(self, audio)
68 # set vector input and connect the network
69 vector_input = VectorInput(audio)
---> 70 vector_input.data >> self.frameCutter.signal
72 run(vector_input)
73 mel_bands = np.array(self.pool['mel_bands'])
File /opt/miniconda3/envs/jaage/lib/python3.8/site-packages/essentia/streaming.py:59, in _StreamConnector.rshift(left, right)
55 if not right.input_algo.hasInput(right.name):
56 raise NameError('The '%s' algorithm does not have a sink called '%s''
57 %(right.input_algo.name(), right.name))
---> 59 _essentia.connect(left.output_algo, left.name,
60 right.input_algo, right.name)
62 # update connections
63 left.output_algo.connections[left].append(right)
TypeError: While connecting VectorInput::data to FrameCutter::signal:
You cannot connect more than one Source to a Sink: FrameCutter::signal is already connected to VectorInput::data`
The text was updated successfully, but these errors were encountered: