Assertion Error t len(mixture) == len(clean) == len(enhanced)

[mnabihali]

when I am trying to run the code it gives me an error in this condition
assert len(mixture) == len(clean) == len(enhanced)
I printed the len of each and found the len of enhanced and clean is equal but len of the mixture is greater than both.

I hope you can help me as soon as possible

[Lerry123]

Hello, have you solved your problem？I have the same problem with you.

[mnabihali]

No, but I remove this assertion and the required scores I calculated it manually (signal by signal comparison this will not raise an error) if you find another solution please tell me.

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On Friday, July 17, 2020, Lerry123 ***@***.***> wrote: Hello, have you solved your problem？I have the same problem with you. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANWBGBEALJCEKHEKVHUCQDDR4ADMJANCNFSM4NNNL46A> .

-- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: mohmed.nabih@gmail.com <mohmed.nabih@gmail.com> * *Mobile: +201285659213* *Work: 02-33318417*

[Lerry123]

Are you Chinese? We can chat on qq. My English is not good.

[Lerry123]

I find this code do the padding in the mixture,but the clean and enhancement don't do the padding.
Which database do you use and how about the result?

[mnabihali]

Dear, Sorry I am not Chinese. Regarding the dataset I am using VCTK dataset and a noisy version of librispeech dataset. Sorry Can I ask which part is doning padding for mixture? Thanks

…

You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANWBGBALDNQ6YC2AOEM5US3R4EC5ZANCNFSM4NNNL46A> .

-- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: mohmed.nabih@gmail.com <mohmed.nabih@gmail.com> * *Mobile: +201285659213* *Work: 02-33318417*

[mnabihali]

Do you find another solution for the problem?

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On Saturday, July 18, 2020, Mohamed Nabih ***@***.***> wrote: Dear, Sorry I am not Chinese. Regarding the dataset I am using VCTK dataset and a noisy version of librispeech dataset. Sorry Can I ask which part is doning padding for mixture? Thanks > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <#7 (comment)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ANWBGBALDNQ6YC2AOEM5US3R4EC5ZANCNFSM4NNNL46A> > . > -- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: ***@***.*** ***@***.***> * *Mobile: +201285659213* *Work: 02-33318417*

-- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: mohmed.nabih@gmail.com <mohmed.nabih@gmail.com> * *Mobile: +201285659213* *Work: 02-33318417*

[Lerry123]

I am also using the VCTK database,I haven't found a solution yet.If I find the solution,I will tell you.

[Lerry123]

I have padded for the clean、enhanced and the mixture and this problem is solved,but I have the new problem. When computed STOI, it 's error.The detail is as follow.
AttributeError: module 'numpy' has no attribute 'gcd'
I haven't found the solution and I only computed the PESQ.
# Metric
#stoi_c_n.append(compute_STOI(clean, mixture, sr=16000))
#stoi_c_e.append(compute_STOI(clean, enhanced, sr=16000))
pesq_c_n.append(compute_PESQ(clean, mixture, sr=16000))
pesq_c_e.append(compute_PESQ(clean, enhanced, sr=16000))
Did you have the same problem?

[mnabihali]

Can you provide me the code how you padding the signals in order to try computing the Stoi

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On Monday, July 20, 2020, Lerry123 ***@***.***> wrote: I have padded for the clean、enhanced and the mixture and this problem is solved,but I have the new problem. When computed STOI, it 's error.The detail is as follow. AttributeError: module 'numpy' has no attribute 'gcd' I haven't found the solution and I only computed the PESQ. # Metric #stoi_c_n.append(compute_STOI(clean, mixture, sr=16000)) #stoi_c_e.append(compute_STOI(clean, enhanced, sr=16000)) pesq_c_n.append(compute_PESQ(clean, mixture, sr=16000)) pesq_c_e.append(compute_PESQ(clean, enhanced, sr=16000)) Did you have the same problem? — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANWBGBGK3Y57CFGKMS6C6F3R4P23FANCNFSM4NNNL46A> .

-- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: mohmed.nabih@gmail.com <mohmed.nabih@gmail.com> * *Mobile: +201285659213* *Work: 02-33318417*

[mnabihali]

@Lerry123
Can you tell me how you pad the signals, and I can try to solve the Numpy issue.

Thanks

[Lerry123]

trainer.py:

for i, (mixture, clean, name) in enumerate(self.validation_data_loader):
assert len(name) == 1, "Only support batch size is 1 in enhancement stage."
name = name[0]
padded_length = 0
#print("len(mixture):",len( mixture.cpu().numpy().reshape(-1)))
mixture = mixture.to(self.device) # [1, 1, T]
clean = clean.to(self.device)
# The input of the model should be fixed length.

        if mixture.size(-1) % sample_length != 0:
            #print("mixture.size(-1):",mixture.size(-1))
            padded_length = sample_length - (mixture.size(-1) % sample_length)
            #print("padded_length:",padded_length)    
            mixture = torch.cat([mixture, torch.zeros(1, 1, padded_length, device=self.device)], dim=-1)
            clean = torch.cat([clean, torch.zeros(1, 1, padded_length, device=self.device)], dim=-1)
        #print("len(mixture):",len( mixture.cpu().numpy().reshape(-1)))
        #print("mixture.size(-1) % sample_length:",mixture.size(-1) % sample_length)
        #print("mixture.dim():",mixture.dim())
        assert mixture.size(-1) % sample_length == 0 and mixture.dim() == 3
        mixture_chunks = list(torch.split(mixture, sample_length, dim=-1))
        #print("mixture_chunks:",mixture_chunks)    
        enhanced_chunks = []
        for chunk in mixture_chunks:
            enhanced_chunks.append(self.model(chunk).detach().cpu())
        enhanced = torch.cat(enhanced_chunks, dim=-1)  # [1, 1, T]
        enhanced = enhanced.to(self.device)
        '''
        print("len(enhanced):",len(enhanced.cpu().reshape(-1).numpy()))
        print("padded_length:",padded_length)
        '''
        enhanced = enhanced 
        if padded_length == 0:
            enhanced = enhanced 
        else:
            
            enhanced = torch.cat([enhanced, torch.zeros(1, 1, padded_length, device=self.device)], dim=-1)
            enhanced=enhanced[:, :, :-padded_length]
        #print("len(enhanced):",len(enhanced.cpu().reshape(-1).numpy())) 
        enhanced = enhanced.cpu().reshape(-1).numpy()
        clean = clean.cpu().numpy().reshape(-1)   
        mixture = mixture.cpu().numpy().reshape(-1)

[mnabihali]

Thanks Regarding the AttributeError: module 'NumPy' has no attribute 'gcd' It is available on Numpy version 1.15.0. So, check the NumPy version and back to me if not solved.

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On Tue, Jul 21, 2020 at 3:18 AM Lerry123 ***@***.***> wrote: trainer.py: for i, (mixture, clean, name) in enumerate(self.validation_data_loader): assert len(name) == 1, "Only support batch size is 1 in enhancement stage." name = name[0] padded_length = 0 #print("len(mixture):",len( mixture.cpu().numpy().reshape(-1))) mixture = mixture.to(self.device) # [1, 1, T] clean = clean.to(self.device) # The input of the model should be fixed length. if mixture.size(-1) % sample_length != 0: #print("mixture.size(-1):",mixture.size(-1)) padded_length = sample_length - (mixture.size(-1) % sample_length) #print("padded_length:",padded_length) mixture = torch.cat([mixture, torch.zeros(1, 1, padded_length, device=self.device)], dim=-1) clean = torch.cat([clean, torch.zeros(1, 1, padded_length, device=self.device)], dim=-1) #print("len(mixture):",len( mixture.cpu().numpy().reshape(-1))) #print("mixture.size(-1) % sample_length:",mixture.size(-1) % sample_length) #print("mixture.dim():",mixture.dim()) assert mixture.size(-1) % sample_length == 0 and mixture.dim() == 3 mixture_chunks = list(torch.split(mixture, sample_length, dim=-1)) #print("mixture_chunks:",mixture_chunks) enhanced_chunks = [] for chunk in mixture_chunks: enhanced_chunks.append(self.model(chunk).detach().cpu()) enhanced = torch.cat(enhanced_chunks, dim=-1) # [1, 1, T] enhanced = enhanced.to(self.device) ''' print("len(enhanced):",len(enhanced.cpu().reshape(-1).numpy())) print("padded_length:",padded_length) ''' enhanced = enhanced if padded_length == 0: enhanced = enhanced else: enhanced = torch.cat([enhanced, torch.zeros(1, 1, padded_length, device=self.device)], dim=-1) enhanced=enhanced[:, :, :-padded_length] #print("len(enhanced):",len(enhanced.cpu().reshape(-1).numpy())) enhanced = enhanced.cpu().reshape(-1).numpy() clean = clean.cpu().numpy().reshape(-1) mixture = mixture.cpu().numpy().reshape(-1) — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANWBGBGQPWLPFNFPN5V6QADR4TUEXANCNFSM4NNNL46A> .

-- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: mohmed.nabih@gmail.com <mohmed.nabih@gmail.com> * *Mobile: +201285659213* *Work: 02-33318417*

[Lerry123]

Thank you! It was solved by your suggestion.

[mnabihali]

Thanks Hope everything will be fine, can you provide me with your email to contact you for further problems

…

On Wednesday, July 22, 2020, Lerry123 ***@***.***> wrote: Thank you! It was solved by your suggestion. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANWBGBEDD2QNFCRZMTGZSWTR42SQ5ANCNFSM4NNNL46A> .

-- *Mohamed Nabih Ali* *Assistant **Lecturer * *Faculty of Computers and IT * *Egyptian E-Learning University* *Ain Shams Center* *Mail: mohmed.nabih@gmail.com <mohmed.nabih@gmail.com> * *Mobile: +201285659213* *Work: 02-33318417*

[Lerry123]

Can I get your email ? Have you done the test? My result is very confusing.