Comments (5)
Vishesh813
commented on July 17, 2024
1
@vbelz please help on this.
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vbelz
commented on July 17, 2024
Hi Miguel,
thanks, good to hear it was useful for you!
1) In creation mode, the audio is splitter into several time windows
(slightly above one second). Each window (will be converted to 2D
spectrogram) and will be a sample for training. nb_samples is simply the
number of windows used.
2) No! As I described in the readme/article, training was using 10h of
sounds! It required the use of GPU and has been trained on colab.
nb_samples=50 was only for example purpose, and you can run it with CPU
only.
3) When reading audio it is resampled to 8kHz (see data creation or data
prediction), so yes before going to the network. Have a look for example at
the function:
audio_files_to_numpy
4) Sure, you should try to train it. My recommendation would be to gather
enough data to train. Be aware as well that it will require GPU (see google
colab or other cloud alternatives). Additionally, the global scaling to
apply for input and output might differ (it is expected to have a
distribution of values between -1 and 1). In term of hyperparameters, try
to play with Unet parameters such as size_filter_in, kernel_init,
activation_layer.
Kind regards,
Vincent
Le jeu. 5 mars 2020 à 09:46, Miguel Betegón <[email protected]> a
écrit :
… HI @vbelz <https://github.com/vbelz> ,
First of all, thankyou for your work, I have tried to denoise some audio
and it worked so good, but I have a few questions
Quoted from README:
Specify how many frames you want to create as nb_samples in args.py (or
pass it as argument from the terminal) I let nb_samples=50 by default for
the demo but for production I would recommend having 40 000 or more.
*1.* What is exactly nb_samples?
*2.* Are the weights provided by you from nb_samples=50?
*3.* Should I resample audio to be 8KHz for denoising or is it done
inside the network? Also, should I do it for training?
*4.* I want to twerk it to be a better denoiser for background noise
rather than specific sounds. What are your thoughts on this? I have a
dataset with clean samples and background noise samples. Will it work if it
train it? Which hyperparameter should I use?
Thank you so much and sorry for bothering you!
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betegon
commented on July 17, 2024
Hi @vbelz , Thank you for your kind and quick response.
I have been working on creating the data necessary to train it, and I have a few more questions (sorry for bothering you).
I have approx. 10h of audio, and when I am about to create the dataset, I end up with the following error, caused in the function numpy_audio_to_matrix_spectrogram:
m_mag_db = np.zeros((nb_audio, dim_square_spec, dim_square_spec))
MemoryError: Unable to allocate array with shape (37028, 257, 257) and data type float64
Also, I have the following questions:
1.) I have used dimensions of 256x256, as I have downsampled the audios to 16KHz instead of 8KH. The window I have used is of 16128KHz, which is slightly more than one second. Do you thinks this is a correct approach? I mean, your window was of 64Hz more than a second for 8KHz, so I scaled it to 16KHz. Also, the problem I am facing is that the size I get from preparing the dataset is 256x257 (the dimensions that librosa.stft returns). I don't know why isn't it 256x256, as my parameters are: hop_length_fft = 63, n_fft = 510, frame_length = 16128 and hop_length_frame = 16128. This gives a result of 16128/63 = 256, so I don't know where it gets that number of 257 columns.
2.) Why the window should be in between a second? Will it improve its performance if it is smaller / bigger?
3.) Do you think there will be any mayor loss of performance by decreasing precision to 32bit (i.e. numpy datatype = 'float32')
4.) It looks like you are cropping all audios as you don't include the last window of them. Therefore, I have added zero padding to the end of each audio to achieve the window size. What do you think about this?
5.) I have concatenated the audios one after another so they keep the audio structure. Is there a special reason to create a random order? you use the function blend_noise_randomly to do this.
6.) What's the difference between frame_length and hop_frame_length? I think they refer to the same parameter: the sliding window size for STFT, which is by definition the frame_length.
Thanks a lot for your time and effort
cheers.
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vaishalibhardwaj
commented on July 17, 2024
@vbelz please help on this.
Hello Vishesh,
As I am new to this project could you guide me on how to get till till the denoised output.
Note: I do not have GPUs on my computer
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Vishesh813
commented on July 17, 2024
Hi vbelz,
How can I help her?
…On Wed, 29 Jul, 2020, 12:05 AM Vaishali Bhardwaj, ***@***.***> wrote:
@vbelz <https://github.com/vbelz> please help on this.
Hello Vishesh,
As I am new to this project could you guide me on how to get till till the
denoised output.
Note: I do not have GPUs on my computer
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Related Issues (20)
- Hi, do you have a paper in arxiv? HOT 2
- Error (tensorflow)
- Python 2 or 3 ? HOT 2
- Is this project unsupervised learning? HOT 1
- Why extracted windows are slightly above 1 second?
- Question about the inputs and the outputs of the model HOT 1
- Question on Error of Invalid Instruction (core dumped) HOT 5
- Parser takes only first character from the filename and says “File not found” HOT 4
- The lack of documentation
- global scaling
- Update requirements.txt with librosa version 0.6.x HOT 2
- a little confusion
- Inference pipeline
- Update requirements.txt and there is no json file outputed HOT 2
- Could you share your best model?
- thanks for this project,and here are some solutions of the problems i met with. for reference only.
- How to train for a different audio sampling rate? HOT 1
- MemoryError HOT 1
- [BUG]: Validation against test data
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