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توضیحات

DreamSound Class for CNN Activation Layer Sonification

ویژگی	مقدار
سیستم عامل	-
نام فایل	dreamsound-0.1.6.3
نام	dreamsound
نسخه کتابخانه	0.1.6.3
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Fede Camara Halac
ایمیل نویسنده	fch226@nyu.edu
آدرس صفحه اصلی	https://github.com/fdch/dreamsound
آدرس اینترنتی	https://pypi.org/project/dreamsound/
مجوز	-

# DreamSound DreamSound is a python package for sonic deep dream generation. ## Description Inspired by the [DeepDream](https://www.tensorflow.org/tutorials/generative/deepdream) project, DreamSound plays a sound file to [Yamnet](https://github.com/tensorflow/models/tree/master/research/audioset/yamnet), a pre-trained neural network, and Yamnet returns a dreamed sound. Internally, DreamSound takes the gradients of a class from the pre-trained yamnet model and filters them with an original sound with some combination technique. ## Example Head to [this Google Colab](https://colab.research.google.com/github/fdch/dreamsound/blob/main/DreamSound_Package_Example.ipynb) for a quick example on how to get started with the module. An old version can be accessible [in this other Google Colab](https://colab.research.google.com/github/fdch/dreamsound/blob/main/DreamSound_v_1_5.ipynb). Yet an older version is [here](https://colab.research.google.com/github/fdch/dreamsound/blob/main/DreamSound_v_1.ipynb), which goes hand in hand with an [early paper](https://github.com/fdch/dreamsound/blob/main/docs/papers/DreamSound__CNN_Activation_Layer_Sonification.pdf) we did on the matter of Convolutional Neural Network Activation Layer Sonification, or what we called DreamSound. ## Install Dreamsound depends on the following pip packages you can `pip install`: ``` requests numpy matplotlib IPython librosa tensorflow soundfile ``` First, install the dependencies ```sh python3 -m pip install -r requirements.txt ``` Install the dreamsound package using pip: ```sh python3 -m pip install dreamsound ``` The pip project is hosted at PyPi: https://pypi.org/project/dreamsound/ **NOTE: you need to download the yamnet model before importing the dreamsound module. Please, continue reading.** ## Prepare Create a directory for your project and relocate there. ```sh mkdir dream_test cd dream_test ``` ### Run the Yamnet Downloader. The Yamnet Downloader file *does not* come with the pip distribution. However, it is distributed on this repository. If you do not want to clone this repository, simply do: `curl -O https://raw.githubusercontent.com/fdch/dreamsound/main/yamnet_downloader.py` on this same directory, and run: ```sh python3 yamnet_downloader.py ``` Alternatively, you can get Yamnet yourself, crudely like this: ```sh git clone https://github.com/fdch/models.git models mv models/research/audioset/yamnet/* . rm -rf models curl -O https://storage.googleapis.com/audioset/yamnet.h5 ``` ## Usage example You must have the yamnet model on the same directory. Now, you can import the dreamsound module and use the class. This code loads some files from disk and passes them to the `DreamSound` class from the `dreamsound` module. This looks something like this: ``` >>> import dreamsound INFO:tensorflow:Enabling eager execution INFO:tensorflow:Enabling v2 tensorshape INFO:tensorflow:Enabling resource variables INFO:tensorflow:Enabling tensor equality INFO:tensorflow:Enabling control flow v2 >>> ds = dreamsound.DreamSound(["../audio/original.wav", "../audio/cat.wav"]) Loading audio files... Done. I have now 2 audio files in memory. Using last layer. Yamnet loaded, using layer:activation_1 Dreamer started. ``` ## Filtering There are two types of filtering, auto or targetted: ### Auto Filtering: Filter the first audio with it's dreamed self ``` >>> ds(audio_index=0) Running step 0, class: Whistling... ... Writing ./audio/Whistle-9-orig.wav... Writing ./audio/Whistle-9-diff.wav... Writing ./audio/Whistle-9-filt.wav... Writing ./audio/Whistle-9-hard.wav... Writing ./audio/Whistle-9-grad.wav... ``` ### Targetted Filtering: Filter the first with a dreamed target ``` >>> ds(audio_index=0, tgt=1) Target class: Animal... Running step 0, class: Whistling... Running step 1, class: Whistle... Running step 2, class: Whistle... Running step 3, class: Whistle... Running step 4, class: Whistle... Running step 5, class: Whistle... Running step 6, class: Whistle... Running step 7, class: Whistle... Running step 8, class: Flute... Running step 9, class: Whistle... Writing ./audio/Whistle-9-orig-tgt-Animal.wav... Writing ./audio/Whistle-9-diff-tgt-Animal.wav... Writing ./audio/Whistle-9-filt-tgt-Animal.wav... Writing ./audio/Whistle-9-hard-tgt-Animal.wav... Writing ./audio/Whistle-9-grad-tgt-Animal.wav... ``` ## Recurse Finally, you can pass no arguments to continue filtering recursively ``` >>> ds() Target class: Animal... Running step 10, class: Whistle... Running step 11, class: Whistle... Running step 12, class: Wind instrument, woodwind instrument... Running step 13, class: Wind instrument, woodwind instrument... Running step 14, class: Flute... Running step 15, class: Flute... Running step 16, class: Wind instrument, woodwind instrument... Running step 17, class: Whistle... Running step 18, class: Whistle... Running step 19, class: Music... Writing ./audio/Music-19-orig-tgt-Animal.wav... Writing ./audio/Music-19-diff-tgt-Animal.wav... Writing ./audio/Music-19-filt-tgt-Animal.wav... Writing ./audio/Music-19-hard-tgt-Animal.wav... Writing ./audio/Music-19-grad-tgt-Animal.wav... ``` ## Class Variables You can change any of the following before or after calling the class: ```python sr = 22050 max_dur = 10 patch_hop = 0.1 win_length = 2048 hop_length = 128 pad_end = False loss_power = 0.001 plot_every = 10 figsize = (10,8) top_db = 80.0 step_size = 0.95 output_type = 3 steps = 10 threshold = 1e-07 classid = None maxloss = True elapsed = 0 recurse = False target = None power = 1.0 audio_dir = "./audio/" image_dir = "./image/" ``` # Authors Fede Camara Halac (https://github.com/fdch) Matias Delgadino (https://github.com/zaytam) # Acknowledgements YamNet AudioSet

نیازمندی

مقدار	نام
-	requests
-	numpy
-	matplotlib
-	librosa
-	tensorflow

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl dreamsound-0.1.6.3:

pip install dreamsound-0.1.6.3.whl

نصب پکیج tar.gz dreamsound-0.1.6.3:

pip install dreamsound-0.1.6.3.tar.gz