Can't I use `musicnn` on Windows10?
scillidan opened this issue · 3 comments
scillidan commented
I have python 3.8.5.
At the time of installation musicnn, it prompts me the errors about packages.
stable-diffusion\env λ .\python.exe -m pip install musicnn
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting musicnn
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/1b/6f/38229e7d99c438e11114bbfa39c8c39185458c398011d0b6d7d7c7401617/musicnn-0.1.0-py3-none-any.whl (29.3 MB)
Requirement already satisfied: tensorflow>=1.14 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from musicnn) (2.10.0)
Collecting librosa>=0.7.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e4/1c/23ef2fd02913d65d43dc7516fc829af709314a66c6f0bdc2e361fdcecc2d/librosa-0.9.2-py3-none-any.whl (214 kB)
Requirement already satisfied: numba>=0.45.1 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from librosa>=0.7.0->musicnn) (0.56.2)
Requirement already satisfied: scipy>=1.2.0 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from librosa>=0.7.0->musicnn) (1.9.1)
Requirement already satisfied: decorator>=4.0.10 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from librosa>=0.7.0->musicnn) (5.1.1)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5b/da/bd63187b2ca1b97c04c270df90c934a97cbe512c8238ab65c89c1b043ae2/librosa-0.9.1-py3-none-any.whl (213 kB)
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Requirement already satisfied: packaging>=20.0 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from librosa>=0.7.0->musicnn) (21.3)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5a/90/61c239b4bf9aee2ac16dcf1a1f05d5f112ac0ad301f06feacb42fa6834aa/librosa-0.9.0-py3-none-any.whl (211 kB)
|████████████████████████████████| 211 kB 6.8 MB/s
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/54/19/a0e2bdc94bc0d1555e4f9bc4099a0751da83fa6e1e6157ec005564f8a98a/librosa-0.8.1-py3-none-any.whl (203 kB)
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Collecting audioread>=2.0.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5d/cb/82a002441902dccbe427406785db07af10182245ee639ea9f4d92907c923/audioread-3.0.0.tar.gz (377 kB)
Collecting joblib>=0.14
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/91/d4/3b4c8e5a30604df4c7518c562d4bf0502f2fa29221459226e140cf846512/joblib-1.2.0-py3-none-any.whl (297 kB)
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Requirement already satisfied: setuptools<60 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from numba>=0.45.1->librosa>=0.7.0->musicnn) (59.8.0)
Requirement already satisfied: importlib-metadata in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from numba>=0.45.1->librosa>=0.7.0->musicnn) (4.12.0)
Collecting numba>=0.43.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/39/cc/bfb25cf17904eef3aba1e091758f1959c34f1ff558dd09a64feaeb3bd001/numba-0.56.0-cp38-cp38-win_amd64.whl (2.5 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a4/46/a4759a5bd7bbd09fa6b70dfca0ad55ee0fae72d48ca0febdc53b252cbfcf/numba-0.55.2-cp38-cp38-win_amd64.whl (2.4 MB)
|████████████████████████████████| 2.4 MB ...
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/56/96/10b81c9fc38360b4a9867da218c607d54ce3a2d3973c0a179f91bdd030f4/numba-0.55.1-cp38-cp38-win_amd64.whl (2.4 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7b/bf/374490d2da3fb4ab510e70517447d8eff72cc9052f3b361d88d52037dbcd/numba-0.55.0-cp38-cp38-win_amd64.whl (2.4 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/a8/b91e3e7a60fc9bcb9f1133475e44e375f2e14b73e6b96f7e6a3e6f2303ac/numba-0.54.1-cp38-cp38-win_amd64.whl (2.3 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6e/1e/4fbf2390ffa7256abf4f0e0daf8c430f66ad263386aa1e175dc22573e4cf/numba-0.54.0-cp38-cp38-win_amd64.whl (2.3 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9c/ce/d0a7573290bfe9a394d5ef3798d7f63978eeb940b6ceac75e792d660b9a3/numba-0.53.1-cp38-cp38-win_amd64.whl (2.3 MB)
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Collecting llvmlite<0.37,>=0.36.0rc1
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Collecting numpy<1.17,>=1.14.5
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b7/6f/24647f014eef9b67a24adfcbcd4f4928349b4a0f8393b3d7fe648d4d2de3/numpy-1.16.6.zip (5.1 MB)
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Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from packaging>=20.0->librosa>=0.7.0->musicnn) (3.0.9)
Collecting pooch>=1.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/8d/64/8e1bfeda3ba0f267b2d9a918e8ca51db8652d0e1a3412a5b3dbce85d90b6/pooch-1.6.0-py3-none-any.whl (56 kB)
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Collecting appdirs>=1.3.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/3b/00/2344469e2084fb287c2e0b57b72910309874c3245463acd6cf5e3db69324/appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f2/d3/5209fd2132452f199b1ddf0d084f9fd5f5f910840e3b282f005b48a503e1/resampy-0.4.2-py3-none-any.whl (3.1 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/96/f6/819514dd8be3681fdd1dc81a94f5e1d51019c18e9e7b351c8e097a86e77f/resampy-0.4.1-py3-none-any.whl (3.1 MB)
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/79/75/e22272b9c2185fc8f3af6ce37229708b45e8b855fd4bc38b4d6b040fff65/resampy-0.2.2.tar.gz (323 kB)
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Collecting scikit-learn>=0.19.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cb/0b/e085436fce6daf49786bf0e1107ade7dcd22eb6110abb44b6eb6f29f9270/scikit_learn-1.1.2-cp38-cp38-win_amd64.whl (7.3 MB)
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Collecting scikit-learn!=0.19.0,>=0.14.0
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Downloading https://pypi.tuna.tsinghua.edu.cn/packages/50/f5/2bfd87943a29870bdbe00346c9f3b0545dd7a188201297a33189f866f04e/scikit_learn-1.0.2-cp38-cp38-win_amd64.whl (7.2 MB)
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Collecting scipy>=1.0.0
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Collecting soundfile>=0.10.2
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b8/de/24e4035f06540ebb4e9993238ede787063875b003e79c537511d32a74d29/SoundFile-0.10.3.post1-py2.py3.cp26.cp27.cp32.cp33.cp34.cp35.cp36.pp27.pp32.pp33-none-win_amd64.whl (689 kB)
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Requirement already satisfied: google-pasta>=0.1.1 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from tensorflow>=1.14->musicnn) (0.2.0)
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Collecting tensorflow>=1.14
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0d/f0/1e509035d97a093a7f1f5da3df6aad5cddf798ce4f9cab9056b330207c82/tensorflow-2.9.2-cp38-cp38-win_amd64.whl (444.1 MB)
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Requirement already satisfied: oauthlib>=3.0.0 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=1.14->musicnn) (3.2.1)
Collecting tensorflow-estimator<2.8,~=2.7.0rc0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/db/de/3a71ad41b87f9dd424e3aec3b0794a60f169fa7e9a9a1e3dd44290b86dd6/tensorflow_estimator-2.7.0-py2.py3-none-any.whl (463 kB)
|████████████████████████████████| 463 kB ...
Collecting threadpoolctl>=2.0.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Requirement already satisfied: MarkupSafe>=2.1.1 in d:\stable-diffusion-ui\stable-diffusion\env\lib\site-packages (from werkzeug>=1.0.1->tensorboard~=2.6->tensorflow>=1.14->musicnn) (2.1.1)Building wheels for collected packages: audioread, numpy, resampy
Building wheel for audioread (setup.py) ... done
Created wheel for audioread: filename=audioread-3.0.0-py3-none-any.whl size=23706 sha256=84986037dad8f4f2eac9abf197d63eeb558521031259ad97736e51232f573d5b
Stored in directory: c:\users\scillidan\appdata\local\pip\cache\wheels\e2\c3\9c\f19ae5a03f8862d9f0776b0c0570f1fdd60a119d90954e3f39
Building wheel for numpy (setup.py) ... done
Created wheel for numpy: filename=numpy-1.16.6-cp38-cp38-win_amd64.whl size=3946142 sha256=bcc845f029c836a5e8fca77bd06c15078245fd9af6eb524788792b96ec95490b
Stored in directory: c:\users\scillidan\appdata\local\pip\cache\wheels\56\40\08\b4589e620c27337e8cb7a4c70a960ce67252310edf064f3f3d
Building wheel for resampy (setup.py) ... done
Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320732 sha256=35dd4bc318504c9a23a02ab8ce92b53dcba3497d5ee36dca3464291825788b65
Stored in directory: c:\users\scillidan\appdata\local\pip\cache\wheels\e2\d8\d2\ebe9bdee286d235792e9d9c694bf38bd355562be46b9c04283
Successfully built audioread numpy resampy
Installing collected packages: numpy, llvmlite, threadpoolctl, scipy, numba, joblib, appdirs, tensorflow-estimator, soundfile, scikit-learn, resampy, pooch, keras, audioread, tensorflow, librosa, musicnn
Attempting uninstall: numpy
Found existing installation: numpy 1.23.3
Uninstalling numpy-1.23.3:
Successfully uninstalled numpy-1.23.3
Attempting uninstall: llvmlite
Found existing installation: llvmlite 0.39.1
Uninstalling llvmlite-0.39.1:
Successfully uninstalled llvmlite-0.39.1
Attempting uninstall: scipy
Found existing installation: scipy 1.9.1
Uninstalling scipy-1.9.1:
Successfully uninstalled scipy-1.9.1
Attempting uninstall: numba
Found existing installation: numba 0.56.2
Uninstalling numba-0.56.2:
Successfully uninstalled numba-0.56.2
Attempting uninstall: tensorflow-estimator
Found existing installation: tensorflow-estimator 2.10.0
Uninstalling tensorflow-estimator-2.10.0:
Successfully uninstalled tensorflow-estimator-2.10.0
Attempting uninstall: keras
Found existing installation: keras 2.10.0
Uninstalling keras-2.10.0:
Successfully uninstalled keras-2.10.0
Attempting uninstall: tensorflow
Found existing installation: tensorflow 2.10.0
Uninstalling tensorflow-2.10.0:
Successfully uninstalled tensorflow-2.10.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
transformers 4.19.2 requires numpy>=1.17, but you have numpy 1.16.6 which is incompatible.
torchmetrics 0.6.0 requires numpy>=1.17.2, but you have numpy 1.16.6 which is incompatible.
tifffile 2022.8.12 requires numpy>=1.19.2, but you have numpy 1.16.6 which is incompatible.
scikit-image 0.19.3 requires numpy>=1.17.0, but you have numpy 1.16.6 which is incompatible.
pywavelets 1.3.0 requires numpy>=1.17.3, but you have numpy 1.16.6 which is incompatible.
pytorch-lightning 1.4.2 requires numpy>=1.17.2, but you have numpy 1.16.6 which is incompatible.
pandas 1.4.4 requires numpy>=1.18.5; platform_machine != "aarch64" and platform_machine != "arm64" and python_version < "3.10", but you have numpy 1.16.6 which is incompatible.
opencv-python 4.1.2.30 requires numpy>=1.17.3, but you have numpy 1.16.6 which is incompatible.
opencv-python-headless 4.6.0.66 requires numpy>=1.17.3; python_version >= "3.8", but you have numpy 1.16.6 which is incompatible.
matplotlib 3.5.3 requires numpy>=1.17, but you have numpy 1.16.6 which is incompatible.
basicsr 1.4.2 requires numpy>=1.17, but you have numpy 1.16.6 which is incompatible.
Successfully installed appdirs-1.4.4 audioread-3.0.0 joblib-1.2.0 keras-2.7.0 librosa-0.8.1 llvmlite-0.36.0 musicnn-0.1.0 numba-0.53.1 numpy-1.16.6 pooch-1.6.0 resampy-0.2.2 scikit-learn-1.0.2 scipy-1.7.3 soundfile-0.10.3.post1 tensorflow-2.7.4 tensorflow-estimator-2.7.0 threadpoolctl-3.1.0LinusOstlund commented
I think you can fix this by removing the upper bound restriction on the Numpy version dependency. I have opened a PR:
scillidan commented
Thanks, man.
I change the requires in setup.py, with numpy<=1.17,>=1.14.5.
Then python37 setup install, and python37 -m pip install musicnn.
Finally, it seems works.
I will try more recently.
on git master [!?] via py v3.7.9 took 8s
musicnn λ python -m musicnn.tagger "D:\\home\\Art School Girlfriend - Bending Back.wav" --save out.tags
D:\home\musicnn\musicnn\models.py:58: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
normalized_input = tf.compat.v1.layers.batch_normalization(expand_input, training=is_training)
2022-10-16 11:28:18.578355: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found
2022-10-16 11:28:18.578543: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
D:\home\musicnn\musicnn\models.py:107: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead.
activation=activation)
D:\home\musicnn\musicnn\models.py:108: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
bn_conv = tf.compat.v1.layers.batch_normalization(conv, training=is_training)
D:\home\musicnn\musicnn\models.py:111: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead.
strides=[1, bn_conv.shape[2]])
D:\home\musicnn\musicnn\models.py:121: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead.
activation=activation)
D:\home\musicnn\musicnn\models.py:122: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
bn_conv = tf.compat.v1.layers.batch_normalization(conv, training=is_training)
D:\home\musicnn\musicnn\models.py:125: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead.
strides=[1, bn_conv.shape[2]])
D:\home\musicnn\musicnn\models.py:139: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead.
activation=tf.nn.relu)
D:\home\musicnn\musicnn\models.py:140: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
bn_conv1 = tf.compat.v1.layers.batch_normalization(conv1, training=is_training)
D:\home\musicnn\musicnn\models.py:149: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead.
activation=tf.nn.relu)
D:\home\musicnn\musicnn\models.py:150: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
bn_conv2 = tf.compat.v1.layers.batch_normalization(conv2, training=is_training)
D:\home\musicnn\musicnn\models.py:160: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead.
activation=tf.nn.relu)
D:\home\musicnn\musicnn\models.py:161: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
bn_conv3 = tf.compat.v1.layers.batch_normalization(conv3, training=is_training)
D:\home\musicnn\musicnn\models.py:176: UserWarning: `tf.layers.flatten` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Flatten` instead.
flat_pool = tf.compat.v1.layers.flatten(tmp_pool)
D:\home\musicnn\musicnn\models.py:177: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
flat_pool = tf.compat.v1.layers.batch_normalization(flat_pool, training=is_training)
D:\home\musicnn\musicnn\models.py:178: UserWarning: `tf.layers.dropout` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dropout` instead.
flat_pool_dropout = tf.compat.v1.layers.dropout(flat_pool, rate=0.5, training=is_training)
D:\home\musicnn\musicnn\models.py:181: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.
activation=tf.nn.relu)
D:\home\musicnn\musicnn\models.py:182: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation).
bn_dense = tf.compat.v1.layers.batch_normalization(dense, training=is_training)
D:\home\musicnn\musicnn\models.py:183: UserWarning: `tf.layers.dropout` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dropout` instead.
dense_dropout = tf.compat.v1.layers.dropout(bn_dense, rate=0.5, training=is_training)
D:\home\musicnn\musicnn\models.py:188: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.
units=num_classes)
2022-10-16 11:28:19.346772: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-16 11:28:19.364027: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
Computing spectrogram (w/ librosa) and tags (w/ tensorflow).. done!scillidan commented
On Windows10, seems there is a lack of tensorflow… version that can be used. It‘s documentation says that can built it byself, but I did not succeed🤡.
This fork say that can to delete something.
Finally, it works on wsl. Same as this docker-image.
I actually installed it to use this cool repo(https://github.com/jonshamir/muser).