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kuleshov / audio-super-res

Audio super resolution using neural networks
MIT License
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./singlespeaker.lr0.000300.1.g4.b64.meta does not exist. #57

Open FurkanGozukara opened 1 year ago

FurkanGozukara commented 1 year ago

I have successfully installed

downloaded pre trained model and extracted into the src folder like below

image

running this command

python run.py eval --logname ./singlespeaker.lr0.000300.1.g4.b64 --out-label singlespeaker-out --wav-file-list list.txt --r 4 --pool_size 2 --strides 2 --model audiotfilm

here full logs

(audio-super-res) C:\audio super res\src>python run.py eval --logname ./singlespeaker.lr0.000300.1.g4.b64 --out-label singlespeaker-out --wav-file-list list.txt --r 4 --pool_size 2 --strides 2 --model audiotfilm
2023-02-15 22:59:17.350890: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2023-02-15 22:59:17.351062: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Using TensorFlow backend.
audiotfilm
2023-02-15 22:59:20.811747: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-02-15 22:59:20.812488: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2023-02-15 22:59:20.826132: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3060 computeCapability: 8.6
coreClock: 1.777GHz coreCount: 28 deviceMemorySize: 12.00GiB deviceMemoryBandwidth: 335.32GiB/s
2023-02-15 22:59:20.827827: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2023-02-15 22:59:20.829273: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found
2023-02-15 22:59:20.830615: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found
2023-02-15 22:59:20.836817: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-02-15 22:59:20.838811: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-02-15 22:59:20.847516: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-02-15 22:59:20.849067: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found
2023-02-15 22:59:20.850036: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-02-15 22:59:20.850899: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] 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...
2023-02-15 22:59:20.851740: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-02-15 22:59:20.892047: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-02-15 22:59:20.892146: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]
2023-02-15 22:59:20.892631: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
Traceback (most recent call last):
  File "run.py", line 176, in <module>
    main()
  File "run.py", line 173, in main
    args.func(args)
  File "run.py", line 130, in eval
    model.load(args.logname) # from default checkpoint
  File "C:\audio super res\src\models\model.py", line 178, in load
    self.saver = tf.compat.v1.train.import_meta_graph(meta)
  File "C:\Users\King\.conda\envs\audio-super-res\lib\site-packages\tensorflow\python\training\saver.py", line 1461, in import_meta_graph
    **kwargs)[0]
  File "C:\Users\King\.conda\envs\audio-super-res\lib\site-packages\tensorflow\python\training\saver.py", line 1475, in _import_meta_graph_with_return_elements
    meta_graph_def = meta_graph.read_meta_graph_file(meta_graph_or_file)
  File "C:\Users\King\.conda\envs\audio-super-res\lib\site-packages\tensorflow\python\framework\meta_graph.py", line 630, in read_meta_graph_file
    raise IOError("File %s does not exist." % filename)
OSError: File ./singlespeaker.lr0.000300.1.g4.b64.meta does not exist.
FurkanGozukara commented 1 year ago

here the installation logs no errors


(base) C:\audio super res>conda env create -f environment.yaml
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
  current version: 22.9.0
  latest version: 23.1.0

Please update conda by running

    $ conda update -n base -c defaults conda

Preparing transaction: done
Verifying transaction: done
Executing transaction: / "By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html"

| "By downloading and using the cuDNN conda packages, you accept the terms and conditions of the NVIDIA cuDNN EULA - https://docs.nvidia.com/deeplearning/cudnn/sla/index.html"

done
Installing pip dependencies: - Ran pip subprocess with arguments:
['C:\\Users\\King\\.conda\\envs\\audio-super-res\\python.exe', '-m', 'pip', 'install', '-U', '-r', 'C:\\audio super res\\condaenv.g3opewu9.requirements.txt']
Pip subprocess output:
Collecting tensorflow==2.4.1
  Downloading tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)
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Collecting keras==2.4.0
  Downloading Keras-2.4.0-py2.py3-none-any.whl (170 kB)
     ---------------------------------------- 170.2/170.2 kB ? eta 0:00:00
Collecting numpy==1.19.5
  Downloading numpy-1.19.5-cp37-cp37m-win_amd64.whl (13.2 MB)
     --------------------------------------- 13.2/13.2 MB 11.3 MB/s eta 0:00:00
Collecting scipy==1.6.0
  Downloading scipy-1.6.0-cp37-cp37m-win_amd64.whl (32.5 MB)
     ---------------------------------------- 32.5/32.5 MB 1.3 MB/s eta 0:00:00
Collecting librosa==0.8.1
  Using cached librosa-0.8.1-py3-none-any.whl (203 kB)
Collecting h5py==2.10.0
  Downloading h5py-2.10.0-cp37-cp37m-win_amd64.whl (2.5 MB)
     ---------------------------------------- 2.5/2.5 MB 231.5 kB/s eta 0:00:00
Collecting matplotlib==3.3.4
  Downloading matplotlib-3.3.4-cp37-cp37m-win_amd64.whl (8.5 MB)
     ---------------------------------------- 8.5/8.5 MB 11.3 MB/s eta 0:00:00
Collecting tqdm==4.58.0
  Downloading tqdm-4.58.0-py2.py3-none-any.whl (73 kB)
     ---------------------------------------- 73.2/73.2 kB ? eta 0:00:00
Collecting typing-extensions~=3.7.4
  Downloading typing_extensions-3.7.4.3-py3-none-any.whl (22 kB)
Collecting gast==0.3.3
  Downloading gast-0.3.3-py2.py3-none-any.whl (9.7 kB)
Collecting tensorboard~=2.4
  Using cached tensorboard-2.11.2-py3-none-any.whl (6.0 MB)
Collecting grpcio~=1.32.0
  Downloading grpcio-1.32.0-cp37-cp37m-win_amd64.whl (2.5 MB)
     ---------------------------------------- 2.5/2.5 MB 11.5 MB/s eta 0:00:00
Collecting astunparse~=1.6.3
  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting flatbuffers~=1.12.0
  Downloading flatbuffers-1.12-py2.py3-none-any.whl (15 kB)
Collecting tensorflow-estimator<2.5.0,>=2.4.0
  Downloading tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462 kB)
     -------------------------------------- 462.0/462.0 kB 9.8 MB/s eta 0:00:00
Collecting keras-preprocessing~=1.1.2
  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)
     ---------------------------------------- 42.6/42.6 kB ? eta 0:00:00
Collecting opt-einsum~=3.3.0
  Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Collecting google-pasta~=0.2
  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
Collecting protobuf>=3.9.2
  Downloading protobuf-4.21.12-cp37-cp37m-win_amd64.whl (526 kB)
     ------------------------------------- 526.5/526.5 kB 11.0 MB/s eta 0:00:00
Collecting six~=1.15.0
  Downloading six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting termcolor~=1.1.0
  Downloading termcolor-1.1.0.tar.gz (3.9 kB)
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Requirement already satisfied: wheel~=0.35 in c:\users\king\.conda\envs\audio-super-res\lib\site-packages (from tensorflow==2.4.1->-r C:\audio super res\condaenv.g3opewu9.requirements.txt (line 1)) (0.38.4)
Requirement already satisfied: wrapt~=1.12.1 in c:\users\king\appdata\roaming\python\python37\site-packages (from tensorflow==2.4.1->-r C:\audio super res\condaenv.g3opewu9.requirements.txt (line 1)) (1.12.1)
Collecting absl-py~=0.10
  Downloading absl_py-0.15.0-py3-none-any.whl (132 kB)
     -------------------------------------- 132.0/132.0 kB 7.6 MB/s eta 0:00:00
Collecting pyyaml
  Downloading PyYAML-6.0-cp37-cp37m-win_amd64.whl (153 kB)
     ---------------------------------------- 153.2/153.2 kB ? eta 0:00:00
Collecting audioread>=2.0.0
  Using cached audioread-3.0.0.tar.gz (377 kB)
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Collecting packaging>=20.0
  Using cached packaging-23.0-py3-none-any.whl (42 kB)
Collecting scikit-learn!=0.19.0,>=0.14.0
  Downloading scikit_learn-1.0.2-cp37-cp37m-win_amd64.whl (7.1 MB)
     ---------------------------------------- 7.1/7.1 MB 11.4 MB/s eta 0:00:00
Collecting soundfile>=0.10.2
  Downloading soundfile-0.12.1-py2.py3-none-win_amd64.whl (1.0 MB)
     ---------------------------------------- 1.0/1.0 MB 12.8 MB/s eta 0:00:00
Collecting resampy>=0.2.2
  Using cached resampy-0.4.2-py3-none-any.whl (3.1 MB)
Collecting numba>=0.43.0
  Downloading numba-0.56.4-cp37-cp37m-win_amd64.whl (2.5 MB)
     ---------------------------------------- 2.5/2.5 MB 11.3 MB/s eta 0:00:00
Collecting pooch>=1.0
  Using cached pooch-1.6.0-py3-none-any.whl (56 kB)
Collecting joblib>=0.14
  Using cached joblib-1.2.0-py3-none-any.whl (297 kB)
Collecting decorator>=3.0.0
  Using cached decorator-5.1.1-py3-none-any.whl (9.1 kB)
Collecting python-dateutil>=2.1
  Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
Collecting cycler>=0.10
  Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting pillow>=6.2.0
  Downloading Pillow-9.4.0-cp37-cp37m-win_amd64.whl (2.5 MB)
     ---------------------------------------- 2.5/2.5 MB 10.6 MB/s eta 0:00:00
Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3
  Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)
Collecting kiwisolver>=1.0.1
  Downloading kiwisolver-1.4.4-cp37-cp37m-win_amd64.whl (54 kB)
     ---------------------------------------- 54.9/54.9 kB 2.8 MB/s eta 0:00:00
Requirement already satisfied: setuptools in c:\users\king\.conda\envs\audio-super-res\lib\site-packages (from numba>=0.43.0->librosa==0.8.1->-r C:\audio super res\condaenv.g3opewu9.requirements.txt (line 5)) (67.3.1)
Collecting importlib-metadata
  Using cached importlib_metadata-6.0.0-py3-none-any.whl (21 kB)
Collecting llvmlite<0.40,>=0.39.0dev0
  Downloading llvmlite-0.39.1-cp37-cp37m-win_amd64.whl (23.2 MB)
     --------------------------------------- 23.2/23.2 MB 11.1 MB/s eta 0:00:00
Collecting requests>=2.19.0
  Using cached requests-2.28.2-py3-none-any.whl (62 kB)
Collecting appdirs>=1.3.0
  Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting threadpoolctl>=2.0.0
  Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Collecting cffi>=1.0
  Downloading cffi-1.15.1-cp37-cp37m-win_amd64.whl (179 kB)
     ---------------------------------------- 179.3/179.3 kB ? eta 0:00:00
Collecting tensorboard-data-server<0.7.0,>=0.6.0
  Using cached tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB)
Collecting tensorboard-plugin-wit>=1.6.0
  Using cached tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)
Collecting google-auth<3,>=1.6.3
  Using cached google_auth-2.16.0-py2.py3-none-any.whl (177 kB)
Collecting werkzeug>=1.0.1
  Downloading Werkzeug-2.2.3-py3-none-any.whl (233 kB)
     ------------------------------------- 233.6/233.6 kB 14.0 MB/s eta 0:00:00
Collecting protobuf>=3.9.2
  Downloading protobuf-3.20.3-cp37-cp37m-win_amd64.whl (905 kB)
     ------------------------------------- 905.1/905.1 kB 11.5 MB/s eta 0:00:00
Collecting markdown>=2.6.8
  Using cached Markdown-3.4.1-py3-none-any.whl (93 kB)
Collecting google-auth-oauthlib<0.5,>=0.4.1
  Using cached google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)
Collecting pycparser
  Using cached pycparser-2.21-py2.py3-none-any.whl (118 kB)
Collecting pyasn1-modules>=0.2.1
  Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
Collecting rsa<5,>=3.1.4
  Using cached rsa-4.9-py3-none-any.whl (34 kB)
Collecting cachetools<6.0,>=2.0.0
  Using cached cachetools-5.3.0-py3-none-any.whl (9.3 kB)
Collecting requests-oauthlib>=0.7.0
  Using cached requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)
Collecting zipp>=0.5
  Using cached zipp-3.13.0-py3-none-any.whl (6.7 kB)
Collecting certifi>=2017.4.17
  Using cached certifi-2022.12.7-py3-none-any.whl (155 kB)
Collecting urllib3<1.27,>=1.21.1
  Using cached urllib3-1.26.14-py2.py3-none-any.whl (140 kB)
Collecting idna<4,>=2.5
  Using cached idna-3.4-py3-none-any.whl (61 kB)
Collecting charset-normalizer<4,>=2
  Downloading charset_normalizer-3.0.1-cp37-cp37m-win_amd64.whl (94 kB)
     ---------------------------------------- 94.0/94.0 kB ? eta 0:00:00
Collecting MarkupSafe>=2.1.1
  Downloading MarkupSafe-2.1.2-cp37-cp37m-win_amd64.whl (16 kB)
Collecting pyasn1<0.5.0,>=0.4.6
  Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
Collecting oauthlib>=3.0.0
  Using cached oauthlib-3.2.2-py3-none-any.whl (151 kB)
Building wheels for collected packages: audioread, termcolor
  Building wheel for audioread (setup.py): started
  Building wheel for audioread (setup.py): finished with status 'done'
  Created wheel for audioread: filename=audioread-3.0.0-py3-none-any.whl size=23736 sha256=c49587967fe34ecc72bb7d7b423f912b4db3ca9a96f43d9b0a7a00d4434cc982
  Stored in directory: c:\users\king\appdata\local\pip\cache\wheels\71\a4\fa\24175dada88ca37d7fd22ffec10b33cb0a4909d7d07f04101f
  Building wheel for termcolor (setup.py): started
  Building wheel for termcolor (setup.py): finished with status 'done'
  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4855 sha256=feb02d834cba92cffd3c20b3f165b08e932654e5ec261289431ebf0fd843544d
  Stored in directory: c:\users\king\appdata\local\pip\cache\wheels\3f\e3\ec\8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2
Successfully built audioread termcolor
Installing collected packages: typing-extensions, termcolor, tensorflow-estimator, tensorboard-plugin-wit, pyasn1, flatbuffers, charset-normalizer, appdirs, zipp, urllib3, tqdm, threadpoolctl, tensorboard-data-server, six, rsa, pyyaml, pyparsing, pycparser, pyasn1-modules, protobuf, pillow, packaging, oauthlib, numpy, MarkupSafe, llvmlite, kiwisolver, joblib, idna, gast, decorator, cycler, certifi, cachetools, audioread, werkzeug, scipy, requests, python-dateutil, opt-einsum, keras-preprocessing, importlib-metadata, h5py, grpcio, google-pasta, google-auth, cffi, astunparse, absl-py, soundfile, scikit-learn, requests-oauthlib, pooch, numba, matplotlib, markdown, resampy, google-auth-oauthlib, tensorboard, librosa, tensorflow, keras
Successfully installed MarkupSafe-2.1.2 absl-py-0.15.0 appdirs-1.4.4 astunparse-1.6.3 audioread-3.0.0 cachetools-5.3.0 certifi-2022.12.7 cffi-1.15.1 charset-normalizer-3.0.1 cycler-0.11.0 decorator-5.1.1 flatbuffers-1.12 gast-0.3.3 google-auth-2.16.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.32.0 h5py-2.10.0 idna-3.4 importlib-metadata-6.0.0 joblib-1.2.0 keras-2.4.0 keras-preprocessing-1.1.2 kiwisolver-1.4.4 librosa-0.8.1 llvmlite-0.39.1 markdown-3.4.1 matplotlib-3.3.4 numba-0.56.4 numpy-1.19.5 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-23.0 pillow-9.4.0 pooch-1.6.0 protobuf-3.20.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 pycparser-2.21 pyparsing-3.0.9 python-dateutil-2.8.2 pyyaml-6.0 requests-2.28.2 requests-oauthlib-1.3.1 resampy-0.4.2 rsa-4.9 scikit-learn-1.0.2 scipy-1.6.0 six-1.15.0 soundfile-0.12.1 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.4.1 tensorflow-estimator-2.4.0 termcolor-1.1.0 threadpoolctl-3.1.0 tqdm-4.58.0 typing-extensions-3.7.4.3 urllib3-1.26.14 werkzeug-2.2.3 zipp-3.13.0

done
#
# To activate this environment, use
#
#     $ conda activate audio-super-res
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Retrieving notices: ...working... done
FurkanGozukara commented 1 year ago

@kuleshov @WMRamadan @Sawyerb @jimmsta @Lootwig

any help is very much appreciated

WMRamadan commented 1 year ago

@FurkanGozukara Interesting I have not tried this on Windows, can you clarify which version of Windows this is? (Windows 10 or 11 - Pro or Home)

Did you check that singlespeaker.lr0.000300.1.g4.b64.meta exists and that you have followed the instructions on how to train the model?

Can you show how you trained the model?

FurkanGozukara commented 1 year ago

@FurkanGozukara Interesting I have not tried this on Windows, can you clarify which version of Windows this is? (Windows 10 or 11 - Pro or Home)

Did you check that singlespeaker.lr0.000300.1.g4.b64.meta exists and that you have followed the instructions on how to train the model?

Can you show how you trained the model?

i didnt train any model. i have downloaded the model file you shared. the pre trained one.

extracted like this into the src

image

then i did run this command

python run.py eval --logname ./singlespeaker.lr0.000300.1.g4.b64 --out-label singlespeaker-out --wav-file-list list.txt --r 4 --pool_size 2 --strides 2 --model audiotfilm

I am using Windows 10 pro

OS Name Microsoft Windows 10 Pro Version 10.0.19045 Build 19045

@WMRamadan

WMRamadan commented 1 year ago

@FurkanGozukara That model was trained using a batch size of 16. Use the following:

python run.py eval   --logname ./singlespeaker.lr0.000300.1.g4.b16   --out-label singlespeaker-out   --wav-file-list ../data/vctk/speaker1/speaker1-val-files.txt   --r 4   --pool_size 2   --strides 2   --model audiotfilm
FurkanGozukara commented 1 year ago

@FurkanGozukara That model was trained using a batch size of 16. Use the following:

python run.py eval   --logname ./singlespeaker.lr0.000300.1.g4.b16   --out-label singlespeaker-out   --wav-file-list ../data/vctk/speaker1/speaker1-val-files.txt   --r 4   --pool_size 2   --strides 2   --model audiotfilm

thank you so much

started processing but via CPU not using GPU due to below errors. I have RTX 3060 and installed exactly as following the instructions. during installation 0 error as I have show above messages

processing not ended yet so cant comment on it. also it is hard coded looking for this folder for data to be in

\data\vctk\VCTK-Corpus\wav48\p225

(audio-super-res) C:\audio super res\src>python run.py eval   --logname ./singlespeaker.lr0.000300.1.g4.b16   --out-label singlespeaker-out   --wav-file-list list.txt   --r 4   --pool_size 2   --strides 2   --model audiotfilm
2023-02-16 13:14:18.942358: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2023-02-16 13:14:18.942472: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Using TensorFlow backend.
audiotfilm
2023-02-16 13:14:21.400084: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-02-16 13:14:21.400818: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2023-02-16 13:14:21.417991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3060 computeCapability: 8.6
coreClock: 1.777GHz coreCount: 28 deviceMemorySize: 12.00GiB deviceMemoryBandwidth: 335.32GiB/s
2023-02-16 13:14:21.419569: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2023-02-16 13:14:21.420944: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found
2023-02-16 13:14:21.422254: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found
2023-02-16 13:14:21.425587: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-02-16 13:14:21.426690: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-02-16 13:14:21.430739: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-02-16 13:14:21.432268: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found
2023-02-16 13:14:21.433068: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-02-16 13:14:21.433137: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] 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...
2023-02-16 13:14:21.433786: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-02-16 13:14:21.470877: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-02-16 13:14:21.470998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]
2023-02-16 13:14:21.471057: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-02-16 13:14:22.490435: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
5.mp3
WMRamadan commented 1 year ago

@FurkanGozukara I don't think you are following the instructions correctly.

Did you do the following step: https://github.com/kuleshov/audio-super-res#retrieving-data

Also did you install CUDA Toolkit on your machine? (https://developer.nvidia.com/cuda-toolkit-archive)

FurkanGozukara commented 1 year ago

@FurkanGozukara I don't think you are following the instructions correctly.

Did you do the following step: https://github.com/kuleshov/audio-super-res#retrieving-data

Also did you install CUDA Toolkit on your machine? (https://developer.nvidia.com/cuda-toolkit-archive)

I am not doing any training. that sections is about training?

also processing ended

here inputs and outputs

p225.zip

1 by 1 sound files

input : http://sndup.net/f7j8

singlespeaker-out.hr : http://sndup.net/hs5f

no I didnt install cuda. going to install now. you should put that into the description. i am installing latest version

image

FurkanGozukara commented 1 year ago

could you process this input and give me what output you are getting?

i didn't notice any difference : http://sndup.net/f7j8

perhaps mine not working?

input mp3.zip

@WMRamadan

Sawyerb commented 1 year ago

@FurkanGozukara try this:

python run.py eval --logname ./singlespeaker.lr0.000300.1.g4.b16/model.ckpt-10401 --out-label singlespeaker-out --wav-file-list ../data/vctk/speaker1/speaker1-val-files.txt --r 4 --pool_size 2 --strides 2 --model audiotfilm

The logname should include the checkpoint that you're using.

FurkanGozukara commented 1 year ago

@FurkanGozukara try this:

python run.py eval --logname ./singlespeaker.lr0.000300.1.g4.b16/model.ckpt-10401 --out-label singlespeaker-out --wav-file-list ../data/vctk/speaker1/speaker1-val-files.txt --r 4 --pool_size 2 --strides 2 --model audiotfilm

The logname should include the checkpoint that you're using.

I don't have ckpt file it is not included in pre trained model. Can you upload it for me?

Or it processes input file list and generates a new ckpt?

I don't want training. I want to give input and get enhanced output

Sawyerb commented 1 year ago

I see the checkpoint in the screenshot you posted: image

satani99 commented 1 year ago

Hey @FurkanGozukara How much time did it take to run inference?

FurkanGozukara commented 1 year ago

Hey @FurkanGozukara How much time did it take to run inference?

I can't remember

But I have this tutorial now

https://youtu.be/OiMRlqcgDL0