Hello,
It seems that running the code in 2023 is extremely difficult because the versions of numpy, librosa, and numba create those well-known dependency hells that one runs into with pip.
This is no complain about the code itself, which is wonderful and well-written. Thank you so much for it and the instructions to run it.
In case it is helpful to anyone, I managed to find a winning combination of packages using conda. I leave the environment.yml here, which might be useful to make this code future-proof (hopefully), as all of these libraries will continue to update in the future.
Here it is. What you need to do, instead of the regular pip install -r requirements.txt is the following:
conda env create -n mlpsinger -f environment.yml
conda activate mlpsinger
python inference.py --checkpoint_path checkpoints/default/model.pt
And the contents of environment.yml are these:
name: mlpsinger
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=1_gnu
- ca-certificates=2023.08.22=h06a4308_0
- ld_impl_linux-64=2.38=h1181459_1
- libffi=3.4.4=h6a678d5_0
- libgcc-ng=11.2.0=h1234567_1
- libgomp=11.2.0=h1234567_1
- libstdcxx-ng=11.2.0=h1234567_1
- ncurses=6.4=h6a678d5_0
- openssl=3.0.12=h7f8727e_0
- pip=23.3.1=py39h06a4308_0
- python=3.9.18=h955ad1f_0
- readline=8.2=h5eee18b_0
- setuptools=68.0.0=py39h06a4308_0
- sqlite=3.41.2=h5eee18b_0
- tk=8.6.12=h1ccaba5_0
- tzdata=2023c=h04d1e81_0
- wheel=0.41.2=py39h06a4308_0
- xz=5.4.5=h5eee18b_0
- zlib=1.2.13=h5eee18b_0
- pip:
- absl-py==2.0.0
- audioread==3.0.1
- cachetools==5.3.2
- certifi==2023.11.17
- cffi==1.16.0
- charset-normalizer==3.3.2
- contourpy==1.2.0
- cycler==0.12.1
- decorator==5.1.1
- filelock==3.13.1
- fonttools==4.46.0
- fsspec==2023.12.2
- google-auth==2.25.2
- google-auth-oauthlib==1.1.0
- grpcio==1.60.0
- idna==3.6
- importlib-metadata==7.0.0
- importlib-resources==6.1.1
- jinja2==3.1.2
- joblib==1.3.2
- kiwisolver==1.4.5
- lazy-loader==0.3
- librosa==0.8.1
- llvmlite==0.41.1
- markdown==3.5.1
- markupsafe==2.1.3
- matplotlib==3.8.2
- mido==1.2.9
- mpmath==1.3.0
- msgpack==1.0.7
- networkx==3.2.1
- numba==0.58.1
- numpy==1.22.0
- nvidia-cublas-cu12==12.1.3.1
- nvidia-cuda-cupti-cu12==12.1.105
- nvidia-cuda-nvrtc-cu12==12.1.105
- nvidia-cuda-runtime-cu12==12.1.105
- nvidia-cudnn-cu12==8.9.2.26
- nvidia-cufft-cu12==11.0.2.54
- nvidia-curand-cu12==10.3.2.106
- nvidia-cusolver-cu12==11.4.5.107
- nvidia-cusparse-cu12==12.1.0.106
- nvidia-nccl-cu12==2.18.1
- nvidia-nvjitlink-cu12==12.3.101
- nvidia-nvtx-cu12==12.1.105
- oauthlib==3.2.2
- packaging==23.2
- pillow==10.1.0
- platformdirs==4.1.0
- pooch==1.8.0
- protobuf==4.23.4
- pyasn1==0.5.1
- pyasn1-modules==0.3.0
- pycparser==2.21
- pyparsing==3.1.1
- python-dateutil==2.8.2
- requests==2.31.0
- requests-oauthlib==1.3.1
- resampy==0.4.2
- rsa==4.9
- scikit-learn==1.3.2
- scipy==1.11.4
- six==1.16.0
- soundfile==0.12.1
- soxr==0.3.7
- sympy==1.12
- tensorboard==2.15.1
- tensorboard-data-server==0.7.2
- threadpoolctl==3.2.0
- torch==2.1.1
- torchaudio==2.1.1
- triton==2.1.0
- typing-extensions==4.9.0
- urllib3==2.1.0
- werkzeug==3.0.1
- zipp==3.17.0
Hello,
It seems that running the code in 2023 is extremely difficult because the versions of
numpy,librosa, andnumbacreate those well-known dependency hells that one runs into with pip.This is no complain about the code itself, which is wonderful and well-written. Thank you so much for it and the instructions to run it.
In case it is helpful to anyone, I managed to find a winning combination of packages using conda. I leave the
environment.ymlhere, which might be useful to make this code future-proof (hopefully), as all of these libraries will continue to update in the future.Here it is. What you need to do, instead of the regular
pip install -r requirements.txtis the following:And the contents of
environment.ymlare these: