Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

error when training ! #509

Closed
kotaxyz opened this issue Nov 30, 2023 · 0 comments
Closed

error when training ! #509

kotaxyz opened this issue Nov 30, 2023 · 0 comments

Comments

@kotaxyz
Copy link

kotaxyz commented Nov 30, 2023

hello , thanks alot for this awesome work , i installed the repo on my wsl2 ubuntu 22.04 and installed the packages using conda i also used gpu so i checked comments and was able to use gpu but the issue is that when i reach the training i get this error

2023-11-30 13:41:31.626134: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-30 13:41:31.626214: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-30 13:41:31.627161: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-11-30 13:41:32.676863: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: 

TensorFlow Addons (TFA) has ended development and introduction of new features.
TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.
Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). 

For more information see: https://github.com/tensorflow/addons/issues/2807 

  warnings.warn(
I1130 13:41:37.002290 139773808506688 ddsp_run.py:180] Restore Dir: /home/genesis/github/ddsp/ddsp-solo-instrument
I1130 13:41:37.002723 139773808506688 ddsp_run.py:181] Save Dir: /home/genesis/github/ddsp/ddsp-solo-instrument
I1130 13:41:37.037069 139773808506688 ddsp_run.py:153] Operative config not found in /home/genesis/github/ddsp/ddsp-solo-instrument
I1130 13:41:37.056087 139773808506688 ddsp_run.py:185] Operative Gin Config:
import ddsp
import ddsp.training as ddsp2

# Macros:
# ==============================================================================
batch_size = 1
evaluators = [@BasicEvaluator]
learning_rate = 0.0003

# Parameters for Add:
# ==============================================================================
Add.name = 'add'

# Parameters for Autoencoder:
# ==============================================================================
Autoencoder.decoder = @decoders.RnnFcDecoder()
Autoencoder.encoder = None
Autoencoder.losses = [@losses.SpectralLoss()]
Autoencoder.preprocessor = @preprocessing.F0LoudnessPreprocessor()
Autoencoder.processor_group = @processors.ProcessorGroup()

# Parameters for evaluate:
# ==============================================================================
evaluate.batch_size = 32
evaluate.data_provider = @data.TFRecordProvider()
evaluate.evaluator_classes = %evaluators
evaluate.num_batches = 5

# Parameters for F0LoudnessPreprocessor:
# ==============================================================================
F0LoudnessPreprocessor.time_steps = 1000

# Parameters for FilteredNoise:
# ==============================================================================
FilteredNoise.n_samples = 64000
FilteredNoise.name = 'filtered_noise'
FilteredNoise.scale_fn = @core.exp_sigmoid
FilteredNoise.window_size = 0

# Parameters for get_model:
# ==============================================================================
get_model.model = @models.Autoencoder()

# Parameters for Harmonic:
# ==============================================================================
Harmonic.n_samples = 64000
Harmonic.name = 'harmonic'
Harmonic.normalize_below_nyquist = True
Harmonic.sample_rate = 16000
Harmonic.scale_fn = @core.exp_sigmoid

# Parameters for MfccTimeDistributedRnnEncoder:
# ==============================================================================
MfccTimeDistributedRnnEncoder.rnn_channels = 512
MfccTimeDistributedRnnEncoder.rnn_type = 'gru'
MfccTimeDistributedRnnEncoder.z_dims = 16
MfccTimeDistributedRnnEncoder.z_time_steps = 125

# Parameters for ProcessorGroup:
# ==============================================================================
ProcessorGroup.dag = \
    [(@synths.Harmonic(), ['amps', 'harmonic_distribution', 'f0_hz']),
     (@synths.FilteredNoise(), ['noise_magnitudes']),
     (@processors.Add(), ['filtered_noise/signal', 'harmonic/signal']),
     (@effects.Reverb(), ['add/signal'])]

# Parameters for Reverb:
# ==============================================================================
Reverb.name = 'reverb'
Reverb.reverb_length = 48000
Reverb.trainable = True

# Parameters for RnnFcDecoder:
# ==============================================================================
RnnFcDecoder.ch = 512
RnnFcDecoder.input_keys = ('ld_scaled', 'f0_scaled')
RnnFcDecoder.layers_per_stack = 3
RnnFcDecoder.output_splits = \
    (('amps', 1), ('harmonic_distribution', 60), ('noise_magnitudes', 65))
RnnFcDecoder.rnn_channels = 512
RnnFcDecoder.rnn_type = 'gru'

# Parameters for sample:
# ==============================================================================
sample.batch_size = 16
sample.ckpt_delay_secs = 300
sample.data_provider = @data.TFRecordProvider()
sample.evaluator_classes = %evaluators
sample.num_batches = 1

# Parameters for SpectralLoss:
# ==============================================================================
SpectralLoss.logmag_weight = 1.0
SpectralLoss.loss_type = 'L1'
SpectralLoss.mag_weight = 1.0

# Parameters for TFRecordProvider:
# ==============================================================================
TFRecordProvider.file_pattern = \
    '/home/genesis/github/ddsp/dataset/train.tfrecord-*'

# Parameters for train:
# ==============================================================================
train.batch_size = %batch_size
train.data_provider = @data.TFRecordProvider()
train.num_steps = 30000
train.steps_per_save = 300
train.steps_per_summary = 300

# Parameters for Trainer:
# ==============================================================================
Trainer.checkpoints_to_keep = 10
Trainer.grad_clip_norm = 3.0
Trainer.learning_rate = %learning_rate
Trainer.lr_decay_rate = 0.98
Trainer.lr_decay_steps = 10000

I1130 13:41:37.197552 139773808506688 train_util.py:78] Defaulting to MirroredStrategy
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I1130 13:41:39.469777 139773808506688 mirrored_strategy.py:423] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I1130 13:41:41.570941 139773808506688 trainers.py:142] Building the model...
Model: "autoencoder"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 f0_loudness_preprocessor (  multiple                  0         
 F0LoudnessPreprocessor)                                         
                                                                 
 rnn_fc_decoder (RnnFcDecod  multiple                  4801150   
 er)                                                             
                                                                 
 processor_group (Processor  multiple                  48000     
 Group)                                                          
                                                                 
 spectral_loss (SpectralLos  multiple                  0         
 s)                                                              
                                                                 
=================================================================
Total params: 4849150 (18.50 MB)
Trainable params: 4849150 (18.50 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
I1130 13:42:00.145416 139773808506688 trainers.py:95] Restoring from checkpoint...
I1130 13:42:00.145583 139773808506688 trainers.py:103] Trainer restoring the full model
I1130 13:42:00.146856 139773808506688 train_util.py:267] No existing checkpoint found in /home/genesis/github/ddsp/ddsp-solo-instrument, skipping checkpoint loading.
Traceback (most recent call last):
  File "/home/genesis/miniconda3/envs/ddsp/bin/ddsp_run", line 8, in <module>
    sys.exit(console_entry_point())
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/ddsp/training/ddsp_run.py", line 229, in console_entry_point
    app.run(main)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/absl/app.py", line 308, in run
    _run_main(main, args)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/absl/app.py", line 254, in _run_main
    sys.exit(main(argv))
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/ddsp/training/ddsp_run.py", line 199, in main
    train_util.train(data_provider=gin.REQUIRED,
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/gin/config.py", line 1605, in gin_wrapper
    utils.augment_exception_message_and_reraise(e, err_str)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/gin/utils.py", line 41, in augment_exception_message_and_reraise
    raise proxy.with_traceback(exception.__traceback__) from None
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/gin/config.py", line 1582, in gin_wrapper
    return fn(*new_args, **new_kwargs)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/ddsp/training/train_util.py", line 289, in train
    losses = trainer.train_step(dataset_iter)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/tmp/__autograph_generated_fileg0vv7gcp.py", line 12, in tf__train_step
    losses = ag__.converted_call(ag__.ld(self).run, (ag__.ld(self).step_fn, ag__.ld(batch)), None, fscope)
  File "/tmp/__autograph_generated_file3in1ge92.py", line 13, in tf__run
    retval_ = ag__.converted_call(ag__.ld(self).strategy.run, (ag__.ld(fn),), dict(args=ag__.ld(args), kwargs=ag__.ld(kwargs)), fscope)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py", line 144, in from_value
    elif isinstance(value, trace.SupportsTracingProtocol):
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/typing_extensions.py", line 689, in __instancecheck__
    val = inspect.getattr_static(instance, attr)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/inspect.py", line 1743, in getattr_static
    instance_result = _check_instance(obj, attr)
  File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/inspect.py", line 1690, in _check_instance
    instance_dict = object.__getattribute__(obj, "__dict__")
TypeError: in user code:

    File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/ddsp/training/trainers.py", line 158, in train_step  *
        losses = self.run(self.step_fn, batch)
    File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/ddsp/training/trainers.py", line 138, in run  *
        return self.strategy.run(fn, args=args, kwargs=kwargs)
    File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py", line 144, in from_value
        elif isinstance(value, trace.SupportsTracingProtocol):
    File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/site-packages/typing_extensions.py", line 689, in __instancecheck__
        val = inspect.getattr_static(instance, attr)
    File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/inspect.py", line 1743, in getattr_static
        instance_result = _check_instance(obj, attr)
    File "/home/genesis/miniconda3/envs/ddsp/lib/python3.10/inspect.py", line 1690, in _check_instance
        instance_dict = object.__getattribute__(obj, "__dict__")

    TypeError: this __dict__ descriptor does not support '_DictWrapper' objects

  In call to configurable 'train' (<function train at 0x7f1f2971a050>)

here is the env i used python 3.10 :

name: ddsp
channels:

  • pytorch
  • anaconda
  • nvidia
  • conda-forge
  • defaults
    dependencies:
  • _libgcc_mutex=0.1=main
  • _openmp_mutex=5.1=1_gnu
  • aiohttp=3.8.1=py310h5764c6d_1
  • aiosignal=1.3.1=pyhd8ed1ab_0
  • anyio=3.5.0=py310h06a4308_0
  • argon2-cffi=21.3.0=pyhd3eb1b0_0
  • argon2-cffi-bindings=21.2.0=py310h7f8727e_0
  • async-lru=2.0.4=py310h06a4308_0
  • async-timeout=4.0.3=pyhd8ed1ab_0
  • attrs=23.1.0=py310h06a4308_0
  • babel=2.11.0=py310h06a4308_0
  • backcall=0.2.0=pyhd3eb1b0_0
  • beautifulsoup4=4.12.2=py310h06a4308_0
  • blas=1.0=mkl
  • bleach=4.1.0=pyhd3eb1b0_0
  • bottleneck=1.3.5=py310ha9d4c09_0
  • brotli-python=1.0.9=py310h6a678d5_7
  • bzip2=1.0.8=h7b6447c_0
  • ca-certificates=2023.11.17=hbcca054_0
  • cachetools=5.3.2=pyhd8ed1ab_0
  • certifi=2023.11.17=pyhd8ed1ab_0
  • cffi=1.16.0=py310h5eee18b_0
  • comm=0.1.2=py310h06a4308_0
  • cryptography=41.0.3=py310hdda0065_0
  • cuda-cudart=11.8.89=0
  • cuda-cupti=11.8.87=0
  • cuda-libraries=11.8.0=0
  • cuda-nvrtc=11.8.89=0
  • cuda-nvtx=11.8.86=0
  • cuda-runtime=11.8.0=0
  • cudatoolkit=11.8.0=h6a678d5_0
  • cudnn=8.9.2.26=cuda11_0
  • cyrus-sasl=2.1.28=h52b45da_1
  • dbus=1.13.18=hb2f20db_0
  • debugpy=1.6.7=py310h6a678d5_0
  • decorator=5.1.1=pyhd3eb1b0_0
  • defusedxml=0.7.1=pyhd3eb1b0_0
  • expat=2.5.0=h6a678d5_0
  • ffmpeg=4.3=hf484d3e_0
  • filelock=3.13.1=py310h06a4308_0
  • fontconfig=2.14.1=h4c34cd2_2
  • freetype=2.12.1=h4a9f257_0
  • frozenlist=1.4.0=py310h5eee18b_0
  • giflib=5.2.1=h5eee18b_3
  • glib=2.69.1=he621ea3_2
  • gmp=6.2.1=h295c915_3
  • gmpy2=2.1.2=py310heeb90bb_0
  • gnutls=3.6.15=he1e5248_0
  • google-auth=2.23.4=pyhca7485f_0
  • google-colab=1.0.0=pyh44b312d_0
  • gst-plugins-base=1.14.1=h6a678d5_1
  • gstreamer=1.14.1=h5eee18b_1
  • icu=73.1=h6a678d5_0
  • intel-openmp=2023.1.0=hdb19cb5_46306
  • ipykernel=6.25.0=py310h2f386ee_0
  • ipywidgets=8.0.4=py310h06a4308_0
  • jinja2=3.1.2=py310h06a4308_0
  • jpeg=9e=h5eee18b_1
  • json5=0.9.6=pyhd3eb1b0_0
  • jsonschema=4.19.2=py310h06a4308_0
  • jsonschema-specifications=2023.7.1=py310h06a4308_0
  • jupyter=1.0.0=py310h06a4308_8
  • jupyter-lsp=2.2.0=py310h06a4308_0
  • jupyter_client=8.6.0=py310h06a4308_0
  • jupyter_console=6.6.3=py310h06a4308_0
  • jupyter_core=5.5.0=py310h06a4308_0
  • jupyter_events=0.8.0=py310h06a4308_0
  • jupyter_server=2.10.0=py310h06a4308_0
  • jupyter_server_terminals=0.4.4=py310h06a4308_1
  • jupyterlab=4.0.8=py310h06a4308_0
  • jupyterlab_pygments=0.1.2=py_0
  • jupyterlab_server=2.25.1=py310h06a4308_0
  • jupyterlab_widgets=3.0.9=py310h06a4308_0
  • krb5=1.20.1=h143b758_1
  • lame=3.100=h7b6447c_0
  • lcms2=2.12=h3be6417_0
  • ld_impl_linux-64=2.38=h1181459_1
  • lerc=3.0=h295c915_0
  • libclang13=14.0.6=default_he11475f_1
  • libcublas=11.11.3.6=0
  • libcufft=10.9.0.58=0
  • libcufile=1.8.1.2=0
  • libcups=2.4.2=h2d74bed_1
  • libcurand=10.3.4.101=0
  • libcusolver=11.4.1.48=0
  • libcusparse=11.7.5.86=0
  • libdeflate=1.17=h5eee18b_1
  • libedit=3.1.20221030=h5eee18b_0
  • libffi=3.4.4=h6a678d5_0
  • libgcc-ng=11.2.0=h1234567_1
  • libgomp=11.2.0=h1234567_1
  • libiconv=1.16=h7f8727e_2
  • libidn2=2.3.4=h5eee18b_0
  • libjpeg-turbo=2.0.0=h9bf148f_0
  • libllvm14=14.0.6=hdb19cb5_3
  • libnpp=11.8.0.86=0
  • libnvjpeg=11.9.0.86=0
  • libpng=1.6.39=h5eee18b_0
  • libpq=12.15=hdbd6064_1
  • libsodium=1.0.18=h7b6447c_0
  • libstdcxx-ng=11.2.0=h1234567_1
  • libtasn1=4.19.0=h5eee18b_0
  • libtiff=4.5.1=h6a678d5_0
  • libunistring=0.9.10=h27cfd23_0
  • libuuid=1.41.5=h5eee18b_0
  • libwebp=1.3.2=h11a3e52_0
  • libwebp-base=1.3.2=h5eee18b_0
  • libxcb=1.15=h7f8727e_0
  • libxkbcommon=1.0.1=h5eee18b_1
  • libxml2=2.10.4=hf1b16e4_1
  • llvm-openmp=14.0.6=h9e868ea_0
  • lz4-c=1.9.4=h6a678d5_0
  • matplotlib-inline=0.1.6=py310h06a4308_0
  • mistune=2.0.4=py310h06a4308_0
  • mkl=2023.1.0=h213fc3f_46344
  • mkl-service=2.4.0=py310h5eee18b_1
  • mkl_fft=1.3.8=py310h5eee18b_0
  • mkl_random=1.2.4=py310hdb19cb5_0
  • mpc=1.1.0=h10f8cd9_1
  • mpfr=4.0.2=hb69a4c5_1
  • mpmath=1.3.0=py310h06a4308_0
  • multidict=6.0.4=py310h5eee18b_0
  • mysql=5.7.24=h721c034_2
  • nbclient=0.8.0=py310h06a4308_0
  • nbconvert=7.10.0=py310h06a4308_0
  • nbformat=5.9.2=py310h06a4308_0
  • ncurses=6.4=h6a678d5_0
  • nettle=3.7.3=hbbd107a_1
  • networkx=3.1=py310h06a4308_0
  • notebook=7.0.6=py310h06a4308_0
  • notebook-shim=0.2.3=py310h06a4308_0
  • numexpr=2.8.7=py310h85018f9_0
  • numpy-base=1.26.0=py310hb5e798b_0
  • openh264=2.1.1=h4ff587b_0
  • openjpeg=2.4.0=h3ad879b_0
  • openssl=3.0.12=h7f8727e_0
  • overrides=7.4.0=py310h06a4308_0
  • pandocfilters=1.5.0=pyhd3eb1b0_0
  • parso=0.8.3=pyhd3eb1b0_0
  • pcre=8.45=h295c915_0
  • pickleshare=0.7.5=pyhd3eb1b0_1003
  • pip=23.3.1=py310h06a4308_0
  • ply=3.11=py310h06a4308_0
  • portpicker=1.6.0=pyhd8ed1ab_0
  • prometheus_client=0.14.1=py310h06a4308_0
  • prompt_toolkit=3.0.36=hd3eb1b0_0
  • ptyprocess=0.7.0=pyhd3eb1b0_2
  • pure_eval=0.2.2=pyhd3eb1b0_0
  • pyasn1=0.5.1=pyhd8ed1ab_0
  • pyasn1-modules=0.3.0=pyhd8ed1ab_0
  • pycparser=2.21=pyhd3eb1b0_0
  • pyopenssl=23.2.0=py310h06a4308_0
  • pyqt=5.15.10=py310h6a678d5_0
  • pyqt5-sip=12.13.0=py310h5eee18b_0
  • pysocks=1.7.1=py310h06a4308_0
  • python=3.10.13=h955ad1f_0
  • python-dateutil=2.8.2=pyhd3eb1b0_0
  • python-fastjsonschema=2.16.2=py310h06a4308_0
  • python-json-logger=2.0.7=py310h06a4308_0
  • python-tzdata=2023.3=pyhd8ed1ab_0
  • python_abi=3.10=2_cp310
  • pytorch=2.1.1=py3.10_cuda11.8_cudnn8.7.0_0
  • pytorch-cuda=11.8=h7e8668a_5
  • pytorch-mutex=1.0=cuda
  • pytz=2023.3.post1=py310h06a4308_0
  • pyu2f=0.1.5=pyhd8ed1ab_0
  • pyyaml=6.0.1=py310h5eee18b_0
  • pyzmq=25.1.0=py310h6a678d5_0
  • qt-main=5.15.2=h53bd1ea_10
  • qtconsole=5.5.0=py310h06a4308_0
  • qtpy=2.4.1=py310h06a4308_0
  • readline=8.2=h5eee18b_0
  • referencing=0.30.2=py310h06a4308_0
  • requests=2.31.0=py310h06a4308_0
  • rfc3339-validator=0.1.4=py310h06a4308_0
  • rfc3986-validator=0.1.1=py310h06a4308_0
  • rpds-py=0.10.6=py310hb02cf49_0
  • rsa=4.9=pyhd8ed1ab_0
  • send2trash=1.8.2=py310h06a4308_0
  • setuptools=68.0.0=py310h06a4308_0
  • sip=6.7.12=py310h6a678d5_0
  • six=1.16.0=pyhd3eb1b0_1
  • sniffio=1.2.0=py310h06a4308_1
  • soupsieve=2.5=py310h06a4308_0
  • sqlite=3.41.2=h5eee18b_0
  • stack_data=0.2.0=pyhd3eb1b0_0
  • sympy=1.11.1=py310h06a4308_0
  • tbb=2021.8.0=hdb19cb5_0
  • terminado=0.17.1=py310h06a4308_0
  • tinycss2=1.2.1=py310h06a4308_0
  • tk=8.6.12=h1ccaba5_0
  • tomli=2.0.1=py310h06a4308_0
  • torchaudio=2.1.1=py310_cu118
  • torchtriton=2.1.0=py310
  • torchvision=0.16.1=py310_cu118
  • typing_extensions=4.7.1=py310h06a4308_0
  • tzdata=2023c=h04d1e81_0
  • webencodings=0.5.1=py310h06a4308_1
  • websocket-client=0.58.0=py310h06a4308_4
  • wheel=0.41.2=py310h06a4308_0
  • widgetsnbextension=4.0.5=py310h06a4308_0
  • wrapt=1.14.1=py310h5764c6d_0
  • xz=5.4.2=h5eee18b_0
  • yaml=0.2.5=h7b6447c_0
  • yarl=1.7.2=py310h5764c6d_2
  • zeromq=4.3.4=h2531618_0
  • zlib=1.2.13=h5eee18b_0
  • zstd=1.5.5=hc292b87_0
  • pip:
    • absl-py==1.4.0
    • apache-beam==2.52.0
    • array-record==0.5.0
    • asttokens==2.4.1
    • astunparse==1.6.3
    • audioread==3.0.1
    • bokeh==3.3.1
    • charset-normalizer==3.3.2
    • chex==0.1.85
    • click==8.1.7
    • cloudml-hypertune==0.1.0.dev6
    • cloudpickle==2.2.1
    • contourpy==1.2.0
    • crcmod==1.7
    • crepe==0.0.14
    • cycler==0.12.1
    • ddsp==1.9.0
    • dill==0.3.1.1
    • dm-tree==0.1.8
    • dnspython==2.4.2
    • docopt==0.6.2
    • etils==1.5.2
    • exceptiongroup==1.2.0
    • executing==2.0.1
    • fastavro==1.9.0
    • fasteners==0.19
    • flatbuffers==23.5.26
    • flax==0.7.5
    • fonttools==4.45.1
    • fsspec==2023.10.0
    • future==0.18.3
    • gast==0.4.0
    • gin-config==0.5.0
    • google-api-core==2.14.0
    • google-auth-oauthlib==1.1.0
    • google-cloud-core==2.3.3
    • google-cloud-storage==2.13.0
    • google-crc32c==1.5.0
    • google-pasta==0.2.0
    • google-resumable-media==2.6.0
    • googleapis-common-protos==1.61.0
    • grpcio==1.59.3
    • h5py==3.10.0
    • hdfs==2.7.3
    • hmmlearn==0.3.0
    • httplib2==0.22.0
    • idna==3.6
    • imageio==2.33.0
    • importlib-resources==6.1.1
    • intervaltree==3.1.0
    • ipython==8.18.1
    • jax==0.4.20
    • jaxlib==0.4.20
    • jedi==0.19.1
    • joblib==1.3.2
    • js2py==0.74
    • keras==2.15.0
    • keras-preprocessing==1.1.2
    • kiwisolver==1.4.5
    • lazy-loader==0.3
    • libclang==16.0.6
    • librosa==0.10.1
    • llvmlite==0.41.1
    • markdown==3.5.1
    • markdown-it-py==3.0.0
    • markupsafe==2.1.3
    • matplotlib==3.8.2
    • mdurl==0.1.2
    • mido==1.3.0
    • mir-eval==0.7
    • ml-dtypes==0.2.0
    • msgpack==1.0.7
    • nest-asyncio==1.5.8
    • note-seq==0.0.3
    • numba==0.58.1
    • numpy==1.24.4
    • nvidia-cublas-cu11==11.11.3.6
    • nvidia-cublas-cu12==12.2.5.6
    • nvidia-cuda-cupti-cu12==12.2.142
    • nvidia-cuda-nvcc-cu12==12.2.140
    • nvidia-cuda-nvrtc-cu12==12.2.140
    • nvidia-cuda-runtime-cu12==12.2.140
    • nvidia-cudnn-cu11==8.6.0.163
    • nvidia-cudnn-cu12==8.9.4.25
    • nvidia-cufft-cu12==11.0.8.103
    • nvidia-curand-cu12==10.3.3.141
    • nvidia-cusolver-cu12==11.5.2.141
    • nvidia-cusparse-cu12==12.1.2.141
    • nvidia-nccl-cu12==2.16.5
    • nvidia-nvjitlink-cu12==12.2.140
    • nvidia-tensorrt==99.0.0
    • oauthlib==3.2.2
    • objsize==0.6.1
    • opt-einsum==3.3.0
    • optax==0.1.7
    • orbax-checkpoint==0.4.3
    • orjson==3.9.10
    • packaging==23.2
    • pandas==2.1.3
    • pexpect==4.9.0
    • pillow==10.1.0
    • platformdirs==4.0.0
    • pooch==1.8.0
    • pretty-midi==0.2.10
    • promise==2.3
    • prompt-toolkit==3.0.41
    • proto-plus==1.22.3
    • protobuf==3.20.0
    • psutil==5.9.6
    • pyarrow==11.0.0
    • pyarrow-hotfix==0.6
    • pybind11==2.11.1
    • pydot==1.4.2
    • pydub==0.25.1
    • pygments==2.17.2
    • pyjsparser==2.7.1
    • pymongo==4.6.1
    • pyparsing==3.1.1
    • regex==2023.10.3
    • requests-oauthlib==1.3.1
    • resampy==0.2.2
    • rich==13.7.0
    • scikit-learn==1.3.2
    • scipy==1.11.4
    • sortedcontainers==2.4.0
    • sounddevice==0.4.6
    • soundfile==0.12.1
    • soxr==0.3.7
    • stack-data==0.6.3
    • tensorboard==2.15.1
    • tensorboard-data-server==0.7.2
    • tensorboard-plugin-wit==1.8.1
    • tensorflow==2.15.0
    • tensorflow-addons==0.23.0
    • tensorflow-datasets==4.9.3
    • tensorflow-decision-forests==1.8.1
    • tensorflow-estimator==2.15.0
    • tensorflow-hub==0.15.0
    • tensorflow-io-gcs-filesystem==0.34.0
    • tensorflow-metadata==1.14.0
    • tensorflow-probability==0.23.0
    • tensorflowjs==4.13.0
    • tensorrt==8.6.1.post1
    • tensorrt-bindings==8.6.1
    • tensorrt-libs==8.6.1
    • tensorstore==0.1.45
    • termcolor==2.3.0
    • tflite-support==0.4.4
    • threadpoolctl==3.2.0
    • toml==0.10.2
    • toolz==0.12.0
    • tornado==6.4
    • tqdm==4.66.1
    • traitlets==5.14.0
    • typeguard==2.13.3
    • typing-extensions==4.8.0
    • tzlocal==5.2
    • urllib3==2.1.0
    • wcwidth==0.2.12
    • werkzeug==3.0.1
    • wurlitzer==3.0.3
    • xyzservices==2023.10.1
    • zipp==3.17.0
    • zstandard==0.22.0
      prefix: /home/genesis/miniconda3/envs/ddsp
@kotaxyz kotaxyz closed this as completed Dec 1, 2023
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant