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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'model'})

This happened while the csv dataset builder was generating data using

hf://datasets/wearemusicai/moisesdb/benchmark/oracle4.csv (at revision 7577c73577f3ba0c9c13a170e2557713619c41ce)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              track_id: string
              model: string
              stem: string
              sdr: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 820
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'track_id': Value(dtype='string', id=None), 'stem': Value(dtype='string', id=None), 'sdr': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'model'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/wearemusicai/moisesdb/benchmark/oracle4.csv (at revision 7577c73577f3ba0c9c13a170e2557713619c41ce)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Unnamed: 0
int64
track_id
string
stem
string
sdr
float64
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drums
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drums
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drums
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8.960332
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End of preview.

MoisesDB

Moises Dataset for Source Separation

Dataset Summary

MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms.

Download the data

Please download the dataset at our research website, extract it and configure the environment variable MOISESDB_PATH accordingly.

export MOISESDB_PATH=./moises-db-data

The directory structure should be

moisesdb:
    moisesdb_v0.1
        track uuid 0
        track uuid 1
        .
        .
        .

Install

You can install this package with

pip install git+https://github.com/moises-ai/moises-db.git

Usage

MoisesDB

After downloading and configuring the path for the dataset, you can create an instance of MoisesDB to access the tracks. You can also provide the dataset path with the data_path argument.

from moisesdb.dataset import MoisesDB

db = MoisesDB(
    data_path='./moisesdb',
    sample_rate=44100
)

The MoisesDB object has iterator properties that you can use to access all files within the dataset.

n_songs = len(db)
track = db[0]  # Returns a MoisesDBTrack object

MoisesDBTrack

The MoisesDBTrack object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.

You can access all the stems and mixture from the stem and audio properties. The stem property returns a dictionary whith available stems as keys and nd.array on values. The audio property results in a nd.array with the mixture.

track = db[0]
stems = track.stems  # stems = {'vocals': ..., 'bass': ..., ...}
mixture track.audio # mixture = nd.array

The MoisesDBTrack object also contains other non-audio information from the track such as:

  • track.id
  • track.provider
  • track.artist
  • track.name
  • track.genre
  • track.sources
  • track.bleedings
  • track.activity

The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the save_stems method of the MoisesDBTrack.

track = db[0]
path =  './moises-db-stems/0'
track.save_stems(path)

Performance Evaluation

We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the MoisesDB. These results are located in csv files at the benchmark folder.

Citing

If you used the MoisesDB dataset on your research, please cite the following paper.

@misc{pereira2023moisesdb,
      title={Moisesdb: A dataset for source separation beyond 4-stems}, 
      author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl},
      year={2023},
      eprint={2307.15913},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

Licensing

MoisesDB is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

For the complete license terms, please visit: https://creativecommons.org/licenses/by-nc-sa/4.0/

See LICENSE file for details.

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