Models

Base classes

class asteroid.models.base_models.BaseModel(sample_rate: float, in_channels: Optional[int] = 1)

Bases: torch.nn.Module

Base class for serializable models.

Defines saving/loading procedures, and separation interface to separate. Need to overwrite the forward and get_model_args methods.

Models inheriting from BaseModel can be used by asteroid.separate and by the asteroid-infer CLI. For models whose forward doesn’t go from waveform to waveform tensors, overwrite forward_wav to return waveform tensors.

Parameters

• sample_rate (float) – Operating sample rate of the model.

• in_channels – Number of input channels in the signal. If None, no checks will be performed.

property sample_rate

Operating sample rate of the model (float).

separate(*args, **kwargs)

Convenience for separate().

torch_separate(*args, **kwargs)

Convenience for torch_separate().

numpy_separate(*args, **kwargs)

Convenience for numpy_separate().

file_separate(*args, **kwargs)

Convenience for file_separate().

forward_wav(wav, *args, **kwargs)

Separation method for waveforms.

In case the network’s forward doesn’t have waveforms as input/output, overwrite this method to separate from waveform to waveform. Should return a single torch.Tensor, the separated waveforms.

Parameters

wav (torch.Tensor) – waveform array/tensor. Shape: 1D, 2D or 3D tensor, time last.

classmethod from_pretrained(pretrained_model_conf_or_path, *args, **kwargs)

Instantiate separation model from a model config (file or dict).

Parameters

• pretrained_model_conf_or_path (Union[dict, str]) – model conf as returned by serialize, or path to it. Need to contain model_args and state_dict keys.

• *args – Positional arguments to be passed to the model.

• **kwargs – Keyword arguments to be passed to the model. They overwrite the ones in the model package.

Returns

nn.Module corresponding to the pretrained model conf/URL.

Raises

ValueError if the input config file doesn't contain the keys – model_name, model_args or state_dict.

serialize()

Serialize model and output dictionary.

Returns

dict, serialized model with keys model_args and state_dict.

get_state_dict()

In case the state dict needs to be modified before sharing the model.

get_model_args()

Should return args to re-instantiate the class.

class asteroid.models.base_models.BaseEncoderMaskerDecoder(encoder, masker, decoder, encoder_activation=None)

Bases: asteroid.models.base_models.BaseModel

Base class for encoder-masker-decoder separation models.

Parameters

• encoder (Encoder) – Encoder instance.

• masker (nn.Module) – masker network.

• decoder (Decoder) – Decoder instance.

• encoder_activation (Optional[str], optional) – Activation to apply after encoder. See asteroid.masknn.activations for valid values.

forward(wav)

Enc/Mask/Dec model forward

Parameters

wav (torch.Tensor) – waveform tensor. 1D, 2D or 3D tensor, time last.

Returns

torch.Tensor, of shape (batch, n_src, time) or (n_src, time).

forward_encoder(wav: torch.Tensor) → torch.Tensor

Computes time-frequency representation of wav.

Parameters

wav (torch.Tensor) – waveform tensor in 3D shape, time last.

Returns

torch.Tensor, of shape (batch, feat, seq).

forward_masker(tf_rep: torch.Tensor) → torch.Tensor

Estimates masks from time-frequency representation.

Parameters

tf_rep (torch.Tensor) – Time-frequency representation in (batch, feat, seq).

Returns

torch.Tensor – Estimated masks

apply_masks(tf_rep: torch.Tensor, est_masks: torch.Tensor) → torch.Tensor

Applies masks to time-frequency representation.

Parameters

• tf_rep (torch.Tensor) – Time-frequency representation in (batch, feat, seq) shape.

• est_masks (torch.Tensor) – Estimated masks.

Returns

torch.Tensor – Masked time-frequency representations.

forward_decoder(masked_tf_rep: torch.Tensor) → torch.Tensor

Reconstructs time-domain waveforms from masked representations.

Parameters

masked_tf_rep (torch.Tensor) – Masked time-frequency representation.

Returns

torch.Tensor – Time-domain waveforms.

get_model_args()

Arguments needed to re-instantiate the model.

asteroid.models.base_models.BaseTasNet

alias of asteroid.models.base_models.BaseEncoderMaskerDecoder

Ready-to-use models

class asteroid.models.conv_tasnet.ConvTasNet(n_src, out_chan=None, n_blocks=8, n_repeats=3, bn_chan=128, hid_chan=512, skip_chan=128, conv_kernel_size=3, norm_type='gLN', mask_act='sigmoid', in_chan=None, causal=False, fb_name='free', kernel_size=16, n_filters=512, stride=8, encoder_activation=None, sample_rate=8000, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

ConvTasNet separation model, as described in [1].

Parameters

• n_src (int) – Number of sources in the input mixtures.

• out_chan (int, optional) – Number of bins in the estimated masks. If None, out_chan = in_chan.

• n_blocks (int, optional) – Number of convolutional blocks in each repeat. Defaults to 8.

• n_repeats (int, optional) – Number of repeats. Defaults to 3.

• bn_chan (int, optional) – Number of channels after the bottleneck.

• hid_chan (int, optional) – Number of channels in the convolutional blocks.

• skip_chan (int, optional) – Number of channels in the skip connections. If 0 or None, TDConvNet won’t have any skip connections and the masks will be computed from the residual output. Corresponds to the ConvTasnet architecture in v1 or the paper.

• conv_kernel_size (int, optional) – Kernel size in convolutional blocks.

• norm_type (str, optional) – To choose from 'BN', 'gLN', 'cLN'.

• mask_act (str, optional) – Which non-linear function to generate mask.

• in_chan (int, optional) – Number of input channels, should be equal to n_filters.

• causal (bool, optional) – Whether or not the convolutions are causal.

• fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].

• n_filters (int) – Number of filters / Input dimension of the masker net.

• kernel_size (int) – Length of the filters.

• stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.

• sample_rate (float) – Sampling rate of the model.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

References

• [1] : “Conv-TasNet: Surpassing ideal time-frequency magnitude masking for speech separation” TASLP 2019 Yi Luo, Nima Mesgarani https://arxiv.org/abs/1809.07454

class asteroid.models.conv_tasnet.VADNet(n_src, out_chan=None, n_blocks=8, n_repeats=3, bn_chan=128, hid_chan=512, skip_chan=128, conv_kernel_size=3, norm_type='gLN', mask_act='sigmoid', in_chan=None, causal=False, fb_name='free', kernel_size=16, n_filters=512, stride=8, encoder_activation=None, sample_rate=8000, **fb_kwargs)

Bases: asteroid.models.conv_tasnet.ConvTasNet

forward_decoder(masked_tf_rep: torch.Tensor) → torch.Tensor

Reconstructs time-domain waveforms from masked representations.

Parameters

masked_tf_rep (torch.Tensor) – Masked time-frequency representation.

Returns

torch.Tensor – Time-domain waveforms.

class asteroid.models.dccrnet.DCCRNet(*args, stft_n_filters=512, stft_kernel_size=400, stft_stride=100, **masknet_kwargs)

Bases: asteroid.models.dcunet.BaseDCUNet

DCCRNet as proposed in [1].

Parameters

• architecture (str) – The architecture to use, must be “DCCRN-CL”.

• stft_kernel_size (int) – STFT frame length to use

• stft_stride (int, optional) – STFT hop length to use.

• sample_rate (float) – Sampling rate of the model.

• masknet_kwargs (optional) – Passed to DCCRMaskNet

References

• [1] : “DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement”, Yanxin Hu et al. https://arxiv.org/abs/2008.00264

masknet_class

alias of asteroid.masknn.recurrent.DCCRMaskNet

forward_encoder(wav)

Computes time-frequency representation of wav.

Parameters

wav (torch.Tensor) – waveform tensor in 3D shape, time last.

Returns

torch.Tensor, of shape (batch, feat, seq).

apply_masks(tf_rep, est_masks)

Applies masks to time-frequency representation.

Parameters

• tf_rep (torch.Tensor) – Time-frequency representation in (batch, feat, seq) shape.

• est_masks (torch.Tensor) – Estimated masks.

Returns

torch.Tensor – Masked time-frequency representations.

class asteroid.models.dcunet.BaseDCUNet(architecture, stft_n_filters=1024, stft_kernel_size=1024, stft_stride=256, sample_rate=16000.0, **masknet_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

Base class for DCUNet and DCCRNet classes.

Parameters

• architecture (str) – The architecture to use. Overriden by subclasses.

• stft_n_filters (int) –

• stft_kernel_size (int) – STFT frame length to use.

• stft_stride (int, optional) – STFT hop length to use.

• sample_rate (float) – Sampling rate of the model.

• masknet_kwargs (optional) – Passed to the masknet constructor.

forward_encoder(wav)

Computes time-frequency representation of wav.

Parameters

wav (torch.Tensor) – waveform tensor in 3D shape, time last.

Returns

torch.Tensor, of shape (batch, feat, seq).

apply_masks(tf_rep, est_masks)

Applies masks to time-frequency representation.

Parameters

• tf_rep (torch.Tensor) – Time-frequency representation in (batch, feat, seq) shape.

• est_masks (torch.Tensor) – Estimated masks.

Returns

torch.Tensor – Masked time-frequency representations.

get_model_args()

Arguments needed to re-instantiate the model.

class asteroid.models.dcunet.DCUNet(architecture, stft_n_filters=1024, stft_kernel_size=1024, stft_stride=256, sample_rate=16000.0, **masknet_kwargs)

Bases: asteroid.models.dcunet.BaseDCUNet

DCUNet as proposed in [1].

Parameters

• architecture (str) – The architecture to use, any of “DCUNet-10”, “DCUNet-16”, “DCUNet-20”, “Large-DCUNet-20”.

• stft_n_filters (int) –

• stft_kernel_size (int) – STFT frame length to use.

• stft_stride (int, optional) – STFT hop length to use.

• sample_rate (float) – Sampling rate of the model.

• masknet_kwargs (optional) – Passed to DCUMaskNet

References

• [1] : “Phase-aware Speech Enhancement with Deep Complex U-Net”, Hyeong-Seok Choi et al. https://arxiv.org/abs/1903.03107

masknet_class

alias of asteroid.masknn.convolutional.DCUMaskNet

class asteroid.models.demask.DeMask(input_type='mag', output_type='mag', hidden_dims=(1024, ), dropout=0.0, activation='relu', mask_act='relu', norm_type='gLN', fb_name='stft', n_filters=512, stride=256, kernel_size=512, sample_rate=16000, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

Simple MLP model for surgical mask speech enhancement A transformed-domain masking approach is used.

Parameters

• input_type (str, optional) – whether the magnitude spectrogram “mag” or both real imaginary parts “reim” are passed as features to the masker network. Concatenation of “mag” and “reim” also can be used by using “cat”.

• output_type (str, optional) – whether the masker ouputs a mask for magnitude spectrogram “mag” or both real imaginary parts “reim”.

• hidden_dims (list, optional) – list of MLP hidden layer sizes.

• dropout (float, optional) – dropout probability.

• activation (str, optional) – type of activation used in hidden MLP layers.

• mask_act (str, optional) – Which non-linear function to generate mask.

• norm_type (str, optional) – To choose from 'BN', 'gLN', 'cLN'.

• fb_name (str) – type of analysis and synthesis filterbanks used, choose between [“stft”, “free”, “analytic_free”].

• n_filters (int) – number of filters in the analysis and synthesis filterbanks.

• stride (int) – filterbank filters stride.

• kernel_size (int) – length of filters in the filterbank.

• encoder_activation (str) –

• sample_rate (float) – Sampling rate of the model.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

forward_masker(tf_rep)

Estimates masks based on time-frequency representations.

Parameters

tf_rep (torch.Tensor) – Time-frequency representation in (batch, freq, seq).

Returns

torch.Tensor – Estimated masks in (batch, freq, seq).

apply_masks(tf_rep, est_masks)

Applies masks to time-frequency representations.

Parameters

• tf_rep (torch.Tensor) – Time-frequency representations in (batch, freq, seq).

• est_masks (torch.Tensor) – Estimated masks in (batch, freq, seq).

Returns

torch.Tensor – Masked time-frequency representations.

get_model_args()

Arguments needed to re-instantiate the model.

class asteroid.models.dprnn_tasnet.DPRNNTasNet(n_src, out_chan=None, bn_chan=128, hid_size=128, chunk_size=100, hop_size=None, n_repeats=6, norm_type='gLN', mask_act='sigmoid', bidirectional=True, rnn_type='LSTM', num_layers=1, dropout=0, in_chan=None, fb_name='free', kernel_size=16, n_filters=64, stride=8, encoder_activation=None, sample_rate=8000, use_mulcat=False, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

DPRNN separation model, as described in [1].

Parameters

• n_src (int) – Number of masks to estimate.

• out_chan (int or None) – Number of bins in the estimated masks. Defaults to in_chan.

• bn_chan (int) – Number of channels after the bottleneck. Defaults to 128.

• hid_size (int) – Number of neurons in the RNNs cell state. Defaults to 128.

• chunk_size (int) – window size of overlap and add processing. Defaults to 100.

• hop_size (int or None) – hop size (stride) of overlap and add processing. Default to chunk_size // 2 (50% overlap).

• n_repeats (int) – Number of repeats. Defaults to 6.

• norm_type (str, optional) –

  Type of normalization to use. To choose from

  • 'gLN': global Layernorm

  • 'cLN': channelwise Layernorm

• mask_act (str, optional) – Which non-linear function to generate mask.

• bidirectional (bool, optional) – True for bidirectional Inter-Chunk RNN (Intra-Chunk is always bidirectional).

• rnn_type (str, optional) – Type of RNN used. Choose between 'RNN', 'LSTM' and 'GRU'.

• num_layers (int, optional) – Number of layers in each RNN.

• dropout (float, optional) – Dropout ratio, must be in [0,1].

• in_chan (int, optional) – Number of input channels, should be equal to n_filters.

• fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].

• n_filters (int) – Number of filters / Input dimension of the masker net.

• kernel_size (int) – Length of the filters.

• stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.

• sample_rate (float) – Sampling rate of the model.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

References

• [1] “Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation”, Yi Luo, Zhuo Chen and Takuya Yoshioka. https://arxiv.org/abs/1910.06379

class asteroid.models.dptnet.DPTNet(n_src, n_heads=4, ff_hid=256, chunk_size=100, hop_size=None, n_repeats=6, norm_type='gLN', ff_activation='relu', encoder_activation='relu', mask_act='relu', bidirectional=True, dropout=0, in_chan=None, fb_name='free', kernel_size=16, n_filters=64, stride=8, sample_rate=8000, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

DPTNet separation model, as described in [1].

Parameters

• n_src (int) – Number of masks to estimate.

• out_chan (int or None) – Number of bins in the estimated masks. Defaults to in_chan.

• bn_chan (int) – Number of channels after the bottleneck. Defaults to 128.

• hid_size (int) – Number of neurons in the RNNs cell state. Defaults to 128.

• chunk_size (int) – window size of overlap and add processing. Defaults to 100.

• hop_size (int or None) – hop size (stride) of overlap and add processing. Default to chunk_size // 2 (50% overlap).

• n_repeats (int) – Number of repeats. Defaults to 6.

• norm_type (str, optional) –

  Type of normalization to use. To choose from

  • 'gLN': global Layernorm

  • 'cLN': channelwise Layernorm

• mask_act (str, optional) – Which non-linear function to generate mask.

• bidirectional (bool, optional) – True for bidirectional Inter-Chunk RNN (Intra-Chunk is always bidirectional).

• rnn_type (str, optional) – Type of RNN used. Choose between 'RNN', 'LSTM' and 'GRU'.

• num_layers (int, optional) – Number of layers in each RNN.

• dropout (float, optional) – Dropout ratio, must be in [0,1].

• in_chan (int, optional) – Number of input channels, should be equal to n_filters.

• fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].

• n_filters (int) – Number of filters / Input dimension of the masker net.

• kernel_size (int) – Length of the filters.

• stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.

• sample_rate (float) – Sampling rate of the model.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

References

• [1]: Jingjing Chen et al. “Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation” Interspeech 2020.

class asteroid.models.lstm_tasnet.LSTMTasNet(n_src, out_chan=None, rnn_type='lstm', n_layers=4, hid_size=512, dropout=0.3, mask_act='sigmoid', bidirectional=True, in_chan=None, fb_name='free', n_filters=64, kernel_size=16, stride=8, encoder_activation=None, sample_rate=8000, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

TasNet separation model, as described in [1].

Parameters

• n_src (int) – Number of masks to estimate.

• out_chan (int or None) – Number of bins in the estimated masks. Defaults to in_chan.

• hid_size (int) – Number of neurons in the RNNs cell state. Defaults to 128.

• mask_act (str, optional) – Which non-linear function to generate mask.

• bidirectional (bool, optional) – True for bidirectional Inter-Chunk RNN (Intra-Chunk is always bidirectional).

• rnn_type (str, optional) – Type of RNN used. Choose between 'RNN', 'LSTM' and 'GRU'.

• n_layers (int, optional) – Number of layers in each RNN.

• dropout (float, optional) – Dropout ratio, must be in [0,1].

• in_chan (int, optional) – Number of input channels, should be equal to n_filters.

• fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].

• n_filters (int) – Number of filters / Input dimension of the masker net.

• kernel_size (int) – Length of the filters.

• stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.

• sample_rate (float) – Sampling rate of the model.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

References

• [1]: Yi Luo et al. “Real-time Single-channel Dereverberation and Separation with Time-domain Audio Separation Network”, Interspeech 2018

class asteroid.models.sudormrf.SuDORMRFNet(n_src, bn_chan=128, num_blocks=16, upsampling_depth=4, mask_act='softmax', in_chan=None, fb_name='free', kernel_size=21, n_filters=512, stride=None, sample_rate=8000, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

SuDORMRF separation model, as described in [1].

Parameters

• n_src (int) – Number of sources in the input mixtures.

• bn_chan (int, optional) – Number of bins in the bottleneck layer and the UNet blocks.

• num_blocks (int) – Number of of UBlocks.

• upsampling_depth (int) – Depth of upsampling.

• mask_act (str) – Name of output activation.

• in_chan (int, optional) – Number of input channels, should be equal to n_filters.

• fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].

• n_filters (int) – Number of filters / Input dimension of the masker net.

• kernel_size (int) – Length of the filters.

• stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.

• sample_rate (float) – Sampling rate of the model.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

References

• [1] : “Sudo rm -rf: Efficient Networks for Universal Audio Source Separation”, Tzinis et al. MLSP 2020.

class asteroid.models.sudormrf.SuDORMRFImprovedNet(n_src, bn_chan=128, num_blocks=16, upsampling_depth=4, mask_act='relu', in_chan=None, fb_name='free', kernel_size=21, n_filters=512, stride=None, sample_rate=8000, **fb_kwargs)

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

Improved SuDORMRF separation model, as described in [1].

Parameters

• n_src (int) – Number of sources in the input mixtures.

• bn_chan (int, optional) – Number of bins in the bottleneck layer and the UNet blocks.

• num_blocks (int) – Number of of UBlocks.

• upsampling_depth (int) – Depth of upsampling.

• mask_act (str) – Name of output activation.

• in_chan (int, optional) – Number of input channels, should be equal to n_filters.

• fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].

• n_filters (int) – Number of filters / Input dimension of the masker net.

• kernel_size (int) – Length of the filters.

• stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.

• **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

References

• [1] : “Sudo rm -rf: Efficient Networks for Universal Audio Source Separation”, Tzinis et al. MLSP 2020.

Publishing models

class asteroid.models.zenodo.Zenodo(api_key=None, use_sandbox=True)

Bases: object

Faciliate Zenodo’s REST API.

Parameters

• api_key (str) – Access token generated to upload depositions.

• use_sandbox (bool) – Whether to use the sandbox (default: True) Note that api_key are different in sandbox.

All methods return the requests response.

Note

A Zenodo record is something that is public and cannot be deleted. A Zenodo deposit has not yet been published, is private and can be deleted.

create_new_deposition(metadata=None)

Creates a new deposition.

Parameters

metadata (dict, optional) – Metadata dict to upload on the new deposition.

change_metadata_in_deposition(dep_id, metadata)

Set or replace metadata in given deposition

Parameters

• dep_id (int) – deposition id. You cna get it with r = create_new_deposition(); dep_id = r.json()[‘id’]

• metadata (dict) – Metadata dict.

Examples

>>> metadata = {
...     'title': 'My first upload',
...     'upload_type': 'poster',
...     'description': 'This is my first upload',
...     'creators': [{'name': 'Doe, John',
...                   'affiliation': 'Zenodo'}]
... }

upload_new_file_to_deposition(dep_id, file, name=None)

Upload one file to existing deposition.

Parameters

• dep_id (int) – deposition id. You cna get it with r = create_new_deposition(); dep_id = r.json()[‘id’]

• file (str or io.BufferedReader) – path to a file, or already opened file (path prefered).

• name (str, optional) – name given to the uploaded file. Defaults to the path.

(More: https://developers.zenodo.org/#deposition-files)

publish_deposition(dep_id)

Publish given deposition (Cannot be deleted)!

Parameters

dep_id (int) – deposition id. You cna get it with r = create_new_deposition(); dep_id = r.json()[‘id’]

get_deposition(dep_id=-1)

Get deposition by deposition id. Get all dep_id is -1 (default).

remove_deposition(dep_id)

Remove deposition with deposition id dep_id

remove_all_depositions()

Removes all unpublished deposition (not records).

asteroid.models.publisher.save_publishable(publish_dir, model_dict, metrics=None, train_conf=None, recipe=None)

Save models to prepare for publication / model sharing.

Parameters

• publish_dir (str) – Path to the publishing directory. Usually under exp/exp_name/publish_dir

• model_dict (dict) – dict at least with keys model_args, state_dict,`dataset` or licenses

• metrics (dict) – dict with evaluation metrics.

• train_conf (dict) – Training configuration dict (from conf.yml).

• recipe (str) – Name of the recipe.

Returns

dict, same as model_dict with added fields.

Raises

AssertionError when either model_args, state_dict,`dataset` or – licenses are not present is model_dict.keys()

asteroid.models.publisher.upload_publishable(publish_dir, uploader=None, affiliation=None, git_username=None, token=None, force_publish=False, use_sandbox=False, unit_test=False)

Entry point to upload publishable model.

Parameters

• publish_dir (str) – Path to the publishing directory. Usually under exp/exp_name/publish_dir

• uploader (str) – Full name of the uploader (Ex: Manuel Pariente)

• affiliation (str, optional) – Affiliation (no accent).

• git_username (str, optional) – GitHub username.

• token (str) – Access token generated to upload depositions.

• force_publish (bool) – Whether to directly publish without asking confirmation before. Defaults to False.

• use_sandbox (bool) – Whether to use Zenodo’s sandbox instead of the official Zenodo.

• unit_test (bool) – If True, we do not ask user input and do not publish.

asteroid.models.publisher.get_username()

Get git of FS username for upload.

asteroid.models.publisher.make_license_notice(model_name, licenses, uploader=None)

Make license notice based on license dicts.

Parameters

• model_name (str) – Name of the model.

• licenses (List[dict]) –

  List of dict with keys (title, title_link, author, author_link,

  licence, licence_link).

• uploader (str) – Name of the uploader such as “Manuel Pariente”.

Returns

str, the license note describing the model, it’s attribution,

the original licenses, what we license it under and the licensor.

asteroid.models.publisher.zenodo_upload(model, token, model_path=None, use_sandbox=False)

Create deposit and upload metadata + model

Parameters

• model (dict) –

• token (str) – Access token.

• model_path (str) – Saved model path.

• use_sandbox (bool) – Whether to use Zenodo’s sandbox instead of the official Zenodo.

Returns

Zenodo (Zenodo instance with access token) int (deposit ID)

asteroid.models.publisher.make_metadata_from_model(model)

Create Zenodo deposit metadata for a given publishable model.

Parameters

model (dict) – Dictionary with all infos needed to publish. More info to come.

Returns

dict, the metadata to create the Zenodo deposit with.

Note

We remove the PESQ from the final results as a license is needed to use it.

asteroid.models.publisher.two_level_dict_html(dic)

Two-level dict to HTML.

Parameters

dic (dict) – two-level dict

Returns

str for HTML-encoded two level dic

asteroid.models.publisher.display_one_level_dict(dic)

Single level dict to HTML

Parameters

dic (dict) –

Returns

str for HTML-encoded single level dic