o 8wi͍@sdZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl m Z ddlZddlZddlZddZ dRddZdSd d Zd d ZddZdTddZ    dUddZddZdVddZdVdefddZddZdd Zed!Z d"d#Z!d$d%Z"d&d'Z#d(d)Z$dWd,d-Z%d.d/Z&d0d1Z'd2d3Z(d4d5Z) dXd6d7Z*d8d9Z+ dYd:d;Z,dZdd?Z.d@dAZ/dBdCZ0d[dDdEZ1d[dFdGZ2d[dHdIZ3d[dJdKZ4d[dLdMZ5dNdOZ6dPdQZ7dS)\zThis library gathers utilities for data io operation. Authors * Mirco Ravanelli 2020 * Aku Rouhe 2020 * Samuele Cornell 2020 * Adel Moumen 2024 * Pierre Champion 2023 N)NumbercCsT|jd}g}t||D]\}}tt||}|dd|}||q |S)a6Produces Python lists given a batch of sentences with their corresponding relative lengths. Arguments --------- batch : torch.Tensor Batch of sentences gathered in a batch. lengths : torch.Tensor Relative length of each sentence in the batch. Returns ------- as_list : list A python list of the corresponding input tensor. Example ------- >>> batch=torch.rand([4,100]) >>> lengths=torch.tensor([0.5,0.6,0.7,1.0]) >>> snt_list=undo_padding(batch, lengths) >>> len(snt_list) 4 r)shapezipinttorchroundnarrowappendtolist)batchlengths batch_max_lenas_listseq seq_length actual_sizeseq_truerY/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/speechbrain/utils/data_utils.py undo_paddings rc Cs6d}d}d}d}t|} t} | D]} tj|| } tj| r-| t| ||||d} q|durJd}d} |D] }|| vrA| d} q7| t|krJd}|dur]d}|D] }|| vr\d}nqR|durxd} |D] }|| vro| d} qe| t|krxd}|durd}|D] }|| vrd}nq|r|r|s|s| | q| S)aReturns a list of files found within a folder. Different options can be used to restrict the search to some specific patterns. Arguments --------- dirName : str The directory to search. match_and : list A list that contains patterns to match. The file is returned if it matches all the entries in `match_and`. match_or : list A list that contains patterns to match. The file is returned if it matches one or more of the entries in `match_or`. exclude_and : list A list that contains patterns to match. The file is returned if it matches none of the entries in `exclude_and`. exclude_or : list A list that contains pattern to match. The file is returned if it fails to match one of the entries in `exclude_or`. Returns ------- allFiles : list The list of files matching the patterns. Example ------- >>> get_all_files('tests/samples/RIRs', match_and=['3.wav']) ['tests/samples/RIRs/rir3.wav'] TF) match_andmatch_or exclude_and exclude_orNrr) oslistdirlistpathjoinisdir get_all_fileslenr )dirNamerrrrmatch_and_entrymatch_or_entryexclude_or_entryexclude_and_entry listOfFileallFilesentryfullPath match_foundelerrrr!=st$       r!,TcCs`g}t|ddd}tj|||d}|D] }|||qWd|S1s)wY|S)aGets a list from the selected field of the input csv file. Arguments --------- csvfile: path Path to the csv file. field: str Field of the csv file used to create the list. delimiter: str Delimiter of the csv file. skipinitialspace: bool Set it to true to skip initial spaces in the entries. Returns ------- The list of files in the given field of a csv zutf-8)newlineencoding) delimiterskipinitialspaceN)opencsv DictReaderr )csvfilefieldr2r3lstcsvfreaderrowrrrget_list_from_csvs r=cCsZt|t|}g}d}|t|kr+||t|t||||7}|t|ks|S)aReturns a list of splits in the sequence. Arguments --------- seq : iterable The input list, to be split. num : int The number of chunks to produce. Returns ------- A list of lists, length num and containing all elements of seq. Example ------- >>> split_list([1, 2, 3, 4, 5, 6, 7, 8, 9], 4) [[1, 2], [3, 4], [5, 6], [7, 8, 9]] g)r"floatr r)rnumavgoutlastrrr split_lists  rCccs>|D]\}}t|turt|EdHq||fVqdS)a}Yield each (key, value) of a nested dictionary. Arguments --------- dictionary : dict The nested dictionary to list. Yields ------ `(key, value)` tuples from the dictionary. Example ------- >>> rec_dict={'lev1': {'lev2': {'lev3': 'current_val'}}} >>> [item for item in recursive_items(rec_dict)] [('lev3', 'current_val')] N)itemstypedictrecursive_items) dictionarykeyvaluerrrrGs   rGFcCsv|D]4\}}t|tjjr||vrt||i|q|r4||vr4td|ddd|D|||<qdS)aSimilar function to `dict.update`, but for a nested `dict`. From: https://stackoverflow.com/a/3233356 If you have to a nested mapping structure, for example: {"a": 1, "b": {"c": 2}} Say you want to update the above structure with: {"b": {"d": 3}} This function will produce: {"a": 1, "b": {"c": 2, "d": 3}} Instead of: {"a": 1, "b": {"d": 3}} Arguments --------- d : dict Mapping to be updated. u : dict Mapping to update with. must_match : bool Whether to throw an error if the key in `u` does not exist in `d`. Example ------- >>> d = {'a': 1, 'b': {'c': 2}} >>> recursive_update(d, {'b': {'d': 3}}) >>> d {'a': 1, 'b': {'c': 2, 'd': 3}} z Override 'z' not found in: cSsg|]}|qSrr).0rIrrr -sz$recursive_update..N) rD isinstance collectionsabcMappingrecursive_updategetKeyErrorkeys)du must_matchkvrrrrQs'  rQc CsztjjtjjrGdddtj}t|j }|j dddd|vr0t ||nGt j|r>t j|rp|rptd|d||ddd |d d d }tjj|||jd Wdn1sjwYnt|d|r|durt j|}td|d||dr|dst|d'} t|ddd} t | | Wdn1swYWdn1swYnt |||rt|WtjjdSWtjjdSWtjjdSWtjjdStjjw)a0Downloads the file from the given source and saves it in the given destination path. Arguments --------- source : path or url Path of the source file. If the source is an URL, it downloads it from the web. dest : path Destination path. unpack : bool If True, it unpacks the data in the dest folder. The archive is preserved. File formats supported for unpacking/decompression are: - any format enumerated by `shutil.get_archive_formats()`, usually including `.tar`, `.tar.gz`, `.zip`. - plain `.gz` file (when not a `.tar` archive) Note that you should ALWAYS trust an archive you are extracting, for security reasons. dest_unpack: path Path where to store the unpacked dataset replace_existing : bool If True, replaces the existing files. write_permissions: bool When set to True, all the files in the dest_unpack directory will be granted write permissions. This option is active only when unpack=True. c@seZdZdZdddZdS)z*download_file..DownloadProgressBarzDownloadProgressBar class.rNcSs&|dur||_||||jdS)z$Needed to support multigpu training.N)totalupdaten)selfbbsizetsizerrr update_toasz4download_file..DownloadProgressBar.update_to)rrN)__name__ __module__ __qualname____doc__rarrrrDownloadProgressBar^srfT)parentsexist_okhttpz Downloading z to Br/)unit unit_scaleminitersdesc)filename reporthookNz exists. Skipping downloadz Extracting z.gzz.tar.gzrbwb)sbutils distributed ddp_barrierif_main_processtqdmpathlibPathresolveparentmkdirshutilcopyfilerrisfileprintspliturllibrequest urlretrieveradirnameendswithgzipr4 copyfileobjunpack_archiveset_writing_permissions) sourcedestunpack dest_unpackreplace_existingwrite_permissionsrfdest_dirtf_inf_outrrr download_file3sb&          4rcCs>t|D]\}}}|D]}tj||}t|dq qdS)z This function sets user writing permissions to all the files in the given folder. Arguments --------- folder_path : folder Folder whose files will be granted write permissions. iN)rwalkrrchmod) folder_pathrootdirsfiles file_name file_pathrrrrs  rconstantcCst||jks Jg}g}t|d}d}|dkrL|||j|ks&Jd|d|||j|g||j||||d8}|d7}|dkstjjj||||d}||fS)a This function takes a torch tensor of arbitrary shape and pads it to target shape by appending values on the right. Arguments --------- tensor : torch.Tensor Input tensor whose dimension we need to pad. target_shape : (list, tuple) Target shape we want for the target tensor its len must be equal to tensor.ndim mode : str Pad mode, please refer to torch.nn.functional.pad documentation. value : float Pad value, please refer to torch.nn.functional.pad documentation. Returns ------- tensor : torch.Tensor Padded tensor. valid_vals : list List containing proportion for each dimension of original, non-padded values. rrz4Target shape must be >= original shape for every dimmoderJ) r"ndimrextendr rnn functionalpad)tensor target_shaperrJpads valid_valsijrrr pad_right_tos   rtensorsc ststdtdkrddtdgfStfddtdtDs/tdg}tdjD](dkrRtfdddd DsRtd | t fd dDq8g}g}D]}t ||||d \}}| || |dqgt |}|t|fS) aGGiven a list of torch tensors it batches them together by padding to the right on each dimension in order to get same length for all. Arguments --------- tensors : list List of tensor we wish to pad together. mode : str Padding mode see torch.nn.functional.pad documentation. value : float Padding value see torch.nn.functional.pad documentation. Returns ------- tensor : torch.Tensor Padded tensor. valid_vals : list List containing proportion for each dimension of original, non-padded values. zTensors list must not be emptyrrg?cs g|] }|jdjkqSr)r)rKr)rrrrLs z#batch_pad_right..z/All tensors must have same number of dimensionscs$g|]}|jdjkqSrrrKxdimrrrrLs$Nzsz recursive_to.._fieldsc3s&|]}t|gRiVqdSNrrKrrrr s$zrecursive_to..cs"g|] }t|gRiqSrrrrrrrL#s"z recursive_to..to) rMrTensorrrNrOrPrDtuplehasattrrESequence)datarrrrrrs   rz[SaUO]cCsH|d}t|}t|tjrBd}z&tjjdur.tdd|D}| |}| |}tj |d|dWSt yA|YSw|j dkr|jdkr|jdkrz.|jd ks\|jd krrt|jjdurh|WStd d|DWS|jd kr}t|WSWdSt y|YSwt|trtj|tjd St|trt|S|S)a\Makes a tensor from list of batch values. Note that this doesn't need to zip(*) values together as PaddedBatch connects them already (by key). Here the idea is not to error out. This is modified from: https://github.com/pytorch/pytorch/blob/c0deb231db76dbea8a9d326401417f7d1ce96ed5/torch/utils/data/_utils/collate.py#L42 rNcSsg|]}|qSr)numelrrrrrLBz'mod_default_collate..)rAnumpystr_string_ndarraymemmapcSsg|]}t|qSr)r as_tensor)rKr^rrrrLUrr)dtype)rErMrrrwrget_worker_infosumstorage _new_sharednewr RuntimeErrorrcrbnp_str_obj_array_patternsearchrstrmod_default_collaterrr>rfloat64r)r elem elem_typerArrrrrr/sB               rcCsHdd}t|tjjjr |\}}||\}}tjj|||fS||S)aSplits a path to source and filename This also handles URLs and Huggingface hub paths, in addition to regular paths. Arguments --------- path : str or FetchSource Returns ------- str Source str Filename cSsd|vr |jdddSd|fS)zCore function to split path.rkr)maxsplitz./)rsplit)srcrrrrtszsplit_path..split)rMrvrwfetching FetchSource)rr fetch_from fetch_pathrrqrrr split_pathbs  rcCs*t|dr |}n|}dd|DS)aConverts a namedtuple or dictionary containing tensors to their scalar value Arguments --------- value: dict or namedtuple a dictionary or named tuple of tensors Returns ------- result: dict a result dictionary _asdictcSsi|] \}}||qSr)item)rKrI item_valuerrrrszscalarize..)rrrD)rJ value_dictrrr scalarizes  rcCs ||jd||S)ahReshape the tensor to be of a shape compatible with the target tensor, only valid if x.dim() <= y.dim() Arguments --------- x: torch.Tensor the original tensor target: torch.Tensor the tensor whose shape Returns ------- result: torch.Tensor a view of tensor x reshaped to a shape compatible with y r)viewrr)rtargetrrr unsqueeze_ass rrc Csj||}|}||}|dkr|||7}t|j}|||<t|||d\} } |dur1|||}| |fS)aAdds extra padding to the specified dimension of a tensor to make it divisible by the specified factor. This is useful when passing variable-length sequences to downsampling UNets or other similar architectures in which inputs are expected to be divisible by the downsampling factor Arguments --------- tensor: torch.Tensor the tensor to be padded, of arbitrary dimension length: torch.Tensor a 1-D tensor of relative lengths factor: int the divisibility factor len_dim: int the index of the dimension used as the length pad_value: int the value with which outputs will be padded Returns ------- tensor_padded: torch.Tensor the tensor, with additional padding if required length: torch.Tensor the adjusted length tensor, if provided Example ------- >>> x = torch.tensor([[1, 2, 3, 4], ... [5, 6, 0, 0]]) >>> lens = torch.tensor([1., .5]) >>> x_pad, lens_pad = pad_divisible(x, length=lens, factor=5) >>> x_pad tensor([[1, 2, 3, 4, 0], [5, 6, 0, 0, 0]]) >>> lens_pad tensor([0.8000, 0.4000]) r)rJN)sizerrr) rlengthfactorlen_dim pad_valuetime_dimdesired_time_dimgap new_shape tensor_padded_rrr pad_divisibles +   r cCs$t|D] \}}||d|}q|S)aTrims the specified tensor to match the specified shape Arguments --------- tensor: torch.Tensor a tensor shape: enumerable the desired shape Returns ------- tensor: torch.Tensor the trimmed tensor r) enumerater )rrrrrrr trim_to_shapesrcCs t||jS)a'Trims the specified tensor to match the shape of another tensor (at most) Arguments --------- tensor: torch.Tensor: a tensor other: torch.Tensor the tensor whose shape to match Returns ------- tensor: torch.Tensor the trimmed tensor )rr)rotherrrrtrim_ass rcCs"t||}||}t||}|S)adA swiss-army-knife helper function to match the shape of a tensor to match that of another tensor - useful for masks, etc. Arguments --------- tensor: torch.Tensor: a tensor other: torch.Tensor the tensor whose shape to match Returns ------- tensor: torch.Tensor the tensor with matching shape )r expand_asr)rrresultrrr match_shapes   rcstt|}t|}|d|||}t|d}||7}t||t}t| |f}t rC|}|Sfdd|D}|S)apShuffles batches of fixed size within a sequence Arguments --------- items: sequence a tensor or an indexable sequence, such as a list batch_size: int the batch size Returns ------- items: sequence the original items. If a tensor was passed, a tensor will be returned. Otherwise, it will return a list rlrcsg|]}|qSrr)rKidxrDrrrLCrz!batch_shuffle..) mathfloorr"rrandpermrexpandarangeconcatflatten is_tensor)rD batch_size batch_countbatches batch_idx batch_offsettailrrrr batch_shuffle's  r%cs&|d}tfdd|D|j}tdd|D}||d}t||}t||}t|||dd\}} t|||dd\} } |  } t |j } | | <t | |j}t|| | || D]\}}}}}t|||}t|||}||||<qg| dd d f| }||fS) aConcatenates multiple padded feature tensors into a single padded tensor in a vectorized manner without including the padding in the final tensor, adding padding only at the end. The function supports optional relative sicing of the tensors. One possible use case is to concatenate batches of spectrograms or audio. Arguments --------- feats: list a list of padded tensors lens: list a list of length tensors dim: int The dimension on which to perform concatenation feats_slice_start: list offsets, relative to the beginning of the sequence, for each of the tensors being concatenated. This is useful if only a subsequence of some slices is included feats_slice_end: list offsets, relative to the end of the sequence, for each of the tensors being concatenated. This is useful if only a subsequence of some slices is included Returns ------- out: torch.Tensor a concatenated tensor rcsg|]}|qSr)rrKrrrrrLirz*concat_padded_features..cSg|]}|dqSrr)rKlen_relrrrrLlrrlT) cumulativeFN)rrrdevicerrr_offset_to_tensor_lens_to_boundariesrrrrzerosr_boundaries_to_maskr>)featslensrfeats_slice_startfeats_slice_end first_item item_lengthslens_abs out_startout_endin_startin_end total_length out_shaperAr item_in_start item_in_enditem_out_start item_out_endin_maskout_maskout_lensrrrconcat_padded_featuresGs4!      rDcCs|durd}|St|r|}|St|trt||}|St|trEt|dtr9t|d|j }|St dd|D}|St d)a!Converts a variety of offset representations to a component x batch tensor, used by concat_padded_features. offset can be a tensor, a list of tensors (where each element is a tensor of relative offsets similar to lengths), a list of floats (in which case all batch elements are presumed to have the same offset) Arguments --------- offset: list|Tensor a list or tensor of offsets lengths: torch.Tensor a length tensor Returns ------- result: torch.Tensor a tensor of offsets NrrlcSr'rr(r&rrrrLrz%_offset_to_tensor..z:The offset must be a number, a tensor or a list of tensors) rrrMr ones_likerrrrr+r ValueError)offsetr rrrrr,s$   r,c Cs|d}td|f|j}|dur td|j}n||}|dur6td|j}n||}|r^|||}tj||gdd} | j ddddddf} n tj|j |j} | |7} | ||} | | fS)aConverts a tensor of lengths to a tensor of start and end boundaries, used for concat_padded_features Arguments --------- lengths: torch.Tensor a (component x batch) tensor of absolute lengths slice_start: torch.Tensor a (component x batch) tensor of relative start offsets slice_end: torch.Tensor a (component x batch) tensor of relative end offsets cumulative: True if true, the start of a given component is assumed to be at the end of the previous component. if false, all components start at the beginning of the length dimension Returns ------- start: torch.Tensor the starting boundary end: torch.Tensor the ending boundary rlrNrr) rrr.rrr+rrrcumsumr) r slice_start slice_endr*r batch_padding start_offset end_offseteffective_lengthseffective_lengths_zpadstartendrrrr-s$   r-c Cs>t||}|}t||d}t||d}||k||k@}|S)a;For a given features tensor and tensors of start and end indexes, computes the corresponding Boolean mask Arguments --------- target: torch.Tensor the target tensor start: torch.Tensor the tensor indicating the starting positions along the length dimension within each batch end: torch.Tensor the tensor indicating the final positions within each batch len_dim: int the dimension used as the length Returns ------- mask: torch.Tensor a Boolean mask of the same shape as target r) length_ranger unsqueeze_1d) rrPrQr out_range feats_dim item_startitem_endmaskrrrr/s   r/cCsdg|}d||<||S)aUnsqueezes a 1-D tensor to the specified number of dimension preserving one dimension and creating "dummy" dimensions elsewhere Arguments --------- value: torch.Tensor A 1-D tensor dim: int the number of dimension value_dim: int the dimension that the value tensor represents Returns ------- result: torch.Tensor a dim-dimensional tensor N.r)rJr value_dim unsqueeze_dimrrrrSs rScCsN||}t||j}t|||}ddt|j|jD}|j |S)aCreates a tensor with a range in a single dimension to one matching the shape of a its tensor Arguments --------- feats: torch.Tensor a features tensor of arbitrary shape len_dim: torch.Tensor the dimension used as length Returns ------- result: torch.Tensor a tensor matching the shape of feats with an 0 to max-length range along the length dimension repeated across other dimensions cSsg|]\}}||qSrr)rK feats_sizeout_sizerrrrL,sz length_range..) rrrrr+rSrrrrepeat)r0rmax_len feats_rangerA repeat_dimrrrrRs   rRcCsttd|S)zReturns all dimensions of the specified tensor except the batch dimension Arguments --------- sample: torch.Tensor an arbitrary tensor Returns ------- dims: list a list of dimensions r)rrr)rrrrnon_batch_dims3sracCs@|dur t|}t|}||j|d||j|dS)a#A metric function that computes the mean of each sample, excluding padding Arguments --------- sample: torch.Tensor a tensor of spectrograms mask: torch.Tensor a length mask Returns ------- result: torch.Tensor a tensor fo means Nr)rrEboolrarrrrXdimsrrr masked_meanDs"recCsd|dur t|}t|}tt|||}|||d}|j|d||j|ddS)a1A metric function that computes the standard deviation of each sample, excluding padding Arguments --------- sample: torch.Tensor a tensor of spectrograms mask: torch.Tensor a length mask Returns ------- result: torch.Tensor a tensor fo means Nrrr) rrErbrarrerrsqrt)rrXrdmeandiff_sqrrr masked_stdZs ricCs:|dur t|}t|}||tjj|dSaA metric function that computes the minimum of each sample Arguments --------- sample: torch.Tensor a tensor of spectrograms mask: torch.Tensor a length mask Returns ------- result: torch.Tensor a tensor fo means Nr)rrErbra masked_fillinfaminrcrrr masked_mintsrncCs<|dur t|}t|}||tj j|dSrj)rrErbrarkrlamaxrcrrr masked_maxsrpcCs&t||t||t||t||dS)aReturns standard distribution statistics (mean, std, min, max) Arguments --------- sample: torch.Tensor a tensor of spectrograms mask: torch.Tensor a length mask Returns ------- result: torch.Tensor a tensor fo means )rgstdminr)rerirnrp)rrXrrr dist_statss rscsfddtDS)aReturns all possible key-value combinations from the given dictionary Arguments --------- values: dict A dictionary with lists of values as values Example: { "digit": [1,2,3], "speaker": [10, 20] } Returns ------- result: list a list of dictionaries in which each dictionary is a possible permutations cs g|] }t|tkr|qSr)r"r&valuesrrrLs z+dict_value_combinations..)dict_value_combinations_genrTrtrrtrdict_value_combinationss  rwc cs^|sdS|^}}||}|D]}||i}t||D]}t|}|||Vq|VqdS)aReturns a generation of permutations of the specified values dictionary Arguments --------- values: dict A dictionary with lists of values as values Example: { "digit": [1,2,3], "speaker": [10, 20] } keys: list the keys to consider Returns ------- result: generator a generator of dictionaries in which each dictionary is a possible permutation N)rvrFr[) rurTrIrest key_valuesrJcurrsubrrrrrvs rv)NNNN)r.T)F)FNFF)rr)Nrrr)rNN)NNTrr)8recollections.abcrNr5rrrr|rerurllib.requestrnumbersrrr{ speechbrainrvrr!r=rCrGrQrrrrrrrcompilerrrrrr rrrr%rDr,r-r/rSrRrarerirnrprsrwrvrrrrsr  " o 5 ` *? 3" >! 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