o ziM@sddlZddlZddlZddlZddlZddlZddlZddlmZddl m Z ddl m Z ddlm Z ddlmZddlZddlZddlZddlmZddlmZe Gd d d Zd efd d Z  d[dejejejeefdedefddZddZdejdedefddZ dejeejj!ffddZ"d\ddZ#ede$fd d!Z%gd"Z&e&fd#ed$eefd%d&Z'd'd(e&fd)eed*e(d+ed$eefd,d-Z) d]d.ejj!d/e$d0efd1d2Z*ed3eje effd4d5Z+d^d7eje,e$ejfd8efd9d:Z-d]d;e.d.ejj!fde$d?efd@dAZ0dBZ1dCZ2    ' ' Dd_dEe.dFedGe(dHe(dIef dJdKZ3 L M N O P Q Rd`dSededTedUedVedWedXe fdYdZZ4dS)aN)contextmanager) dataclass)Path)Dict)Listflatten) unflattenc@s4eZdZUdZeed<eed<edefddZdS)Infoz%Shim for torchaudio.info API changes. sample_rate num_framesreturncCs |j|jSN)r r )selfrH/home/ubuntu/.local/lib/python3.10/site-packages/audiotools/core/util.pydurations z Info.durationN) __name__ __module__ __qualname____doc__float__annotations__intpropertyrrrrrr s r audio_pathcCshz tt|}Wn tjjt|}Yt|tr*|d}t|j|j d}|St|j |j d}|S)zShim for torchaudio.info to make 0.7.2 API match 0.8.0. Parameters ---------- audio_path : str Path to audio file. r)r r ) torchaudioinfostrbackendsoundfile_backend isinstancetupler ratelengthr r )rr signal_inforrrr!s  rxndim batch_sizecCs~t|s t|}|dur$|j|ksJ|j|kr$|d}|j|ks|dur=|jd|kr=t|j}||d<|j|}|S)aEnsures that the input ``x`` is a tensor of specified dimensions and batch size. Parameters ---------- x : typing.Union[np.ndarray, torch.Tensor, float, int] Data that will become a tensor on its way out. ndim : int, optional How many dimensions should be in the output, by default None batch_size : int, optional The batch size of the output, by default None Returns ------- torch.Tensor Modified version of ``x`` as a tensor. Nr)torch is_tensor as_tensorr' unsqueezeshapelistexpand)r&r'r(r.rrr ensure_tensor8s       r1cCs ddlm}t||r|jS|S)N AudioSignal)r4r! audio_data)otherr4rrr _get_value\s  r8hzn_fftr cCsx|j}|}td|dd|d}|d|||dk<|dddf|dddf}|jddj}|j|S)a[Closest frequency bin given a frequency, number of bins, and a sampling rate. Parameters ---------- hz : torch.Tensor Tensor of frequencies in Hz. n_fft : int Number of FFT bins. sample_rate : int Sample rate of audio. Returns ------- torch.Tensor Closest bins to the data. rN)dim)r.rr*linspaceabsminindicesreshape)r9r:r r.freqsclosest closest_binsrrr hz_to_binds$ rEseedcCsZ|dus |tjurtjjjSt|tjtjtfrtj |St|tjj r'|St d|)a; Turn seed into a np.random.RandomState instance. Parameters ---------- seed : typing.Union[int, np.random.RandomState] or None If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError. Returns ------- np.random.RandomState Random state object. Raises ------ ValueError If seed is not valid, an error is thrown. Nz=%r cannot be used to seed a numpy.random.RandomState instance) nprandommtrand_randr!numbersIntegralintegerr RandomState ValueError)rFrrr random_states  rPFcCs@t|tj|t||rdtjj_dtjj_dSdS)a Seeds all random states with the same random seed for reproducibility. Seeds ``numpy``, ``random`` and ``torch`` random generators. For full reproducibility, two further options must be set according to the torch documentation: https://pytorch.org/docs/stable/notes/randomness.html To do this, ``set_cudnn`` must be True. It defaults to False, since setting it to True results in a performance hit. Args: random_seed (int): integer corresponding to random seed to use. set_cudnn (bool): Whether or not to set cudnn into determinstic mode and off of benchmark mode. Defaults to False. TFN) r* manual_seedrGrHrFbackendscudnn deterministic benchmark) random_seed set_cudnnrrrrFs    tmpfilesc#s2fdd}zdVWn||dS)aUtility function for creating a context and closing all temporary files once the context is exited. For correct functionality, all temporary file handles created inside the context must be appended to the ```tmpfiles``` list. This function is taken wholesale from Scaper. Parameters ---------- tmpfiles : list List of temporary file handles cs0D]}z |t|jWqYqdSr)closeosunlinkname)trXrr_closesz!_close_temp_files.._closeNr)rXr_rr^r_close_temp_filess   r`).wavz.flacz.mp3z.mp4folderextcCslt|}t|t|r#dt|vr tjt|dt|vdS|gSg}|D] }||d|7}q'|S)a8Finds all audio files in a directory recursively. Returns a list. Parameters ---------- folder : str Folder to look for audio files in, recursively. ext : List[str], optional Extensions to look for without the ., by default ``['.wav', '.flac', '.mp3', '.mp4']``. *z**) recursivez**/*)rrendswithr"glob)rbrcfilesr&rrr find_audios  riTr5sources remove_empty relative_pathc Csg}t|}|D]l}t|}g}|drSt|d0}t|}|D] } |r-| ddkr-q"| ddkr=t|| d| d<|| q"Wdn1sMwYnt||dD]} t|| } |d| iqY|t|ddd q|S) aeReads audio sources that can either be folders full of audio files, or CSV files that contain paths to audio files. CSV files that adhere to the expected format can be generated by :py:func:`audiotools.data.preprocess.create_csv`. Parameters ---------- sources : List[str] List of audio sources to be converted into a list of lists of audio files. remove_empty : bool, optional Whether or not to remove rows with an empty "path" from each CSV file, by default True. Returns ------- list List of lists of rows of CSV files. .csvrpathr5N)rccSs|dS)Nror)r&rrr*szread_sources..)key) rrrfopencsv DictReaderappendrisorted) rjrkrlrcrhsource_filesfreaderr&rrr read_sourcess,      r{state list_of_listspcCs>|jttt||d}|t||}|||||fS)aChoose a single item from a list of lists. Parameters ---------- state : np.random.RandomState Random state to use when choosing an item. list_of_lists : list A list of lists from which items will be drawn. p : float, optional Probabilities of each list, by default None Returns ------- typing.Any An item from the list of lists. )r~)choicer/rangelenrandint)r|r}r~ source_idxitem_idxrrrchoose_from_list_of_lists.srnewdirc cs8t}zt|dVWt|dSt|w)z Context manager for switching directories to run a function. Useful for when you want to use relative paths to different runs. Parameters ---------- newdir : typing.Union[Path, str] Directory to switch to. N)rZgetcwdchdir)rcurdirrrrrFs   rcpubatchdevicecCst|tr&t|}|D]\}}z ||||<Wq Yq t|}|St|r2||}|St|trPt t |D]}z |||||<Wq=Yq=|S)a Moves items in a batch (typically generated by a DataLoader as a list or a dict) to the specified device. This works even if dictionaries are nested. Parameters ---------- batch : typing.Union[dict, list, torch.Tensor] Batch, typically generated by a dataloader, that will be moved to the device. device : str, optional Device to move batch to, by default "cpu" Returns ------- typing.Union[dict, list, torch.Tensor] Batch with all values moved to the specified device. ) r!dictritemstor r*r+r/rr)rrrqvalirrr prepare_batchZs&   r dist_tuplecCs:|ddkr |dSt|}t||d}||ddS)aSamples from a distribution defined by a tuple. The first item in the tuple is the distribution type, and the rest of the items are arguments to that distribution. The distribution function is gotten from the ``np.random.RandomState`` object. Parameters ---------- dist_tuple : tuple Distribution tuple state : np.random.RandomState, optional Random state, or seed to use, by default None Returns ------- typing.Union[float, int, str] Draw from the distribution. Examples -------- Sample from a uniform distribution: >>> dist_tuple = ("uniform", 0, 1) >>> sample_from_dist(dist_tuple) Sample from a constant distribution: >>> dist_tuple = ("const", 0) >>> sample_from_dist(dist_tuple) Sample from a normal distribution: >>> dist_tuple = ("normal", 0, 0.5) >>> sample_from_dist(dist_tuple) rconstr2N)rPgetattr)rr|dist_fnrrrsample_from_dists $r list_of_dictsn_splitsc sddlmg}t|}|durdnd}|durdn|}tt||}td||D]Q}dd||||Dfd d dD}i}|D])\} } t| t rtt fd d | Drij | dd || <qKt j jjj| || <qK|t|q+|s|d}|S|}|S)a Collates a list of dictionaries (e.g. as returned by a dataloader) into a dictionary with batched values. This routine uses the default torch collate function for everything except AudioSignal objects, which are handled by the :py:func:`audiotools.core.audio_signal.AudioSignal.batch` function. This function takes n_splits to enable splitting a batch into multiple sub-batches for the purposes of gradient accumulation, etc. Parameters ---------- list_of_dicts : list List of dictionaries to be collated. n_splits : int Number of splits to make when creating the batches (split into sub-batches). Useful for things like gradient accumulation. Returns ------- dict Dictionary containing batched data. r2r3NFTrcSsg|]}t|qSrr).0drrr zcollate..cs i|] fddDqS)csg|]}|qSrr)rdickrrrrz&collate...r)r)list_of_dicts_rr szcollate..c3s|]}t|VqdSr)r!)rsr3rr szcollate..) pad_signals)r5r4rrmathceilrrr!r/allrr*utilsdata_utilscollatedefault_collaterur ) rrbatcheslist_len return_listn_itemsr dict_of_listsrrvr)r4rrrs.    ri`) whitefig_sizetitle format_axesformat font_colorcCsddl}ddlm}|durt}|sdS|dur|}|j||j}||jd} | t } |r|j}|D]z} | \} } | \}} | }|ddD]}|dj |ddd||fdd d d d |d | dd }qP| dd}|ddD]}|dj |dd|| fddd dd|d | dd }q|| dd| | j|| j|q8|jddddddd|dur|dj |ddd| dd dd dd }|tdddddSdS) a*Prettifies the spectrogram and waveform plots. A title can be inset into the top right corner, and the axes can be inset into the figure, allowing the data to take up the entire image. Used in - :py:func:`audiotools.core.display.DisplayMixin.specshow` - :py:func:`audiotools.core.display.DisplayMixin.waveplot` - :py:func:`audiotools.core.display.DisplayMixin.wavespec` Parameters ---------- fig_size : tuple, optional Size of figure, by default (9, 3) title : str, optional Title to inset in top right, by default None fig : matplotlib.figure.Figure, optional Figure object, if None ``plt.gcf()`` will be used, by default None format_axes : bool, optional Format the axes to be inside the figure, by default True format : bool, optional This formatting can be skipped entirely by passing ``format=False`` to any of the plotting functions that use this formater, by default True font_color : str, optional Color of font of axes, by default "white" rNr;r)iz2.1frr)z offset pointslefttop g?) xyxycoordsxytext textcoordshavacolorfontsizealphar)rrcenterbottomr2)rrrightrhspacewspace)r2r2z axes fraction)rrrr)rrrrrrrrblackg?) facecolorr edgecolor) matplotlibmatplotlib.pyplotpyplotDEFAULT_FIG_SIZEgcfset_size_inchesaxesget_size_inchesdpi BASE_SIZEget_ylimget_xlim get_yticksannotate get_xticksmargins set_axis_offxaxisset_major_locator NullLocatoryaxissubplots_adjustset_bboxr)rrfigrrrrpltaxspixels font_scaleaxymin_xminticksr]rrr format_figures!        rDr?C2C6chords max_voices num_itemsrmin_notemax_note output_dircCsddl}ddlm}ddlm} ||} ||} g} t|D]7} i}td|}t|D]#}t| | }t d||}|j | |||dd }||d |<q.| |q t |}|jd d t| D]$\} }|d | }|jd d |D]\}}|||dq{qfttdd| D}dd|D}| D]}|D]}||vr|| ||jq|| dqq|D]\}}| |||dd dq|S)a Generates a toy multitrack dataset of chords, synthesized from sine waves. Parameters ---------- max_voices : int, optional Maximum number of voices in a chord, by default 8 sample_rate : int, optional Sample rate of audio, by default 44100 num_items : int, optional Number of items to generate, by default 5 duration : float, optional Duration of each item, by default 1.0 min_note : str, optional Minimum note in the dataset, by default "C2" max_note : str, optional Maximum note in the dataset, by default "C6" output_dir : Path, optional Directory to save the dataset, by default "chords" rNr2r3r;) create_csvg333333?sine) frequencyrr r.voice_T)exist_oktrack_racSsg|] }|D]}|qqSr)keys)rtrackrrrrrsz*generate_chord_dataset..cSsi|]}|gqSrr)rvoicerrrrsz*generate_chord_dataset..r5rm)loudness)librosar5r4data.preprocessr note_to_midirrHruniformwave midi_to_hzrurmkdir enumeraterwriter/set path_to_file)rr rrrrrr r4rmin_midimax_miditracksidxr num_voices voice_idxmidinotedursig track_dir voice_name all_voices voice_listspathsrrrgenerate_chord_datasetQsN           r#)NN)Fr)r)NNNTTr)rrrrrrr)5rsrgrrKrZrHtyping contextlibr dataclassesrpathlibrrrnumpyrGr*r flatten_dictrr r rrUnionndarrayTensorrrr1r8rErNrPrFr/r`AUDIO_EXTENSIONSriboolr{rrrrr"rrrrrr#rrrrs         $ " 1 "%+8 l