o SiO&@sdZddlZddlZddlmZddlmZmZmZddl Z ddl m Z ddl m Z mZddlmZddlmZdd lmZmZd ZGd d d ejZGd ddZdede defddZdS)z This is an experimental workflow that can be used to simulate multi-speaker meetings from a CutSet containing MonoCut objects. N)groupby)ListOptionalUnion)tqdm) RecordingSetSupervisionSet)CutSet)DynamicCutSampler)fastcopyis_module_availableic @seZdZdZddZdefddZejdde e ddfd d Z ej  dd e d e e d e e de fddZejd e dede fddZdS)BaseMeetingSimulatora> Base class for meeting simulators. A simulator consists of a `fit()`, a `simulate()`, and a `reverberate()` method. The `fit()` method is used to learn the distribution of the meeting parameters (e.g. turn-taking, overlap ratio, etc.) from a given dataset, presented in the form of a SupervisionSet. The parameters themselves are simulator specific. The `simulate()` method takes a CutSet containing MonoCut objects and simulates the desired number of multi-speaker meetings, based on the learned distribution. The output is a CutSet containing MixedCut objects, where each track represents a different speaker. The `reverberate()` method takes a CutSet containing MixedCut objects (usually the output of `simulate()`) and applies a reverberation effect to each track. We can apply single or multi-channel room impulse responses (RIRs) to each track. The output is a CutSet containing MixedCut objects, where each track represents a different speaker, convolved with a different RIR. The base class should be inherited from and the different methods should be implemented. The output is expected to be a CutSet containing MixedCut objects, where each track represents a different speaker, possibly convolved with a different RIR. This is analogous to the "mixture model" of speech signals. Example usage: >>> simulator = MyMeetingSimulator() >>> simulator.fit(cuts) >>> simulated_cuts = simulator.simulate(mono_cuts, num_meetings=10) >>> simulated_cuts = simulator.reverberate(simulated_cuts, rirs) cCs(t|tur tdtdstddS)NzNBaseMeetingSimulator is an abstract base class and should not be instantiated.scipyz!Please 'pip install scipy' first.)typer TypeErrorr ImportErrorselfr\/home/ubuntu/.local/lib/python3.10/site-packages/lhotse/workflows/meeting_simulation/base.py__init__5s zBaseMeetingSimulator.__init__returncCst|jdS)Nz())r__name__rrrr__repr__>szBaseMeetingSimulator.__repr__NmeetingscCdS)zX Learn the distribution of the meeting parameters from a given dataset. Nr)rrrrrfitAzBaseMeetingSimulator.fitcuts num_meetings num_repeatscCr)zH Simulate the desired number of multi-speaker meetings. Nr)rrrr rrrsimulateHs zBaseMeetingSimulator.simulaterirscGr)z= Apply a reverberation effect to each track. Nr)rrr"rrr reverberateTrz BaseMeetingSimulator.reverberateN)NN)r __module__ __qualname____doc__rstrrabcabstractmethodrrrr intr!rr#rrrrr s(   r c@s~eZdZdZ       ddedeed eed eeeefd eee d ee d eedefddZ ddZ ddZ dS)MeetingSamplera> Create a sampler that will be used to sample groups of utterances from the sources. The cuts are partitioned into speaker-wise buckets, and a SimpleCutSampler is created for each bucket. When we sample a group of utterances, we first sample the number of speakers in the meeting, and then sample the utterances of each speaker. This is done by sampling a batch from the corresponding SimpleCutSampler. :param cuts: a CutSet containing MonoCut objects. :param num_repeats: the number of times each cut will be repeated (by default, they are repeated infinitely). :param num_meetings: the number of meetings to simulate. :param num_speakers_per_meeting: the number of speakers per meeting. :param speaker_count_probs: the probabilities of the number of speakers per meeting. :param max_duration_per_speaker: the maximum duration of a speaker in a meeting. :param max_utterances_per_speaker: the maximum number of utterances of a speaker in a meeting. :param seed: the random seed. :return: a DynamicCutSampler object. N4@rrr rnum_speakers_per_meetingspeaker_count_probsmax_duration_per_speakermax_utterances_per_speakerseedc Cstdd|DsJd|tdd|Ds Jd|t|dks-Jd|t|t|ks9Jdi|_ttt|d d d d d d dD]\} } tt t | j |dd||d|d} | |j| <qM||_ ||_ tj||_t||_||_dS)Ncs|]}|dkVqdS)Nr).0nrrr |z*MeetingSampler.__init__..z@The number of speakers per meeting must be greater than 1. Got: csr5)gNr)r7prrrr9r:zUThe probabilities of the number of speakers per meeting must be greater than 0. Got: g?zLThe probabilities of the number of speakers per meeting must sum to 1. Got: zTThe number of speakers per meeting and the number of probabilities must be the same.cS |jdjSNr supervisionsspeakercutrrr z)MeetingSampler.__init__..)keycSr<r=r>rArrrrCrDz%Creating samplers for each speaker...)descF)times preserve_idT) max_durationmax_cutsshuffler4)allsumlensamplersrrsortedr r from_cutslistrepeatr0r1nprandom RandomStatenprRandomrng_remaining_meetings) rrr rr0r1r2r3r4spkspk_cutssamplerrrrrpsV     zMeetingSampler.__init__cCs|jD]}t|q|Sr$)rOvaluesiter)rr]rrr__iter__s zMeetingSampler.__iter__c Cs|jdur |jdkr tt|jt|jkrtt|jj|j|jdt|j}|j t |j |}t g}|D]}|j|}z t|}||7}Wq=ty[|j|=Yq=w|}|jdurl|jd8_t|dkrt|St|S)Nr)r;r6)rZ StopIterationrNrOminr0rWchoicer1rYsamplerRkeysr rQnextrK)rNthis_batch_spk_ids utterancesspk_idr] this_batchrrr__next__s.     zMeetingSampler.__next__)NNr-Nr.r/r) rr%r&r'r rr+rrfloatrr`rlrrrrr,\s8  < r,rr"rc sg}t|dkrtdd|Dnd}|D]A}t|j|krOg}|fdd}t|j|D]\}}|t||j |dq3|t||dq|| qt |S)a Use provided RIRs to convolve each track of the input CutSet. The cuts here are MixedCut objects containing different speakers in different tracks. To reverberate, we choose a random RIR containing as many Recording objects as there are tracks in the MixedCut. If impulse responses are not provided, we use the fast randomized approximation method to simulate artificial single-channel RIRs. :param cuts: a CutSet containing MixedCut objects. :param rirs: one or more RecordingSet (each set is a group of RIRs from the same room). :return: a CutSet containing MixedCut objects reverberated with the provided RIRs. rcss|]}t|VqdSr$rN)r7 rir_grouprrrr9r:z#reverberate_cuts..cs t|kSr$rn)r num_speakersrrrCrDz"reverberate_cuts..rA)tracks) rNmaxrsfilterrUzipappendr rB reverb_rirr rQ) rr"out_cuts max_sourcesrBrsrotrackrirrrqrreverberate_cutss"  r})r'r)rU itertoolsrtypingrrrnumpyrTrlhotserr lhotse.cutr lhotse.dataset.samplingr lhotse.utilsr r MAX_TASKS_WAITINGABCr r,r}rrrrs    Gx