o %ݫi^@s,dZddlZddlmZGdddeZdS)aI Specifies the inference interfaces for diarization modules. Authors: * Aku Rouhe 2021 * Peter Plantinga 2021 * Loren Lugosch 2020 * Mirco Ravanelli 2020 * Titouan Parcollet 2021 * Abdel Heba 2021 * Andreas Nautsch 2022, 2023 * Pooneh Mousavi 2023 * Sylvain de Langen 2023 * Adel Moumen 2023 * Pradnya Kandarkar 2023 N) Pretrainedc@sPeZdZdZgdZddZddZddZd d Zd d Z d dZ ddZ dS)Speech_Emotion_Diarizationa A ready-to-use SED interface (audio -> emotions and their durations) Arguments --------- See ``Pretrained`` Example ------- >>> from speechbrain.inference.diarization import Speech_Emotion_Diarization >>> tmpdir = getfixture("tmpdir") >>> sed_model = Speech_Emotion_Diarization.from_hparams(source="speechbrain/emotion-diarization-wavlm-large", savedir=tmpdir,) # doctest: +SKIP >>> sed_model.diarize_file("speechbrain/emotion-diarization-wavlm-large/example.wav") # doctest: +SKIP ) input_normwav2vec output_mlpcCs4||}|d}tdg}||||g}|S)a&Get emotion diarization of a spoken utterance. Arguments --------- path : str Path to audio file which to diarize. Returns ------- list of dictionary: List[Dict[List]] The emotions and their temporal boundaries. rg?) load_audio unsqueezetorchtensor diarize_batch)selfpathwaveformbatch rel_length frame_classrU/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/inference/diarization.py diarize_file's  z'Speech_Emotion_Diarization.diarize_filecCsnt|jdkr |d}|durtj|jd|jd}||j||j}}|j||}|j |}|S)a)Encodes audios into fine-grained emotional embeddings Arguments --------- wavs : torch.Tensor Batch of waveforms [batch, time, channels]. wav_lens : torch.Tensor Lengths of the waveforms relative to the longest one in the batch, tensor of shape [batch]. The longest one should have relative length 1.0 and others len(waveform) / max_length. Used for ignoring padding. Returns ------- torch.Tensor The encoded batch rN)device) lenshaperr onesrtomodsrwav2vec2)r wavswav_lensoutputsrrr encode_batch;s  z'Speech_Emotion_Diarization.encode_batchc Cs`|||}|j|}|j|}|j|}tj|dd\}}|jj |}| ||} | S)aGet emotion diarization of a batch of waveforms. The waveforms should already be in the model's desired format. You can call: ``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)`` to get a correctly converted signal in most cases. Arguments --------- wavs : torch.Tensor Batch of waveforms [batch, time, channels]. wav_lens : torch.Tensor Lengths of the waveforms relative to the longest one in the batch, tensor of shape [batch]. The longest one should have relative length 1.0 and others len(waveform) / max_length. Used for ignoring padding. batch_id : torch.Tensor id of each batch (file names etc.) Returns ------- list of dictionary: List[Dict[List]] The emotions and their temporal boundaries. )dim) r hparamsavg_poolrr log_softmaxr max label_encoder decode_torchpreds_to_diarization) r rrbatch_idr averaged_outscoreindexpredsresultsrrrr Zs     z(Speech_Emotion_Diarization.diarize_batchc Csi}tt|D]D}||}g}tt|D]%}t|jjd|d}t||jjdd} ||||| ||gq||}dd|D|||<q|S)alConvert frame-wise predictions into a dictionary of diarization results. Arguments --------- prediction : torch.Tensor Frame-wise predictions batch_id : str The id for this batch Returns ------- dictionary A dictionary with the start/end of each emotion g{Gz?cSs$g|]}|d|d|ddqS)rr0)startendemotionr).0krrr szCSpeech_Emotion_Diarization.preds_to_diarization..)rangerroundr#stride window_lengthappend!merge_ssegs_same_emotion_adjacent) r predictionr*r/ipredloljr2r3rrrr)|s z/Speech_Emotion_Diarization.preds_to_diarizationcCs||||S)z1Get emotion diarization for a batch of waveforms.)r )r rrr*rrrforwardsz"Speech_Emotion_Diarization.forwardcCs||krdSdS)aReturns True if segments are overlapping. Arguments --------- end1 : float End time of the first segment. start2 : float Start time of the second segment. Returns ------- overlapped : bool True of segments overlapped else False. Example ------- >>> from speechbrain.processing import diarization as diar >>> diar.is_overlapped(5.5, 3.4) True >>> diar.is_overlapped(5.5, 6.4) False FTr)r end1start2rrr is_overlappedsz(Speech_Emotion_Diarization.is_overlappedcCsg}|d}d}tdt|D]5}||}||d|dr=|d|dkr=|d|d<|t|dkr>> from speechbrain.utils.EDER import merge_ssegs_same_emotion_adjacent >>> lol=[['u1', 0.0, 7.0, 'a'], ... ['u1', 7.0, 9.0, 'a'], ... ['u1', 9.0, 11.0, 'n'], ... ['u1', 11.0, 13.0, 'n'], ... ['u1', 13.0, 15.0, 'n'], ... ['u1', 15.0, 16.0, 'a']] >>> merge_ssegs_same_emotion_adjacent(lol) [['u1', 0.0, 9.0, 'a'], ['u1', 9.0, 15.0, 'n'], ['u1', 15.0, 16.0, 'a']] rFrr0r1Tr!)r8rrFr<)r rAnew_lolssegflagr? next_ssegrrrr=s$   zs