o ½e¦i^ã@s,dZddlZddlmZGdd„deƒZdS)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 éN)Ú Pretrainedc@sPeZdZdZgd¢Zdd„Zdd„Zdd„Zd d „Zd d „Z d d„Z dd„Z dS)ÚSpeech_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 )Ú input_normÚwav2vecÚ output_mlpcCs4| |¡}| d¡}t dg¡}| |||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. rgð?)Ú load_audioÚ unsqueezeÚtorchÚtensorÚ diarize_batch)ÚselfÚpathÚwaveformÚbatchÚ rel_lengthÚ frame_class©rú_/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/inference/diarization.pyÚ diarize_file's  z'Speech_Emotion_Diarization.diarize_filecCsnt|jƒdkr | 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 érN)Údevice) ÚlenÚshaperr ÚonesrÚtoÚmodsrÚwav2vec2)r ÚwavsÚwav_lensÚoutputsrrrÚ encode_batch;s  z'Speech_Emotion_Diarization.encode_batchc Cs`| ||¡}|j |¡}|j |¡}|j |¡}tj|dd\}}|jj  |¡}|  ||¡} | S)a‰Get 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. éÿÿÿÿ)Údim) r ÚhparamsÚavg_poolrrÚ log_softmaxr ÚmaxÚ label_encoderÚ decode_torchÚpreds_to_diarization) r rrÚbatch_idrÚ averaged_outÚscoreÚindexÚpredsÚresultsrrrr Zs     z(Speech_Emotion_Diarization.diarize_batchc Csži}tt|ƒƒD]D}||}g}tt|ƒƒD]%}t|jjd|dƒ}t||jjddƒ} | |||| ||g¡q| |¡}dd„|Dƒ|||<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{®Gáz”?écSs$g|]}|d|d|ddœ‘qS)rr0é)ÚstartÚendÚemotionr)Ú.0ÚkrrrÚ —sÿzCSpeech_Emotion_Diarization.preds_to_diarization..)ÚrangerÚroundr#ÚstrideÚ window_lengthÚappendÚ!merge_ssegs_same_emotion_adjacent) r Ú predictionr*r/ÚiÚpredÚlolÚjr2r3rrrr)|s ÿz/Speech_Emotion_Diarization.preds_to_diarizationcCs| |||¡S)z1Get emotion diarization for a batch of waveforms.)r )r rrr*rrrÚforwardœsz"Speech_Emotion_Diarization.forwardcCs||krdSdS)aReturns True if segments are overlapping. Arguments --------- end1 : float End time of the first segment. start2 : float Start time of the second segment. Returns ------- overlapped : bool True of segments overlapped else False. Example ------- >>> from speechbrain.processing import diarization as diar >>> diar.is_overlapped(5.5, 3.4) True >>> diar.is_overlapped(5.5, 6.4) False FTr)r Úend1Ústart2rrrÚ is_overlapped sz(Speech_Emotion_Diarization.is_overlappedcCs¤g}|d}d}tdt|ƒƒD]5}||}| |d|d¡r=|d|dkr=|d|d<|t|ƒdkr>> from speechbrain.utils.EDER import merge_ssegs_same_emotion_adjacent >>> lol=[['u1', 0.0, 7.0, 'a'], ... ['u1', 7.0, 9.0, 'a'], ... ['u1', 9.0, 11.0, 'n'], ... ['u1', 11.0, 13.0, 'n'], ... ['u1', 13.0, 15.0, 'n'], ... ['u1', 15.0, 16.0, 'a']] >>> merge_ssegs_same_emotion_adjacent(lol) [['u1', 0.0, 9.0, 'a'], ['u1', 9.0, 15.0, 'n'], ['u1', 15.0, 16.0, 'a']] rFrr0r1Tr!)r8rrFr<)r rAÚnew_lolÚssegÚflagr?Ú next_ssegrrrr=½s$ÿ  € zs