o %ݫi@s,dZddlZddlmZGdddeZdS)aQ Specifies the inference interfaces for speaker recognition 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)EncoderClassifiercs<eZdZdZgdZfddZ d ddZd d ZZS) SpeakerRecognitionazA ready-to-use model for speaker recognition. It can be used to perform speaker verification with verify_batch(). Arguments --------- *args : tuple **kwargs : dict Arguments are forwarded to ``Pretrained`` parent class. Example ------- >>> import torchaudio >>> from speechbrain.inference.speaker import SpeakerRecognition >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> verification = SpeakerRecognition.from_hparams( ... source="speechbrain/spkrec-ecapa-voxceleb", ... savedir=tmpdir, ... ) >>> # Perform verification >>> signal, fs = torchaudio.load("tests/samples/single-mic/example1.wav") >>> signal2, fs = torchaudio.load("tests/samples/single-mic/example2.flac") >>> score, prediction = verification.verify_batch(signal, signal2) )compute_features mean_var_normembedding_modelmean_var_norm_embcs(tj|i|tjjddd|_dS)Ngư>)dimeps)super__init__torchnnCosineSimilarity similarity)selfargskwargs __class__Q/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/inference/speaker.pyr 8szSpeakerRecognition.__init__N?c Cs8|j||dd}|j||dd}|||}|||kfS)aPerforms speaker verification with cosine distance. It returns the score and the decision (0 different speakers, 1 same speakers). Arguments --------- wavs1 : Torch.Tensor torch.Tensor containing the speech waveform1 (batch, time). Make sure the sample rate is fs=16000 Hz. wavs2 : Torch.Tensor torch.Tensor containing the speech waveform2 (batch, time). Make sure the sample rate is fs=16000 Hz. wav1_lens : Torch.Tensor torch.Tensor containing the relative length for each sentence in the length (e.g., [0.8 0.6 1.0]) wav2_lens : Torch.Tensor torch.Tensor containing the relative length for each sentence in the length (e.g., [0.8 0.6 1.0]) threshold : Float Threshold applied to the cosine distance to decide if the speaker is different (0) or the same (1). Returns ------- score The score associated to the binary verification output (cosine distance). prediction The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise. F) normalize) encode_batchr) rwavs1wavs2 wav1_lens wav2_lens thresholdemb1emb2scorerrr verify_batch<s#  zSpeakerRecognition.verify_batchc KsX|j|fi|}|j|fi|}|d}|d}|||\}} |d| dfS)akSpeaker verification with cosine distance Returns the score and the decision (0 different speakers, 1 same speakers). Arguments --------- path_x : str Path to file x path_y : str Path to file y **kwargs : dict Arguments to ``load_audio`` Returns ------- score The score associated to the binary verification output (cosine distance). prediction The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise. r) load_audio unsqueezer#) rpath_xpath_yr waveform_x waveform_ybatch_xbatch_yr"decisionrrr verify_filesds   zSpeakerRecognition.verify_files)NNr) __name__ __module__ __qualname____doc__MODULES_NEEDEDr r#r- __classcell__rrrrrs  (r)r1r !speechbrain.inference.classifiersrrrrrrs