o 7wi[@s,dZddlZddlmZGdddeZdS)aU Specifies the inference interfaces for Speech Translation (ST) 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) PretrainedcsNeZdZdZdgZddgZfddZddZd d Zd d Z d dZ Z S)EncoderDecoderS2UTa"A ready-to-use Encoder Decoder for speech-to-unit translation model The class can be used to run the entire encoder-decoder S2UT model (translate_file()) to translate speech. The given YAML must contains the fields specified in the *_NEEDED[] lists. Arguments --------- *args : tuple **kwargs : dict Arguments are forwarded to ``Pretrained`` parent class. Example ------- >>> from speechbrain.inference.ST import EncoderDecoderS2UT >>> tmpdir = getfixture("tmpdir") >>> s2ut_model = EncoderDecoderS2UT.from_hparams(source="speechbrain/s2st-transformer-fr-en-hubert-l6-k100-cvss", savedir=tmpdir) # doctest: +SKIP >>> s2ut_model.translate_file("speechbrain/s2st-transformer-fr-en-hubert-l6-k100-cvss/example-fr.wav") # doctest: +SKIP sample_rateencoderdecodercs tj|i||jj|_dS)N)super__init__hparamsr)selfargskwargs __class__U/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/speechbrain/inference/ST.pyr.szEncoderDecoderS2UT.__init__cCs@||}||j}|d}tdg}|||}|dS)a6Translates the given audiofile into a sequence speech unit. Arguments --------- path : str Path to audio file which to translate. Returns ------- int[] The audiofile translation produced by this speech-to-unit translationmodel. rg?) load_audiotodevice unsqueezetorchtensortranslate_batch)r pathaudiobatch rel_lengthpredicted_tokensrrrtranslate_file2s     z!EncoderDecoderS2UT.translate_filecCs4|}||j||j}}|j||}|S)aEncodes the input audio into a sequence of hidden states The waveforms should already be in the model's desired format. You can call: ``normalized = EncoderDecoderS2UT.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. Returns ------- torch.tensor The encoded batch )floatrrmodsr)r wavswav_lens encoder_outrrr encode_batchHszEncoderDecoderS2UT.encode_batchcCs\t ||j}|||}|j||\}}}}Wd|S1s'wY|S)aMTranslates the input audio into a sequence of words The waveforms should already be in the model's desired format. You can call: ``normalized = EncoderDecoderS2UT.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. Returns ------- list Each waveform in the batch translated. tensor Each predicted token id. N)rno_gradrrr#rr)r r r!r"r_rrrrds    z"EncoderDecoderS2UT.translate_batchcCs |||S)zRuns full translation)r#)r r r!rrrforwards zEncoderDecoderS2UT.forward) __name__ __module__ __qualname____doc__HPARAMS_NEEDEDMODULES_NEEDEDrrr#rr& __classcell__rrr rrs r)r*r speechbrain.inference.interfacesrrrrrrs