o %Ý«i[ã@s,dZddlZddlmZGdd„deƒZdS)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 éN)Ú PretrainedcsNeZdZdZdgZddgZ‡fdd„Zdd„Zd d „Zd d „Z d d„Z ‡Z S)ÚEncoderDecoderS2UTa"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 Ú sample_rateÚencoderÚdecodercs tƒj|i|¤Ž|jj|_dS)N)ÚsuperÚ__init__Úhparamsr)ÚselfÚargsÚkwargs©Ú __class__©úL/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/inference/ST.pyr.szEncoderDecoderS2UT.__init__cCs@| |¡}| |j¡}| d¡}t dg¡}| ||¡}|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. rgð?)Ú load_audioÚtoÚdeviceÚ unsqueezeÚtorchÚtensorÚtranslate_batch)r ÚpathÚaudioÚbatchÚ rel_lengthÚpredicted_tokensrrrÚtranslate_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 )ÚfloatrrÚmodsr)r ÚwavsÚwav_lensÚ encoder_outrrrÚ encode_batchHszEncoderDecoderS2UT.encode_batchcCs\t ¡ | |j¡}| ||¡}|j ||¡\}}}}Wdƒ|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)rÚno_gradrrr#rr)r r r!r"rÚ_rrrrds    ýüz"EncoderDecoderS2UT.translate_batchcCs | ||¡S)zRuns full translation)r#)r r r!rrrÚforwardƒs zEncoderDecoderS2UT.forward) Ú__name__Ú __module__Ú __qualname__Ú__doc__ÚHPARAMS_NEEDEDÚMODULES_NEEDEDrrr#rr&Ú __classcell__rrr rrs r)r*rÚ speechbrain.inference.interfacesrrrrrrÚs