o }oi~@sddlZddlZddlmZddlmZmZmZmZm Z ddl Z ddl Z ddl mZddlmZmZmZddlmZddlmZddlmZdd lmZmZdd lmZdd lmZdd l m!Z!dd l"m#Z#m$Z$ddl%m&Z&m'Z'm(Z(m)Z)ddl*m+Z+m,Z,ddl-m.Z.ddl/m0Z0m1Z1ddl2m3Z3ddl4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;ddlZ>ddl?m@Z@ddlAmBZBmCZCmDZDmEZEmFZFmGZGddlHmIZIdgZJGddde#e$e&e(e'ZKdS)N)ceil)AnyDictListOptionalUnion)Trainer) DictConfig OmegaConf open_dict) DataLoader)audio_to_text_dataset)_AudioTextDataset)AudioToCharDALIDataset DALIOutputs)LhotseSpeechToTextBpeDataset)CTCLoss)WER)ASRModelExportableEncDecModel)ASRModuleMixinASRTranscriptionMixin InterCTCMixinTranscribeConfig)GenericTranscriptionTypeTranscriptionReturnType)ChannelSelectorType) CTCDecodingCTCDecodingConfig)get_semi_sorted_batch_sampler) Hypothesis)process_timestamp_outputs)!get_lhotse_dataloader_from_config) make_parser)PretrainedModelInfo typecheck) AccessMixin) AudioSignal LabelsType LengthsType LogprobsType NeuralTypeSpectrogramType)loggingEncDecCTCModelcsheZdZdZdSdedeffdd Z      dTd eee ee j e j efd ed ededeedededeedeedeffdd ZdSde edeefddZdUdedefddZdeefddZd eeeeffd!d"Zd#eeeeffd$d%Zd&eeeeffd'd(Zedeeeeffd)d*Zedeeeeffd+d,Z e! dVd-d.Z"d/d0Z#dWd1d2Z$dWd3d4Z%dWd5d6Z&dWd7effd8d9 Z'dWd7effd:d; Z(dWdd?Z* d@e+dAefdBdCZ,dAede-fdDdEZ.dFe/e/e0fdGdHZ1deddIfdJdKZ2e3de e4fdLdMZ5ede efdNdOZ6edPdQZ7e7j8dRdQZ7Z9S)Xr.z0Base class for encoder decoder CTC-based models.Ncfgtrainercs,d|_|dur |j|_tj||dt|jj|_t|jj|_t|jXd|jj vs:|jj j sAt |jdrA|jj |jj _ d|jj vsL|jj j sPt d|jj jdkry|jj jdurytd|jj jt|jj jt|jj j|j d<Wdn1swYt|jj |_ t|j jdd|jd d d |_t |jd r|jjdurt|jj|_nd|_|jd d}|durtt}t|j ||j_Wdn1swYt|jjt |j jd|_t!|j|jddd|jddd|_"|#|j$dddd|%dS)N)r/r0feat_in _feat_outz1param feat_in of the decoder's config is not set!zP Replacing placeholder number of classes ({}) with actual number of classes - {} num_classesT ctc_reduction mean_batchr4 zero_infinity reduction spec_augmentdecoding) vocabularyuse_cerFlog_predictionr;r=dist_sync_on_stepr>decoderlosswer) decoder_name loss_namewer_name)& world_sizesuper__init__r.from_config_dict_cfg preprocessorencoderr rAr2hasattrr3 ValueErrorr/r4r<r-infoformatlenrnum_classes_with_blankgetrBr:spec_augmentationr structuredrr;r to_containerrrCsetup_optimization_flagssetup_interctcsetup_adapters)selfr/r0 decoding_cfg __class__Z/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/asr/models/ctc_models.pyrI4sb           zEncDecCTCModel.__init__FrTaudio batch_sizereturn_hypotheses num_workerschannel_selector augmentorverbose timestampsoverride_configreturnc s|p | dur | jnd}|dur~|s| durI| jrItdd}t|jjd|jj_d|jj_Wdn1s:wY|j|jjddn5t|jj|jj dd|jj_|jj dd|jj_Wdn1spwY|j|jjddt j ||||||||| d S) a Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. Args: audio: (a single or list) of paths to audio files or a np.ndarray/tensor audio array or path to a manifest file. Can also be a dataloader object that provides values that can be consumed by the model. Recommended length per file is between 5 and 25 seconds. But it is possible to pass a few hours long file if enough GPU memory is available. batch_size: (int) batch size to use during inference. Bigger will result in better throughput performance but would use more memory. return_hypotheses: (bool) Either return hypotheses or text With hypotheses can do some postprocessing like getting timestamp or rescoring num_workers: (int) number of workers for DataLoader channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. timestamps: Optional(Bool): timestamps will be returned if set to True as part of hypothesis object (output.timestep['segment']/output.timestep['word']). Refer to `Hypothesis` class for more details. Default is None and would retain the previous state set by using self.change_decoding_strategy(). verbose: (bool) whether to display tqdm progress bar override_config: (Optional[TranscribeConfig]) override transcription config pre-defined by the user. **Note**: All other arguments in the function will be ignored if override_config is passed. You should call this argument as `model.transcribe(audio, override_config=TranscribeConfig(...))`. Returns: A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files NzTimestamps requested, setting decoding timestamps to True. Capture them in Hypothesis object, with output[idx].timestep['word'/'segment'/'char']TF)rhcompute_timestampspreserve_alignments) rbrcrdrerfrgrhrirj) rir-rPr r/r;rlrmchange_decoding_strategyrTrH transcribe) r[rbrcrdrerfrgrhrirjr]r_r`roxs8+  zEncDecCTCModel.transcribenew_vocabularyr\c Cs|jj|krtd|jjd|ddS|dus t|dkr'td||j}t|}||d<t||d<|`t ||_|` t |jj d d |jd d d |_ |dur^|jj}tt}tt|}t||}t|t|jjd|_t|j|jddd |jddd|_t|jj ||j_Wdn1swYt|jj ||j_Wdn1swYgd}|D](}||jvrt|j|t||j|d<Wdn1swYqtd|jjddS)a Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need model to learn capitalization, punctuation and/or special characters. If new_vocabulary == self.decoder.vocabulary then nothing will be changed. Args: new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, this is target alphabet. Returns: None zOld z and new z match. Not changing anything.Nrz;New vocabulary must be non-empty list of chars. But I got: r<r4r1Tr5r6r7r\r<r=Fr>r?)train_ds validation_dstest_dslabelszChanged decoder to output to z vocabulary.)rAr<r-warningrRrOto_config_dictcopydeepcopyr.rJrBrrSrKrTr/r;r rVrcreaterWmergerrrCr rP)r[rpr\decoder_confignew_decoder_config decoding_clsds_keyskeyr_r_r`change_vocabularysX               z EncDecCTCModel.change_vocabularycCs|dur td|jj}tt}tt|}t ||}t |t|j j d|_t |j|jj|jjdd|_|dd|j _t|jj ||j_Wdn1sWwY|rmtdt|jjdSdS) af Changes decoding strategy used during CTC decoding process. Args: decoding_cfg: A config for the decoder, which is optional. If the decoding type needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. verbose: (bool) whether to display logging information NzONo `decoding_cfg` passed when changing decoding strategy, using internal configrqT)r;r=r>r@ temperatureg?zChanged decoding strategy to )r-rPr/r;r rVrrzrWr{rrAr<rrCr=r>rTrr to_yaml)r[r\rhr~r_r_r`rn s,    z'EncDecCTCModel.change_decoding_strategyconfigcCstj|j|ddtj|j|dd|drZt||dds"|jn|d|dds/|jn|dtt|dd|d d |d d |d d |ddd|ddddStj ||j |j|j|j ddd}|durrdSt |t ry|S|d}t |tjjjrd}t|dr|j}nt|jddr|jdj}n |jdjdj}d}|ddrt |tstdt|t|||}d|d<d|d<d}tjjj||d|d||dd||dd|ddd S)N sample_rate)rru use_lhotse do_transcribeF global_rankrGparseren unk_index blank_indexnormalize_transcripts)runameunk_idblank_id do_normalize) tokenizer return_cuts)rrGdatasetrL)r local_rankrrGpreprocessor_cfgshuffle collate_fnruse_semi_sorted_batchingzmSemi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset but found dataset of type rc drop_lastre pin_memory) rrcsampler batch_samplerrrrrer)r )inject_dataloader_value_from_model_configr/rTr"rrGrr#*get_audio_to_text_char_dataset_from_configrrK isinstancertorchutilsdataIterableDatasetrNrdatasetsr RuntimeErrortyperr )r[rrrrrr_r_r`_setup_dataloader_from_config0sx                z,EncDecCTCModel._setup_dataloader_from_configtrain_data_configcCsd|vrd|d<|jd|d|j|d|_|jdur\t|jdr^t|jjtjjj r`|j durPt|j j t rPt |j j tt|jj|j|d|j _ dS|j durbtd dSdSdSdSdS) a Sets up the training data loader via a Dict-like object. Args: train_data_config: A config that contains the information regarding construction of an ASR Training dataset. Supported Datasets: - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` rTtrain dataset_namerrNrrczModel Trainer was not set before constructing the dataset, incorrect number of training batches will be used. Please set the trainer and rebuild the dataset.)_update_dataset_configr _train_dlrNrrrrrr_trainerlimit_train_batchesfloatintrrRrGr-rv)r[rr_r_r`setup_training_data~s*     z"EncDecCTCModel.setup_training_dataval_data_configcC0d|vrd|d<|jd|d|j|d|_dS)a Sets up the validation data loader via a Dict-like object. Args: val_data_config: A config that contains the information regarding construction of an ASR Training dataset. Supported Datasets: - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` rF validationrrN)rr_validation_dl)r[rr_r_r`setup_validation_dataz$EncDecCTCModel.setup_validation_datatest_data_configcCr)a Sets up the test data loader via a Dict-like object. Args: test_data_config: A config that contains the information regarding construction of an ASR Training dataset. Supported Datasets: - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` rFtestrrN)rr_test_dl)r[rr_r_r`setup_test_datarzEncDecCTCModel.setup_test_datac Csxt|jdrt|jjd}nt}td|ddttdtddtdtddttdtddttdtdddS) N _sample_rate)freqBTT)optionalr)rDr) input_signalinput_signal_lengthprocessed_signalprocessed_signal_length sample_id)rNrLr'rr+tupler)r,)r[input_signal_eltyper_r_r` input_typess  zEncDecCTCModel.input_typescCs(tdtttdttdtdS)N)rrrrr)outputsencoded_lengthsgreedy_predictions)r+r*rr)r(r[r_r_r` output_typess  zEncDecCTCModel.output_typesc Cs|duo|du}|duo|du}||Adkrt|d|s(|j||d\}}|jdur7|jr7|j||d}|j||d}|d}|d} |j|d } | jd dd } | | | fS) a/ Forward pass of the model. Args: input_signal: Tensor that represents a batch of raw audio signals, of shape [B, T]. T here represents timesteps, with 1 second of audio represented as `self.sample_rate` number of floating point values. input_signal_length: Vector of length B, that contains the individual lengths of the audio sequences. processed_signal: Tensor that represents a batch of processed audio signals, of shape (B, D, T) that has undergone processing via some DALI preprocessor. processed_signal_length: Vector of length B, that contains the individual lengths of the processed audio sequences. 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Args: config: A python dictionary which contains the following keys: paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. Recommended length per file is between 5 and 25 seconds. batch_size: (int) batch size to use during inference. Bigger will result in better throughput performance but would use more memory. temp_dir: (str) A temporary directory where the audio manifest is temporarily stored. num_workers: (int) number of workers. Depends of the batch_size and machine. 0 - only the main process will load batches, 1 - one worker (not main process) Returns: A pytorch DataLoader for the given audio file(s). manifest_filepathrctemp_dirz manifest.jsonpaths2audio_filesFrer1TrfN) r^rrurc trim_silencerrerrfrgr)ospathjoinminrRrLrr rWrAr<rT cpu_countrr )r[rr^rc dl_configtemporary_datalayerr_r_r`_setup_transcribe_dataloaders&    z+EncDecCTCModel._setup_transcribe_dataloadercCsXg}tdddd}||tdddd}||tdd d d}||td d d d}||tdddd}||tdddd}||tdddd}||tdddd}||tdddd}||tdddd}||td d!d"d}||td#d$d%d}||td#d$d%d}||td&d'd(d}|||S))z This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. zQuartzNet15x5Base-EnaQuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. Please visit https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels for further details.zmhttps://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo)pretrained_model_name descriptionlocationstt_en_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemostt_en_jasper10x5drzpFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_jasper10x5drzuhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/files/stt_en_jasper10x5dr.nemostt_ca_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_quartznet15x5/versions/1.0.0rc1/files/stt_ca_quartznet15x5.nemostt_it_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_quartznet15x5/versions/1.0.0rc1/files/stt_it_quartznet15x5.nemostt_fr_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_quartznet15x5/versions/1.0.0rc1/files/stt_fr_quartznet15x5.nemostt_es_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_quartznet15x5/versions/1.0.0rc1/files/stt_es_quartznet15x5.nemostt_de_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_quartznet15x5/versions/1.0.0rc1/files/stt_de_quartznet15x5.nemostt_pl_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_quartznet15x5/versions/1.0.0rc1/files/stt_pl_quartznet15x5.nemostt_ru_quartznet15x5zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_quartznet15x5/versions/1.0.0rc1/files/stt_ru_quartznet15x5.nemostt_zh_citrinet_512zpFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512zuhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemostt_zh_citrinet_1024_gamma_0_25z|For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25zhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemoasr_talknet_alignerzpFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:asr_talknet_alignerzzhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/asr_talknet_aligner/versions/1.0.0rc1/files/qn5x5_libri_tts_phonemes.nemo)r$r+)clsresultsmodelr_r_r`list_available_modelss              z$EncDecCTCModel.list_available_modelscCsgdS)N)rMrAr_rr_r_r`adapter_module_namessz#EncDecCTCModel.adapter_module_namescCs|jSr@_werrr_r_r`rCszEncDecCTCModel.wercCs ||_dSr@r)r[rCr_r_r`rCs r@)raFrNNTNN)T)NNNNrZ):__name__ __module__ __qualname____doc__r rrIrstrrrrnpndarrayr rboolrrrrrorrnrrrrrpropertyr+rrr%rrrr(r,r0r5r?rArrGrrXrr r]ri classmethodr$r|r~rCsetter __classcell__r_r_r]r`r.1sG   JG'N-  5 9  *  (*r )Lrxrbmathrtypingrrrrrrrrlightning.pytorchr omegaconfr r r torch.utils.datar nemo.collections.asr.datar 'nemo.collections.asr.data.audio_to_textr,nemo.collections.asr.data.audio_to_text_dalirr.nemo.collections.asr.data.audio_to_text_lhotsernemo.collections.asr.losses.ctcr nemo.collections.asr.metrics.werr%nemo.collections.asr.models.asr_modelrr!nemo.collections.asr.parts.mixinsrrrr/nemo.collections.asr.parts.mixins.transcriptionrr0nemo.collections.asr.parts.preprocessing.segmentr2nemo.collections.asr.parts.submodules.ctc_decodingrr-nemo.collections.asr.parts.utils.asr_batchingr+nemo.collections.asr.parts.utils.rnnt_utilsr 0nemo.collections.asr.parts.utils.timestamp_utilsr!#nemo.collections.common.data.lhotser"3nemo.collections.common.parts.preprocessing.parsersr#nemo.core.classes.commonr$r%nemo.core.classes.mixinsr&nemo.core.neural_typesr'r(r)r*r+r, nemo.utilsr-__all__r.r_r_r_r`s>