o }o™igˆã@sddlZddlZddlmZmZmZmZddlZddlm Z m Z m Z m Z ddl mZddlmZddlmZddlmZddlmZdd lmZdd lmZdd lmZdd lmZmZdd l m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'm(Z(dgZ)Gdd„deeƒZ*dS)éN)ÚDictÚListÚOptionalÚUnion)Ú DictConfigÚ ListConfigÚ OmegaConfÚ open_dict)Úaudio_to_text_dataset)Ú_AudioTextDataset)ÚAudioToBPEDALIDataset)ÚLhotseSpeechToTextBpeDataset)ÚCTCLoss)ÚWER)ÚEncDecCTCModel)Ú ASRBPEMixin)ÚCTCBPEDecodingÚCTCBPEDecodingConfig)Úget_semi_sorted_batch_sampler)Ú!get_lhotse_dataloader_from_config)ÚPretrainedModelInfo)ÚloggingÚ model_utilsÚEncDecCTCModelBPEcsœeZdZdZddef‡fdd„ Zdeefdd„Zded d fd d „Z dd e e efde deefdd„Z ddede fdd„Zed eefdd„ƒZ‡ZS)rz9Encoder decoder CTC-based models with Byte Pair Encoding.NÚcfgcsht |¡}t |¡}d|vrtdƒ‚| |j¡|jj ¡}t|ƒ|jdkr/t |ƒ|j _ n t t |  ¡ƒƒ|j _ Wdƒn1sCwY|j d}|dkrct d |t|ƒ¡¡t|ƒ|j d<tƒj||d|j dd¡}|dur”t t¡}t|jƒ ||j_Wdƒn1swYt|jj|jd |_t|j|j d d ¡d |j d d ¡d|_dS)NÚ tokenizerz:`cfg` must have `tokenizer` config to create a tokenizer !ÚaggÚ num_classesézP Replacing placeholder number of classes ({}) with actual number of classes - {})rÚtrainerÚdecoding)rÚuse_cerFTÚlog_prediction)r r!Údist_sync_on_stepr")rÚ#convert_model_config_to_dict_configÚmaybe_update_config_versionÚ ValueErrorÚ_setup_tokenizerrÚ get_vocabr Útokenizer_typerÚdecoderÚ vocabularyÚlistÚkeysrÚinfoÚformatÚlenÚsuperÚ__init__rÚgetrÚ structuredrr rrÚ_cfgÚwer)Úselfrrr+rÚ decoding_cfg©Ú __class__©ú^/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/asr/models/ctc_bpe_models.pyr2*sD      €û ÿÿ   ÿ   üzEncDecCTCModelBPE.__init__Úconfigc Csˆ| d¡r0t|| dd¡s|jn| d¡| dd¡s|jn| d¡t|j| dd¡d|jdStj||j|j|j|j|j  dd¡d }|durJdSt |t ƒrQ|S|d }t |t j jjƒr_d}t|d ƒrh|j}nt|jd d ƒrw|jd j}n |jd jd j}d}| d d¡r¦t |tƒs–tdt|ƒ›ƒ‚t|||ƒ}d|d<d|d<d}t j jj||d|d|| dd¡|| dd ¡| dd¡d S)NÚ use_lhotseÚ do_transcribeFÚ global_rankÚ world_size)rÚ return_cuts)r@rAÚdatasetrÚ preprocessor)r=Ú local_rankr@rArÚpreprocessor_cfgÚshuffleÚ collate_fnrÚuse_semi_sorted_batchingzmSemi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset but found dataset of type Ú batch_sizeÚ drop_lastÚ num_workersÚ pin_memory) rCrJÚsamplerÚ batch_samplerrHrKrGrLrM)r3rr@rAr rr Ú)get_audio_to_text_bpe_dataset_from_configrErÚ isinstancer ÚtorchÚutilsÚdataÚIterableDatasetÚhasattrrHÚdatasetsr Ú RuntimeErrorÚtyperÚ DataLoader)r7r=rCrGrHrOr;r;r<Ú_setup_dataloader_from_config`sl  þõ ú     ÿÿ    ÷z/EncDecCTCModelBPE._setup_dataloader_from_configÚreturnztorch.utils.data.DataLoaderc Cs²d|vr |d}|d}ntj |dd¡}t|dt|dƒƒ}||jj|d| dt|t ¡dƒ¡d | d d ¡|j j  d d¡d œ}| d¡rO| d¡|d<|j t |ƒd}|S)a Setup function for a temporary data loader which wraps the provided audio file. 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). 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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. Args: new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) new_tokenizer_type: Either `agg`, `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers, whereas `wpe` is used for `BertTokenizer`. new_tokenizer_cfg: A config for the new tokenizer. if provided, pre-empts the dir and type Returns: None rzxNew tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: NzDNew tokenizer dir must be non-empty path to a directory. But I got: )ÚbpeÚwpez0New tokenizer type must be either `bpe` or `wpe`)ÚdirrYrzE Replacing old number of classes ({}) with new number of classes - {}rTÚ ctc_reductionÚ mean_batch)rÚ zero_infinityÚ reduction©r8rr!Fr"©r r!r"r#zChanged tokenizer to z vocabulary.)+rQrr&rdreÚisdirÚNotADirectoryErrorÚlowerrÚcreater'rr(ÚcopyÚdeepcopyr*Úto_config_dictr)rr+r,r-rr.r/r0rÚfrom_config_dictÚlossrÚnum_classes_with_blankr5r3rr r4rÚ to_containerÚmergerrr6r ) r7rnror8Únew_tokenizer_cfgÚ tokenizer_cfgr+Údecoder_configÚdecoder_num_classesÚ decoding_clsr;r;r<Úchange_vocabularyÑst ÿÿ ÿ     ÿÿ    ý    ü ÿ ÿz#EncDecCTCModelBPE.change_vocabularyTÚverbosecCsÖ|dur t d¡|jj}t t¡}t t |¡¡}t  ||¡}t ||j d|_t |j|j j|j jdd|_ | dd¡|j_t|jjƒ ||j_Wdƒn1sSwY|rit dt |jj¡›¡dSdS) a` 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: Whether to print the new config or not. NzONo `decoding_cfg` passed when changing decoding strategy, using internal configrwTrxÚ temperaturegð?zChanged decoding strategy to )rr.rr rr4rr|rƒr„rrrr6r!r"r3r*rŒr Úto_yaml)r7r8r‹r‰r;r;r<Úchange_decoding_strategy>s.   þü ÿÿz*EncDecCTCModelBPE.change_decoding_strategycCsg}tdddd}| |¡tdddd}| |¡tdd d d}| |¡td d d d}| |¡tdddd}| |¡tdddd}| |¡tdddd}| |¡tdddd}| |¡tdddd}| |¡tdddd}| |¡tdd d!d}| |¡td"d#d$d}| |¡td%d&d'd}| |¡td(d)d*d}| |¡td+d,d-d}| |¡td.d/d0d}| |¡td1d2d3d}| |¡td4d5d6d}| |¡td7d8d9d}| |¡td:d;dd?d}| |¡td@dAdBd}| |¡tdCdDdEd}| |¡tdFdGdHd}| |¡tdIdJdKd}| |¡tdLdJdMd}| |¡tdNdOdPd}| |¡tdQdRdSd}| |¡tdTdUdVd}| |¡tdWdXdYd}| |¡tdZd[d\d}| |¡td]d^d_d}| |¡td`dadbd}| |¡tdcddded}| |¡tdfdgdhd}| |¡tdidjdkd}| |¡tdldmdnd}| |¡tdodpdqd}| |¡tdrdsdtd}| |¡tdudvdwd}| |¡tdxdydzd}| |¡td{d|d}d}| |¡td~dd€d}| |¡|S)zÁ 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. Ústt_en_citrinet_256zpFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256zuhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256/versions/1.0.0rc1/files/stt_en_citrinet_256.nemo)Úpretrained_model_nameÚ descriptionÚlocationÚstt_en_citrinet_512zpFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512zuhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512/versions/1.0.0rc1/files/stt_en_citrinet_512.nemoÚstt_en_citrinet_1024zqFor details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024zwhttps://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024/versions/1.0.0rc1/files/stt_en_citrinet_1024.nemoÚstt_en_citrinet_256_gamma_0_25z{For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia: 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