/nemo/collections/asr/models/ctc_bpe_models.py

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import copy
import os
from typing import Dict, List, Optional, Union

import torch
from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict

from nemo.collections.asr.data import audio_to_text_dataset
from nemo.collections.asr.data.audio_to_text import _AudioTextDataset
from nemo.collections.asr.data.audio_to_text_dali import AudioToBPEDALIDataset
from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset
from nemo.collections.asr.losses.ctc import CTCLoss
from nemo.collections.asr.metrics.wer import WER
from nemo.collections.asr.models.ctc_models import EncDecCTCModel
from nemo.collections.asr.parts.mixins import ASRBPEMixin
from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig
from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler
from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config
from nemo.core.classes.common import PretrainedModelInfo
from nemo.utils import logging, model_utils

__all__ = ['EncDecCTCModelBPE']


class EncDecCTCModelBPE(EncDecCTCModel, ASRBPEMixin):
    """Encoder decoder CTC-based models with Byte Pair Encoding."""

    def __init__(self, cfg: DictConfig, trainer=None):
        # Convert to Hydra 1.0 compatible DictConfig
        cfg = model_utils.convert_model_config_to_dict_config(cfg)
        cfg = model_utils.maybe_update_config_version(cfg, make_copy=False)

        if 'tokenizer' not in cfg:
            raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !")

        # Setup the tokenizer
        self._setup_tokenizer(cfg.tokenizer)

        # Initialize a dummy vocabulary
        vocabulary = self.tokenizer.tokenizer.get_vocab()

        # Set the new vocabulary
        with open_dict(cfg):
            # sidestepping the potential overlapping tokens issue in aggregate tokenizers
            if self.tokenizer_type == "agg":
                cfg.decoder.vocabulary = ListConfig(vocabulary)
            else:
                cfg.decoder.vocabulary = ListConfig(list(vocabulary.keys()))

        # Override number of classes if placeholder provided
        num_classes = cfg.decoder["num_classes"]

        if num_classes < 1:
            logging.info(
                "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format(
                    num_classes, len(vocabulary)
                )
            )
            cfg.decoder["num_classes"] = len(vocabulary)

        super().__init__(cfg=cfg, trainer=trainer)

        # Setup decoding objects
        decoding_cfg = self.cfg.get('decoding', None)

        # In case decoding config not found, use default config
        if decoding_cfg is None:
            decoding_cfg = OmegaConf.structured(CTCBPEDecodingConfig)
            with open_dict(self.cfg):
                self.cfg.decoding = decoding_cfg

        self.decoding = CTCBPEDecoding(self.cfg.decoding, tokenizer=self.tokenizer)

        # Setup metric with decoding strategy
        self.wer = WER(
            decoding=self.decoding,
            use_cer=self._cfg.get('use_cer', False),
            dist_sync_on_step=True,
            log_prediction=self._cfg.get("log_prediction", False),
        )

    def _setup_dataloader_from_config(self, config: Optional[Dict]):
        if config.get("use_lhotse"):
            return get_lhotse_dataloader_from_config(
                config,
                # During transcription, the model is initially loaded on the CPU.
                # To ensure the correct global_rank and world_size are set,
                # these values must be passed from the configuration.
                global_rank=self.global_rank if not config.get("do_transcribe", False) else config.get("global_rank"),
                world_size=self.world_size if not config.get("do_transcribe", False) else config.get("world_size"),
                dataset=LhotseSpeechToTextBpeDataset(
                    tokenizer=self.tokenizer,
                    return_cuts=config.get("do_transcribe", False),
                ),
                tokenizer=self.tokenizer,
            )

        dataset = audio_to_text_dataset.get_audio_to_text_bpe_dataset_from_config(
            config=config,
            local_rank=self.local_rank,
            global_rank=self.global_rank,
            world_size=self.world_size,
            tokenizer=self.tokenizer,
            preprocessor_cfg=self.cfg.get("preprocessor", None),
        )

        if dataset is None:
            return None

        if isinstance(dataset, AudioToBPEDALIDataset):
            # DALI Dataset implements dataloader interface
            return dataset

        shuffle = config['shuffle']
        if isinstance(dataset, torch.utils.data.IterableDataset):
            shuffle = False

        if hasattr(dataset, 'collate_fn'):
            collate_fn = dataset.collate_fn
        elif hasattr(dataset.datasets[0], 'collate_fn'):
            # support datasets that are lists of entries
            collate_fn = dataset.datasets[0].collate_fn
        else:
            # support datasets that are lists of lists
            collate_fn = dataset.datasets[0].datasets[0].collate_fn

        batch_sampler = None
        if config.get('use_semi_sorted_batching', False):
            if not isinstance(dataset, _AudioTextDataset):
                raise RuntimeError(
                    "Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset "
                    f"but found dataset of type {type(dataset)}"
                )
            # set batch_size and batch_sampler to None to disable automatic batching
            batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config)
            config['batch_size'] = None
            config['drop_last'] = False
            shuffle = False

        return torch.utils.data.DataLoader(
            dataset=dataset,
            batch_size=config['batch_size'],
            sampler=batch_sampler,
            batch_sampler=None,
            collate_fn=collate_fn,
            drop_last=config.get('drop_last', False),
            shuffle=shuffle,
            num_workers=config.get('num_workers', 0),
            pin_memory=config.get('pin_memory', False),
        )

    def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader':
        """
        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).
        """

        if 'manifest_filepath' in config:
            manifest_filepath = config['manifest_filepath']
            batch_size = config['batch_size']
        else:
            manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json')
            batch_size = min(config['batch_size'], len(config['paths2audio_files']))

        dl_config = {
            'use_lhotse': config.get('use_lhotse', True),
            'manifest_filepath': manifest_filepath,
            'sample_rate': self.preprocessor._sample_rate,
            'batch_size': batch_size,
            'shuffle': False,
            'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)),
            'pin_memory': True,
            'channel_selector': config.get('channel_selector', None),
            'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False),
        }

        if config.get("augmentor"):
            dl_config['augmentor'] = config.get("augmentor")

        temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
        return temporary_datalayer

    def change_vocabulary(
        self,
        new_tokenizer_dir: Union[str, DictConfig],
        new_tokenizer_type: str,
        decoding_cfg: Optional[DictConfig] = None,
    ):
        """
        Changes vocabulary of the tokenizer 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.

        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

        """
        if isinstance(new_tokenizer_dir, DictConfig):
            if new_tokenizer_type == 'agg':
                new_tokenizer_cfg = new_tokenizer_dir
            else:
                raise ValueError(
                    f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer \
                        type is: {new_tokenizer_type}'
                )
        else:
            new_tokenizer_cfg = None

        if new_tokenizer_cfg is not None:
            tokenizer_cfg = new_tokenizer_cfg
        else:
            if not os.path.isdir(new_tokenizer_dir):
                raise NotADirectoryError(
                    f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}'
                )

            if new_tokenizer_type.lower() not in ('bpe', 'wpe'):
                raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`, got {new_tokenizer_type}')

            tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type})

        # Setup the tokenizer
        self._setup_tokenizer(tokenizer_cfg)

        # Initialize a dummy vocabulary
        vocabulary = self.tokenizer.tokenizer.get_vocab()

        # Set the new vocabulary
        decoder_config = copy.deepcopy(self.decoder.to_config_dict())
        # sidestepping the potential overlapping tokens issue in aggregate tokenizers
        if self.tokenizer_type == "agg":
            decoder_config.vocabulary = ListConfig(vocabulary)
        else:
            decoder_config.vocabulary = ListConfig(list(vocabulary.keys()))

        decoder_num_classes = decoder_config['num_classes']

        # Override number of classes if placeholder provided
        logging.info(
            "\nReplacing old number of classes ({}) with new number of classes - {}".format(
                decoder_num_classes, len(vocabulary)
            )
        )

        decoder_config['num_classes'] = len(vocabulary)

        del self.decoder
        self.decoder = EncDecCTCModelBPE.from_config_dict(decoder_config)
        del self.loss
        self.loss = CTCLoss(
            num_classes=self.decoder.num_classes_with_blank - 1,
            zero_infinity=True,
            reduction=self._cfg.get("ctc_reduction", "mean_batch"),
        )

        if decoding_cfg is None:
            # Assume same decoding config as before
            decoding_cfg = self.cfg.decoding

        # Assert the decoding config with all hyper parameters
        decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig)
        decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls))
        decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg)

        self.decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer)

        self.wer = WER(
            decoding=self.decoding,
            use_cer=self._cfg.get('use_cer', False),
            log_prediction=self._cfg.get("log_prediction", False),
            dist_sync_on_step=True,
        )

        # Update config
        with open_dict(self.cfg.decoder):
            self._cfg.decoder = decoder_config

        with open_dict(self.cfg.decoding):
            self._cfg.decoding = decoding_cfg

        logging.info(f"Changed tokenizer to {self.decoder.vocabulary} vocabulary.")

    def change_decoding_strategy(self, decoding_cfg: DictConfig, verbose: bool = True):
        """
        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.
        """
        if decoding_cfg is None:
            # Assume same decoding config as before
            logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config")
            decoding_cfg = self.cfg.decoding

        # Assert the decoding config with all hyper parameters
        decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig)
        decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls))
        decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg)

        self.decoding = CTCBPEDecoding(
            decoding_cfg=decoding_cfg,
            tokenizer=self.tokenizer,
        )

        self.wer = WER(
            decoding=self.decoding,
            use_cer=self.wer.use_cer,
            log_prediction=self.wer.log_prediction,
            dist_sync_on_step=True,
        )

        self.decoder.temperature = decoding_cfg.get('temperature', 1.0)

        # Update config
        with open_dict(self.cfg.decoding):
            self.cfg.decoding = decoding_cfg

        if verbose:
            logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}")

    @classmethod
    def list_available_models(cls) -> List[PretrainedModelInfo]:
        """
        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.
        """
        results = []

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_citrinet_256",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256/versions/1.0.0rc1/files/stt_en_citrinet_256.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_citrinet_512",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512/versions/1.0.0rc1/files/stt_en_citrinet_512.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_citrinet_1024",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024/versions/1.0.0rc1/files/stt_en_citrinet_1024.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_citrinet_256_gamma_0_25",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:\nemo:stt_en_citrinet_256_gamma_0_25",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_256_gamma_0_25.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_citrinet_512_gamma_0_25",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512_gamma_0_25",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_512_gamma_0_25.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_citrinet_1024_gamma_0_25",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024_gamma_0_25",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_1024_gamma_0_25.nemo",
        )

        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_es_citrinet_512",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_512",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_citrinet_512/versions/1.0.0/files/stt_es_citrinet_512.nemo",
        )

        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_de_citrinet_1024",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_citrinet_1024",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_citrinet_1024/versions/1.5.0/files/stt_de_citrinet_1024.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_fr_citrinet_1024_gamma_0_25",
            description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_citrinet_1024_gamma_0_25",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_citrinet_1024_gamma_0_25/versions/1.5/files/stt_fr_citrinet_1024_gamma_0_25.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_fr_no_hyphen_citrinet_1024_gamma_0_25",
            description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_citrinet_1024_gamma_0_25",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_citrinet_1024_gamma_0_25/versions/1.5/files/stt_fr_no_hyphen_citrinet_1024_gamma_0_25.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_es_citrinet_1024_gamma_0_25",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_1024_gamma_0_25",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_citrinet_1024_gamma_0_25/versions/1.8.0/files/stt_es_citrinet_1024_gamma_0_25.nemo",
        )

        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_small",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small/versions/1.6.0/files/stt_en_conformer_ctc_small.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_medium",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium/versions/1.6.0/files/stt_en_conformer_ctc_medium.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large/versions/1.10.0/files/stt_en_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_xlarge",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_xlarge",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_xlarge/versions/1.10.0/files/stt_en_conformer_ctc_xlarge.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_small_ls",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small_ls",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small_ls/versions/1.0.0/files/stt_en_conformer_ctc_small_ls.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_medium_ls",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium_ls",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium_ls/versions/1.0.0/files/stt_en_conformer_ctc_medium_ls.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_conformer_ctc_large_ls",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large_ls/versions/1.0.0/files/stt_en_conformer_ctc_large_ls.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_fr_conformer_ctc_large",
            description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_ctc_large/versions/1.5.1/files/stt_fr_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_fr_no_hyphen_conformer_ctc_large",
            description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_ctc_large/versions/1.5.1/files/stt_fr_no_hyphen_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_de_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_conformer_ctc_large/versions/1.5.0/files/stt_de_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_es_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_conformer_ctc_large/versions/1.8.0/files/stt_es_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_hi_conformer_ctc_medium",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hi_conformer_ctc_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hi_conformer_ctc_medium/versions/1.6.0/files/stt_hi_conformer_ctc_medium.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_mr_conformer_ctc_medium",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_mr_conformer_ctc_medium",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_mr_conformer_ctc_medium/versions/1.6.0/files/stt_mr_conformer_ctc_medium.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_enes_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_ctc_large/versions/1.0.0/files/stt_enes_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_ca_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_conformer_ctc_large/versions/1.11.0/files/stt_ca_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_rw_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_rw_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_rw_conformer_ctc_large/versions/1.11.0/files/stt_rw_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_enes_conformer_ctc_large_codesw",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_ctc_large_codesw",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_ctc_large_codesw/versions/1.0.0/files/stt_enes_conformer_ctc_large_codesw.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_be_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_be_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_be_conformer_ctc_large/versions/1.12.0/files/stt_be_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_hr_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hr_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hr_conformer_ctc_large/versions/1.11.0/files/stt_hr_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_it_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_conformer_ctc_large/versions/1.13.0/files/stt_it_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_ru_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_conformer_ctc_large/versions/1.13.0/files/stt_ru_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_eo_conformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_eo_conformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_eo_conformer_ctc_large/versions/1.14.0/files/stt_eo_conformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_fastconformer_ctc_large",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_large",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_large/versions/1.0.0/files/stt_en_fastconformer_ctc_large.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_fastconformer_ctc_large_ls",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_large_ls",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_large_ls/versions/1.0.0/files/stt_en_fastconformer_ctc_large_ls.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_fastconformer_ctc_xlarge",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_xlarge",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_xlarge/versions/1.20.0/files/stt_en_fastconformer_ctc_xlarge.nemo",
        )
        results.append(model)

        model = PretrainedModelInfo(
            pretrained_model_name="stt_en_fastconformer_ctc_xxlarge",
            description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_xxlarge",
            location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_xxlarge/versions/1.20.1/files/stt_en_fastconformer_ctc_xxlarge.nemo",
        )
        results.append(model)

        return results