# Copyright (c) 2025, 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 logging import warnings from functools import partial from itertools import repeat from pathlib import Path from typing import KeysView, Mapping, Sequence, Tuple, Union import omegaconf from lhotse import CutSet, Features, Recording from lhotse.array import Array, TemporalArray from lhotse.cut import Cut, MixedCut, PaddingCut from omegaconf import DictConfig, ListConfig, OmegaConf from nemo.collections.common.data.lhotse.nemo_adapters import ( LazyNeMoIterator, LazyNeMoTarredIterator, expand_sharded_filepaths, ) from nemo.collections.common.data.lhotse.text_adapters import ( AudioTurn, LhotseTextAdapter, LhotseTextPairAdapter, NeMoMultimodalConversation, NeMoMultimodalConversationJsonlAdapter, NeMoSFTJsonlAdapter, TextTurn, ) from nemo.collections.common.parts.preprocessing.manifest import get_full_path def read_cutset_from_config(config: Union[DictConfig, dict]) -> Tuple[CutSet, bool]: """ Reads NeMo configuration and creates a CutSet either from Lhotse or NeMo manifests. Returns a tuple of ``CutSet`` and a boolean indicating whether the data is tarred (True) or not (False). """ # First, check if the dataset is specified in the new configuration format and use it if possible. if not isinstance(config, DictConfig): config = DictConfig(config) if config.get("input_cfg") is not None: cuts, is_tarred = read_dataset_config(config) else: # Now, we'll figure out if we should read Lhotse manifest or NeMo manifest. use_nemo_manifest = all(config.get(opt) is None for opt in ("cuts_path", "shar_path")) if use_nemo_manifest: if config.get("manifest_filepath") is None: raise IncompleteConfigError("You must specify either: manifest_filepath, cuts_path, or shar_path") cuts, is_tarred = read_nemo_manifest(config) else: cuts, is_tarred = read_lhotse_manifest(config) return cuts, is_tarred class IncompleteConfigError(RuntimeError): """Placeholder for an error raised.""" pass KNOWN_DATA_CONFIG_TYPES = {} def get_known_config_data_types() -> KeysView[str]: """ Return the names of all registered data type parsers. Example: >>> get_known_config_data_types() ["nemo", "nemo_tarred", "lhotse", ...] """ return KNOWN_DATA_CONFIG_TYPES.keys() def get_parser_fn(data_type_name: str): """ Return the parsing function for a given data type name. Parsing function reads a dataloading config and returns a tuple of lhotse ``CutSet`` and boolean indicating whether we should use iterable dataset (True) or map dataset (False) mechanism ("is tarred"). """ return KNOWN_DATA_CONFIG_TYPES[data_type_name] def data_type_parser(name: Union[str, list[str]]): """ Decorator used to register data type parser functions. Parsing function reads a dataloading config and returns a tuple of lhotse ``CutSet`` and boolean indicating whether we should use iterable dataset (True) or map dataset (False) mechanism ("is tarred"). Example: >>> @data_type_parser("my_new_format") ... def my_new_format(config): ... return CutSet(read_my_format(**config)), True ... ... fn = get_parser_fn("my_new_format") ... cuts, is_tarred = fn({"my_arg_0": ..., "my_arg_1": ..., ...}) """ def _decorator(fn): global KNOWN_DATA_CONFIG_TYPES if isinstance(name, str): KNOWN_DATA_CONFIG_TYPES[name] = fn else: for n in name: KNOWN_DATA_CONFIG_TYPES[n] = fn return fn return _decorator def read_dataset_config(config) -> tuple[CutSet, bool]: """ Input configuration format examples. Example 1. Combine two datasets with equal weights and attach custom metadata in ``tags`` to each cut:: input_cfg: - type: nemo_tarred manifest_filepath: /path/to/manifest__OP_0..512_CL_.json tarred_audio_filepath: /path/to/tarred_audio/audio__OP_0..512_CL_.tar weight: 0.5 tags: lang: en some_metadata: some_value - type: nemo_tarred manifest_filepath: /path/to/manifest__OP_0..512_CL_.json tarred_audio_filepath: /path/to/tarred_audio/audio__OP_0..512_CL_.tar weight: 0.5 tags: lang: pl some_metadata: some_value Example 2. Combine multiple (4) datasets, with 2 corresponding to different tasks (ASR, AST). There are two levels of weights: per task (outer) and per dataset (inner). The final weight is the product of outer and inner weight:: input_cfg: - type: group weight: 0.7 tags: task: asr input_cfg: - type: nemo_tarred manifest_filepath: /path/to/asr1/manifest__OP_0..512_CL_.json tarred_audio_filepath: /path/to/tarred_audio/asr1/audio__OP_0..512_CL_.tar weight: 0.6 tags: lang: en some_metadata: some_value - type: nemo_tarred manifest_filepath: /path/to/asr2/manifest__OP_0..512_CL_.json tarred_audio_filepath: /path/to/asr2/tarred_audio/audio__OP_0..512_CL_.tar weight: 0.4 tags: lang: pl some_metadata: some_value - type: group weight: 0.3 tags: task: ast input_cfg: - type: nemo_tarred manifest_filepath: /path/to/ast1/manifest__OP_0..512_CL_.json tarred_audio_filepath: /path/to/ast1/tarred_audio/audio__OP_0..512_CL_.tar weight: 0.2 tags: src_lang: en tgt_lang: pl - type: nemo_tarred manifest_filepath: /path/to/ast2/manifest__OP_0..512_CL_.json tarred_audio_filepath: /path/to/ast2/tarred_audio/audio__OP_0..512_CL_.tar weight: 0.8 tags: src_lang: pl tgt_lang: en """ propagate_attrs = { "shuffle": config.get("shuffle", False), "shard_seed": config.get("shard_seed", "trng"), "text_field": config.get("text_field", "text"), "lang_field": config.get("lang_field", "lang"), "metadata_only": config.get("metadata_only", False), "force_finite": config.get("force_finite", False), "max_open_streams": config.get("max_open_streams", None), "token_equivalent_duration": config.get("token_equivalent_duration", None), "skip_missing_manifest_entries": config.get("skip_missing_manifest_entries", False), "force_map_dataset": config.get("force_map_dataset", False), "force_iterable_dataset": config.get("force_iterable_dataset", False), } input_cfg = config.input_cfg if isinstance(input_cfg, (str, Path)): # Resolve /path/to/input_cfg.yaml into config contents if needed. input_cfg = OmegaConf.load(input_cfg) cuts, is_tarred = parse_and_combine_datasets(input_cfg, propagate_attrs=propagate_attrs) return cuts, is_tarred def parse_group(grp_cfg: DictConfig, propagate_attrs: dict) -> [CutSet, bool]: """Parse a group configuration, potentially combining multiple datasets.""" assert grp_cfg.type in get_known_config_data_types(), f"Unknown item type in dataset config list: {grp_cfg.type=}" # Note: Text data types will return is_tarred=True. # We choose to treat text as-if it was tarred, which tends to be more # efficient as it moves the text file iteration into dataloading subprocess. if grp_cfg.type != "group": parser_fn = get_parser_fn(grp_cfg.type) cuts, is_tarred = parser_fn(grp_cfg) else: cuts, is_tarred = parse_and_combine_datasets( grp_cfg.input_cfg, propagate_attrs=propagate_attrs, ) # Attach extra tags to every utterance dynamically, if provided. if (extra_tags := grp_cfg.get("tags")) is not None: cuts = cuts.map(partial(attach_tags, tags=extra_tags), apply_fn=None) return cuts, is_tarred @data_type_parser("txt") def read_txt_paths(config: DictConfig) -> tuple[CutSet, bool]: """Read paths to text files and create a CutSet.""" cuts = CutSet( LhotseTextAdapter( paths=config.paths, language=config.language, shuffle_shards=config.shuffle, shard_seed=config.shard_seed, ) ) if not config.get("force_finite", False): cuts = cuts.repeat() return cuts, True @data_type_parser("txt_pair") def read_txt_pair_paths(config: DictConfig) -> tuple[CutSet, bool]: """Read paths to source and target text files and create a CutSet.""" cuts = CutSet( LhotseTextPairAdapter( source_paths=config.source_paths, target_paths=config.target_paths, source_language=config.get("source_language"), target_language=config.get("target_language"), questions_path=config.get("questions_path"), questions_language=config.get("questions_language"), shuffle_shards=config.shuffle, shard_seed=config.shard_seed, ) ) if not config.get("force_finite", False): cuts = cuts.repeat() return cuts, True @data_type_parser("nemo_sft_jsonl") def read_nemo_sft_jsonl(config: DictConfig) -> tuple[CutSet, bool]: """Read paths to Nemo SFT JSONL files and create a CutSet.""" cuts = CutSet( NeMoSFTJsonlAdapter( paths=config.paths, language=config.get("language"), shuffle_shards=config.shuffle, shard_seed=config.shard_seed, ) ) if not config.get("force_finite", False): cuts = cuts.repeat() return cuts, True @data_type_parser("multimodal_conversation") def read_multimodal_conversation_jsonl(config: DictConfig) -> tuple[CutSet, bool]: """Read paths to multimodal conversation JSONL files and create a CutSet.""" cuts = CutSet( NeMoMultimodalConversationJsonlAdapter( manifest_filepath=config.manifest_filepath, tarred_audio_filepaths=config.get("tarred_audio_filepaths"), audio_locator_tag=config.audio_locator_tag, token_equivalent_duration=config.get("token_equivalent_duration"), shuffle_shards=config.shuffle, shard_seed=config.shard_seed, ) ) if not config.get("force_finite", False): cuts = cuts.repeat() return cuts, True def attach_tags(cut, tags: dict): """Attach extra tags to a cut dynamically.""" for key, val in tags.items(): setattr(cut, key, val) return cut @data_type_parser("group") def parse_and_combine_datasets( config_list: Union[list[DictConfig], ListConfig], propagate_attrs: dict ) -> tuple[CutSet, bool]: """Parse a list of dataset configurations, potentially combining multiple datasets.""" cuts = [] weights = [] tarred_status = [] assert len(config_list) > 0, "Empty group in dataset config list." for item in config_list: # Check if we have any attributes that are propagated downwards to each item in the group. # If a key already exists in the item, it takes precedence (we will not overwrite); # otherwise we will assign it. # We also update propagate_atts for the next sub-groups based on what's present in this group next_propagate_attrs = propagate_attrs.copy() for k, v in propagate_attrs.items(): if k not in item: item[k] = v else: next_propagate_attrs[k] = item[k] # Load the item (which may also be another group) as a CutSet. item_cuts, item_is_tarred = parse_group(item, next_propagate_attrs) cuts.append(item_cuts) tarred_status.append(item_is_tarred) if (w := item.get("weight")) is not None: weights.append(w) all_same_tarred_status = all(t == tarred_status[0] for t in tarred_status) if not all_same_tarred_status: if propagate_attrs["force_map_dataset"] or propagate_attrs["force_iterable_dataset"]: logging.warning( f"Not all datasets in the group have the same tarred status, using provided force_map_dataset " f"({propagate_attrs['force_map_dataset']}) and force_iterable_dataset " f"({propagate_attrs['force_iterable_dataset']}) to determine the final tarred status." ) else: raise ValueError( "Mixing tarred and non-tarred datasets is not supported when neither force_map_dataset " "nor force_iterable_dataset is True." ) assert len(weights) == 0 or len(cuts) == len( weights ), "Missing dataset weight. When weighting datasets, every dataset must have a specified weight." if len(cuts) > 1: cuts = mux( *cuts, weights=weights if weights else None, max_open_streams=propagate_attrs["max_open_streams"], seed=propagate_attrs["shard_seed"], force_finite=propagate_attrs["force_finite"] or propagate_attrs["metadata_only"], ) else: (cuts,) = cuts return cuts, tarred_status[0] @data_type_parser(["lhotse", "lhotse_shar"]) def read_lhotse_manifest(config) -> tuple[CutSet, bool]: """Read paths to Lhotse manifest files and create a CutSet.""" is_tarred = config.get("shar_path") is not None if is_tarred: # Lhotse Shar is the equivalent of NeMo's native "tarred" dataset. # The combination of shuffle_shards, and repeat causes this to # be an infinite manifest that is internally reshuffled on each epoch. # The parameter ``config.shard_seed`` is used to determine shard shuffling order. Options: # - "trng" means we'll defer setting the seed until the iteration # is triggered, and we'll use system TRNG to get a completely random seed for each worker. # This results in every dataloading worker using full data but in a completely different order. # - "randomized" means we'll defer setting the seed until the iteration # is triggered, and we'll use config.seed to get a pseudo-random seed for each worker. # This results in every dataloading worker using full data but in a completely different order. # Unlike "trng", this is deterministic, and if you resume training, you should change the seed # to observe different data examples than in the previous run. # - integer means we'll set a specific seed in every worker, and data would be duplicated across them. # This is mostly useful for unit testing or debugging. shard_seed = config.get("shard_seed", "trng") metadata_only = config.get("metadata_only", False) force_finite = config.get("force_finite", False) if config.get("cuts_path") is not None: warnings.warn("Note: lhotse.cuts_path will be ignored because lhotse.shar_path was provided.") if isinstance(config.shar_path, (str, Path)): logging.info(f"Initializing Lhotse Shar CutSet (tarred) from a single data source: '{config.shar_path}'") cuts = CutSet.from_shar( **_resolve_shar_inputs(config.shar_path, metadata_only), shuffle_shards=True, seed=shard_seed ) if not metadata_only and not force_finite: cuts = cuts.repeat() elif isinstance(config.shar_path, Sequence): # Multiple datasets in Lhotse Shar format: we will dynamically multiplex them # with probability approximately proportional to their size logging.info( "Initializing Lhotse Shar CutSet (tarred) from multiple data sources with a weighted multiplexer. " "We found the following sources and weights: " ) cutsets = [] weights = [] for item in config.shar_path: if isinstance(item, (str, Path)): path = item cs = CutSet.from_shar( **_resolve_shar_inputs(path, metadata_only), shuffle_shards=True, seed=shard_seed ) weight = len(cs) else: assert isinstance(item, Sequence) and len(item) == 2 and isinstance(item[1], (int, float)), ( "Supported inputs types for config.shar_path are: " "str | list[str] | list[tuple[str, number]] " "where str is a path and number is a mixing weight (it may exceed 1.0). " f"We got: '{item}'" ) path, weight = item cs = CutSet.from_shar( **_resolve_shar_inputs(path, metadata_only), shuffle_shards=True, seed=shard_seed ) logging.info(f"- {path=} {weight=}") cutsets.append(cs) weights.append(weight) cuts = mux( *cutsets, weights=weights, max_open_streams=config.get("max_open_streams", None), seed=shard_seed, force_finite=force_finite, ) elif isinstance(config.shar_path, Mapping): fields = {k: expand_sharded_filepaths(v) for k, v in config.shar_path.items()} assert "cuts" in config.shar_path.keys(), ( f"Invalid value for key 'shar_path': a dict was provided, but didn't specify key 'cuts' pointing " f"to the manifests. We got the following: {config.shar_path=}" ) if metadata_only: fields = {"cuts": fields["cuts"]} cuts = CutSet.from_shar(fields=fields, shuffle_shards=True, seed=shard_seed) if not metadata_only and not force_finite: cuts = cuts.repeat() else: raise RuntimeError( f"Unexpected value for key 'shar_path'. We support string, list of strings, " f"list of tuples[string,float], and dict[string,list[string]], " f"but got: {type(config.shar_path)=} {config.shar_path=}" ) else: # Regular Lhotse manifest points to individual audio files (like native NeMo manifest). path = config.cuts_path cuts = CutSet.from_file(path).map(partial(resolve_relative_paths, manifest_path=path)) return cuts, is_tarred @data_type_parser(["lhotse_as_conversation"]) def read_lhotse_as_conversation(config) -> tuple[CutSet, bool]: def cut_to_conversation(cut: Cut) -> NeMoMultimodalConversation: turns = [ AudioTurn(cut=cut, role="user", audio_locator_tag=config.audio_locator_tag), TextTurn(value=cut.supervisions[0].text, role="assistant"), ] if hasattr(cut, "context"): turns = [TextTurn(value=cut.context, role="user")] + turns return NeMoMultimodalConversation( id=cut.id, turns=turns, token_equivalent_duration=config.token_equivalent_duration, custom=cut.custom, ) cuts, is_tarred = read_cutset_from_config(config) cuts = cuts.map(cut_to_conversation) return cuts, is_tarred def _resolve_shar_inputs(path: Union[str, Path], only_metadata: bool) -> dict: if only_metadata: return dict(fields={"cuts": sorted(Path(path).glob("cuts.*"))}) else: return dict(in_dir=path) def resolve_relative_paths(cut: Cut, manifest_path: str) -> Cut: """Resolve relative paths in a Cut object to their full paths.""" if isinstance(cut, PaddingCut): return cut if isinstance(cut, MixedCut): for track in cut.tracks: track.cut = resolve_relative_paths(track.cut, manifest_path) return cut def resolve_recording(value): for audio_source in value.sources: if audio_source.type == "file": audio_source.source = get_full_path(audio_source.source, manifest_file=manifest_path) def resolve_array(value): if isinstance(value, TemporalArray): value.array = resolve_array(value.array) else: if value.storage_type in ("numpy_files", "lilcom_files"): abspath = Path( get_full_path(str(Path(value.storage_path) / value.storage_key), manifest_file=manifest_path) ) value.storage_path = str(abspath.parent) value.storage_key = str(abspath.name) elif value.storage_type in ( "kaldiio", "chunked_lilcom_hdf5", "lilcom_chunky", "lilcom_hdf5", "numpy_hdf5", ): value.storage_path = get_full_path(value.storage_path, manifest_file=manifest_path) # ignore others i.e. url, in-memory data, etc. if cut.has_recording: resolve_recording(cut.recording) if cut.has_features: resolve_array(cut.features) if cut.custom is not None: for key, value in cut.custom.items(): if isinstance(value, Recording): resolve_recording(value) elif isinstance(value, (Array, TemporalArray, Features)): resolve_array(value) return cut @data_type_parser(["nemo", "nemo_tarred"]) def read_nemo_manifest(config) -> tuple[CutSet, bool]: """Read NeMo manifest and return a Lhotse CutSet.""" common_kwargs = {} for key in ("text_field", "lang_field", "shuffle", "shard_seed", "extra_fields"): if key in config: if key == "shuffle": common_kwargs["shuffle_shards"] = config[key] else: common_kwargs[key] = config[key] # The option below is to allow a special case of NeMo manifest iteration as Lhotse CutSet # without performing any I/O. NeMo manifests typically don't have sampling_rate information required by Lhotse, # so lhotse has to look up the headers of audio files to fill it on-the-fly. # (this only has an impact on non-tarred data; tarred data is read into memory anyway). # This is useful for utility scripts that iterate metadata and estimate optimal batching settings # and other data statistics. metadata_only = config.get("metadata_only", False) force_finite = config.get("force_finite", False) notar_kwargs = {"metadata_only": metadata_only} is_tarred = config.get("tarred_audio_filepaths") is not None if isinstance(config.manifest_filepath, (str, Path)): logging.info( f"""Initializing Lhotse CutSet from a single NeMo manifest (is_tarred={is_tarred}): '{config.manifest_filepath}'""" ) if is_tarred and not metadata_only: cuts = CutSet( LazyNeMoTarredIterator( config.manifest_filepath, tar_paths=config.tarred_audio_filepaths, skip_missing_manifest_entries=config.get("skip_missing_manifest_entries", False), **common_kwargs, ) ) if not force_finite: cuts = cuts.repeat() else: cuts = CutSet(LazyNeMoIterator(config.manifest_filepath, **notar_kwargs, **common_kwargs)) else: # Format option 1: # Assume it's [[path1], [path2], ...] (same for tarred_audio_filepaths). # This is the format for multiple NeMo buckets. # Note: we set "weights" here to be proportional to the number of utterances in each data source. # this ensures that we distribute the data from each source uniformly throughout each epoch. # Setting equal weights would exhaust the shorter data sources closer the towards the beginning # of an epoch (or over-sample it in the case of infinite CutSet iteration with .repeat()). # Format option 2: # Assume it's [[path1, weight1], [path2, weight2], ...] (while tarred_audio_filepaths remain unchanged). # Note: this option allows to manually set the weights for multiple datasets. # Format option 3: # i.e., NeMo concatenated dataset # Assume it's [path1, path2, ...] (while tarred_audio_filepaths in the same format). logging.info( f"""Initializing Lhotse CutSet from multiple NeMo manifest (is_tarred={is_tarred}) sources with a weighted multiplexer. We found the following sources and weights: """ ) cutsets = [] weights = [] tar_paths = config.tarred_audio_filepaths if is_tarred else repeat((None,)) # Create a stream for each dataset. for manifest_info, tar_path in zip(config.manifest_filepath, tar_paths): if is_tarred and isinstance(tar_path, (list, tuple, ListConfig)): # if it's in option 1 or 2 (tar_path,) = tar_path manifest_path = manifest_info[0] else: # if it's in option 3 manifest_path = manifest_info # First, convert manifest_path[+tar_path] to an iterator. if is_tarred and not metadata_only: nemo_iter = LazyNeMoTarredIterator( manifest_path=manifest_path, tar_paths=tar_path, skip_missing_manifest_entries=config.get("skip_missing_manifest_entries", False), **common_kwargs, ) else: nemo_iter = LazyNeMoIterator(manifest_path, **notar_kwargs, **common_kwargs) # Then, determine the weight or use one provided if isinstance(manifest_info, str) or len(manifest_info) == 1: weight = len(nemo_iter) else: assert ( isinstance(manifest_info, Sequence) and len(manifest_info) == 2 and isinstance(manifest_info[1], (int, float)) ), ( "Supported inputs types for config.manifest_filepath are: " "str | list[list[str]] | list[tuple[str, number]] " "where str is a path and number is a mixing weight (it may exceed 1.0). " f"We got: '{manifest_info}'" ) weight = manifest_info[1] logging.info(f"- {manifest_path=} {weight=}") # [optional] When we have a limit on the number of open streams, # split the manifest to individual shards if applicable. # This helps the multiplexing achieve closer data distribution # to the one desired in spite of the limit. if config.get("max_open_streams") is not None: for subiter in nemo_iter.to_shards(): cutsets.append(CutSet(subiter)) weights.append(weight) else: cutsets.append(CutSet(nemo_iter)) weights.append(weight) cuts = mux( *cutsets, weights=weights, max_open_streams=config.get("max_open_streams"), seed=config.get("shard_seed", "trng"), force_finite=force_finite or metadata_only, ) return cuts, is_tarred def mux( *cutsets: CutSet, weights: list[Union[int, float]], max_open_streams: Union[int, None] = None, seed: Union[str, int] = "trng", force_finite: bool = False, ) -> CutSet: """ Helper function to call the right multiplexing method flavour in lhotse. The result is always an infinitely iterable ``CutSet``, but depending on whether ``max_open_streams`` is set, it will select a more appropriate multiplexing strategy. """ if max_open_streams is not None: assert not force_finite, "max_open_streams and metadata_only/force_finite options are not compatible" cuts = CutSet.infinite_mux(*cutsets, weights=weights, seed=seed, max_open_streams=max_open_streams) else: if not force_finite: cutsets = [cs.repeat() for cs in cutsets] if len(cutsets) == 1: # CutSet.mux must take more than one CutSet. cuts = cutsets[0] else: cuts = CutSet.mux(*cutsets, weights=weights, seed=seed) return cuts def guess_parse_cutset(inp: Union[str, dict, omegaconf.DictConfig]) -> CutSet: """ Utility function that supports opening a CutSet from: * a string path to YAML input spec (see :func:`read_dataset_config` for details) * a string path to Lhotse non-tarred JSONL manifest * a string path to NeMo non-tarred JSON manifest * a dictionary specifying inputs with keys available in :class:`nemo.collections.common.data.lhotse.dataloader.LhotseDataLoadingConfig` It's intended to be used in a generic context where we are not sure which way the user will specify the inputs. """ from nemo.collections.common.data.lhotse.dataloader import make_structured_with_schema_warnings if isinstance(inp, (dict, omegaconf.DictConfig)): try: config = make_structured_with_schema_warnings(OmegaConf.from_dotlist([f"{k}={v}" for k, v in inp.items()])) cuts, _ = read_cutset_from_config(config) return cuts except Exception as e: raise RuntimeError( f"Couldn't open CutSet based on dict input {inp} (is it compatible with LhotseDataLoadingConfig?)" ) from e elif isinstance(inp, str): if inp.endswith(".yaml"): # Path to YAML file with the input configuration config = make_structured_with_schema_warnings(OmegaConf.from_dotlist([f"input_cfg={inp}"])) elif inp.endswith(".jsonl") or inp.endswith(".jsonl.gz"): # Path to a Lhotse non-tarred manifest config = make_structured_with_schema_warnings(OmegaConf.from_dotlist([f"cuts_path={inp}"])) else: # Assume anything else is a NeMo non-tarred manifest config = make_structured_with_schema_warnings(OmegaConf.from_dotlist([f"manifest_filepath={inp}"])) cuts, _ = read_cutset_from_config(config) return cuts else: raise RuntimeError(f'Unsupported input type: {type(inp)} (expected a dict or a string)')