Repository Analysis

NVIDIA-NeMo/NeMo

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

23.7 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of NVIDIA-NeMo/NeMo, a Python project with 17,770 GitHub stars. SynthScan v2.0 examined 475,067 lines of code across 1900 source files, recording 8067 pattern matches distributed across 23 syntactic categories. The overall adjusted score of 23.7 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

23.7
Adjusted Score
23.7
Raw Score
100%
Time Factor
2026-07-14
Last Push
17.8K
Stars
Python
Language
475.1K
Lines of Code
1.9K
Files
8.1K
Pattern Hits
2026-07-14
Scan Date
0.28
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 14HIGH 526MEDIUM 410LOW 7117

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 8067 distinct pattern matches across 23 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers2999 hits · 3016 pts
SeverityFileLineSnippetContext
LOWtools/nemo_forced_aligner/align_eou.py248def get_manifests_for_this_rank(manifest_list, num_nodes, num_gpus, node_idx, gpu_idx):CODE
LOW…orced_aligner/tests/test_add_t_start_end_to_utt_obj.py260def test_add_t_start_end_to_utt_obj(alignment, expected_output_utterance, output_timestep_duration):CODE
LOWtools/nemo_forced_aligner/utils/make_ass_files.py335def make_token_level_ass_file(utt_obj, output_dir_root, ass_file_config, audio_dur):CODE
LOWtools/ctc_segmentation/scripts/utils.py135def _prepare_tokenized_text_for_bpe_model(text: List[str], tokenizer, vocabulary: List[str], blank_idx: int = 0):CODE
LOWtools/ctc_segmentation/scripts/utils.py213def determine_utterance_segments(config, utt_begin_indices, char_probs, timings, text, char_list):CODE
LOWtools/ctc_segmentation/scripts/utils.py303def write_labels_for_audacity(CODE
LOWtools/speech_data_explorer/data_explorer.py54def _ensure_numba_coverage_compatibility():CODE
LOWtools/speech_data_explorer/data_explorer.py293def expand_sharded_path_without_braceexpand(path_pattern):CODE
LOWtools/speech_data_explorer/data_explorer.py627def build_tar_index_from_local(tar_path):CODE
LOW…taset_preparation/customization_dataset_preparation.py85def recommend_hyperparameters_human_readable(recommended_hyperparameters):CODE
LOW…taset_preparation/customization_dataset_preparation.py92def recommend_hyperparameters(df, model=None):CODE
LOW…taset_preparation/customization_dataset_preparation.py148def estimating_customization_job_time(df, recommended_hyperparameters):CODE
LOW…taset_preparation/customization_dataset_preparation.py165def warn_completion_is_not_empty(df):CODE
LOW…taset_preparation/customization_dataset_preparation.py181def warn_imbalanced_completion(df):CODE
LOW…taset_preparation/customization_dataset_preparation.py304def convert_into_prompt_completion_only(df, prompt_template="{prompt}", completion_template="{completion}"):CODE
LOW…taset_preparation/customization_dataset_preparation.py311def warn_and_drop_long_samples(df, max_total_char_length):CODE
LOW…taset_preparation/customization_dataset_preparation.py363def split_into_train_validation(df, val_proportion=0.1):CODE
LOW…ration/tests/test_customization_dataset_preparation.py39def test_recommend_hyperparameters():CODE
LOW…ration/tests/test_customization_dataset_preparation.py83def test_warn_completion_is_not_empty():CODE
LOW…ration/tests/test_customization_dataset_preparation.py106def test_warn_imbalanced_completion():CODE
LOW…ration/tests/test_customization_dataset_preparation.py206def test_warn_duplicated_rows():CODE
LOW…ration/tests/test_customization_dataset_preparation.py223def test_drop_duplicated_rows():CODE
LOW…ration/tests/test_customization_dataset_preparation.py262def test_drop_unrequired_fields():CODE
LOW…ration/tests/test_customization_dataset_preparation.py271def test_convert_into_template():CODE
LOW…ration/tests/test_customization_dataset_preparation.py295def test_convert_into_prompt_completion_only():CODE
LOW…ration/tests/test_customization_dataset_preparation.py313def get_indexes_of_long_examples(df, max_total_char_length):CODE
LOW…ration/tests/test_customization_dataset_preparation.py318def test_warn_and_drop_long_samples():CODE
LOW…ration/tests/test_customization_dataset_preparation.py346def test_show_first_example_in_df():CODE
LOW…ration/tests/test_customization_dataset_preparation.py356def test_get_prepared_filename():CODE
LOW…ration/tests/test_customization_dataset_preparation.py374def test_split_into_train_validation():CODE
LOWnemo/lightning/base_callback.py78 def on_save_checkpoint_success(self, *args, **kwargs) -> None:CODE
LOWnemo/lightning/__init__.py24def _is_slurm_interactive_mode():CODE
LOWnemo/lightning/callback_group.py141def hook_class_init_with_callbacks(cls, start_callback: str, end_callback: str) -> None:CODE
LOWnemo/lightning/one_logger_callback.py37def get_one_logger_init_config() -> Dict[str, Any]:CODE
LOWnemo/lightning/one_logger_callback.py67def _get_base_callback_config(CODE
LOWnemo/lightning/one_logger_callback.py205def _should_enable_for_current_rank() -> bool:CODE
LOWnemo/core/connectors/save_restore_connector.py602 def _inject_model_parallel_rank_for_ckpt(self, dirname, basename):CODE
LOWnemo/core/connectors/save_restore_connector.py608 def _make_nemo_file_from_folder(filename, source_dir):CODE
LOWnemo/core/connectors/save_restore_connector.py618 def _make_nemo_file_from_folder_with_multistorageclient(filename, source_dir):CODE
LOWnemo/core/connectors/save_restore_connector.py98 def load_config_and_state_dict(CODE
LOWnemo/core/connectors/save_restore_connector.py224 def load_instance_with_state_dict(self, instance, state_dict, strict):CODE
LOWnemo/core/connectors/save_restore_connector.py512 def check_artifact_and_query_basename_match(query_path: str) -> bool:CODE
LOWnemo/core/connectors/save_restore_connector.py706 def _unpack_nemo_file_with_multistorageclient(CODE
LOWnemo/core/connectors/save_restore_connector.py748 def _load_state_dict_from_disk(model_weights, map_location='cpu'):CODE
LOWnemo/core/config/optimizers.py237def register_optimizer_params(name: str, optimizer_params: OptimizerParams):CODE
LOWnemo/core/config/schedulers.py234def register_scheduler_params(name: str, scheduler_params: SchedulerParams):CODE
LOWnemo/core/classes/exportable.py295 def disabled_deployment_input_names(self) -> List[str]:CODE
LOWnemo/core/classes/exportable.py300 def disabled_deployment_output_names(self) -> List[str]:CODE
LOWnemo/core/classes/exportable.py339 def dynamic_shapes_for_export(self, use_dynamo=False):CODE
LOWnemo/core/classes/common.py306def _get_allowed_target_class(target_path: str):CODE
LOWnemo/core/classes/common.py312def _validate_config_targets_recursive(config_node: Any):CODE
LOWnemo/core/classes/common.py342def is_semantic_typecheck_enabled():CODE
LOWnemo/core/classes/common.py539 def _attach_and_validate_output_types(self, out_objects, ignore_collections=False, output_types=None):CODE
LOWnemo/core/classes/common.py834 def _inspect_signature_for_trainer(cls, check_cls):CODE
LOWnemo/core/classes/common.py975 def get_available_model_names(cls) -> List[str]:CODE
LOWnemo/core/classes/common.py1055 def _get_ngc_pretrained_model_info(cls, model_name: str, refresh_cache: bool = False) -> Tuple[type, str]:CODE
LOWnemo/core/classes/common.py1114 def _get_hf_hub_pretrained_model_info(cls, model_name: str, refresh_cache: bool = False) -> Tuple[type, str]:CODE
LOWnemo/core/classes/common.py1397 def set_semantic_check_enabled(enabled: bool = True):CODE
LOWnemo/core/classes/modelPT.py298 def has_native_or_submodules_artifacts(self) -> bool:CODE
LOWnemo/core/classes/modelPT.py577 def setup_multiple_validation_data(self, val_data_config: Union[DictConfig, Dict]):CODE
2939 more matches not shown…
Cross-File Repetition444 hits · 2220 pts
SeverityFileLineSnippetContext
HIGHnemo/core/config/optimizers.py0convenience method to obtain an optimizer class and partially instantiate it with optimizer kwargs. args: name: name of STRING
HIGHnemo/core/config/schedulers.py0convenience method to obtain an optimizer class and partially instantiate it with optimizer kwargs. args: name: name of STRING
HIGHnemo/core/optim/lr_scheduler.py0convenience method to obtain an optimizer class and partially instantiate it with optimizer kwargs. args: name: name of STRING
HIGHnemo/core/optim/optimizers.py0convenience method to obtain an optimizer class and partially instantiate it with optimizer kwargs. args: name: name of STRING
HIGHnemo/core/classes/exportable.py0implement this method to return a set of output names disabled for exportSTRING
HIGHnemo/collections/asr/modules/conv_asr.py0implement this method to return a set of output names disabled for exportSTRING
HIGHnemo/collections/asr/modules/rnnt.py0implement this method to return a set of output names disabled for exportSTRING
HIGHnemo/collections/tts/models/fastpitch.py0implement this method to return a set of output names disabled for exportSTRING
HIGHnemo/core/classes/mixins/adapter_mixins.py0add an adapter module to this model. args: name: a globally unique name for the adapter. will be used to access, enable STRING
HIGHnemo/collections/asr/parts/mixins/asr_adapter_mixins.py0add an adapter module to this model. args: name: a globally unique name for the adapter. will be used to access, enable STRING
HIGH…llections/tts/parts/mixins/fastpitch_adapter_mixins.py0add an adapter module to this model. args: name: a globally unique name for the adapter. will be used to access, enable STRING
HIGHnemo/core/classes/mixins/adapter_mixins.py0checks if any adapter module has been instantiated. returns: bool, determining if any adapter module has been instantiatSTRING
HIGHnemo/collections/asr/parts/mixins/asr_adapter_mixins.py0checks if any adapter module has been instantiated. returns: bool, determining if any adapter module has been instantiatSTRING
HIGH…llections/tts/parts/mixins/fastpitch_adapter_mixins.py0checks if any adapter module has been instantiated. returns: bool, determining if any adapter module has been instantiatSTRING
HIGHnemo/core/classes/mixins/adapter_mixins.py0updated the internal adapter config, determining if an adapter (or all adapters) are either enabled or disabled. a commoSTRING
HIGHnemo/collections/asr/parts/mixins/asr_adapter_mixins.py0updated the internal adapter config, determining if an adapter (or all adapters) are either enabled or disabled. a commoSTRING
HIGH…llections/tts/parts/mixins/fastpitch_adapter_mixins.py0updated the internal adapter config, determining if an adapter (or all adapters) are either enabled or disabled. a commoSTRING
HIGHnemo/core/classes/mixins/adapter_mixins.py0utility method to resolve a given global/module adapter name to its components. always returns a tuple representing (modSTRING
HIGHnemo/collections/asr/parts/mixins/asr_adapter_mixins.py0utility method to resolve a given global/module adapter name to its components. always returns a tuple representing (modSTRING
HIGH…llections/tts/parts/mixins/fastpitch_adapter_mixins.py0utility method to resolve a given global/module adapter name to its components. always returns a tuple representing (modSTRING
HIGHnemo/core/classes/mixins/adapter_mixins.py0fastpitch adapter mixin that can augment any encoder module with adapter module support. this mixin class should be usedSTRING
HIGHnemo/collections/asr/parts/mixins/asr_adapter_mixins.py0fastpitch adapter mixin that can augment any encoder module with adapter module support. this mixin class should be usedSTRING
HIGH…llections/tts/parts/mixins/fastpitch_adapter_mixins.py0fastpitch adapter mixin that can augment any encoder module with adapter module support. this mixin class should be usedSTRING
HIGH…ts/voice_agent/pipecat/services/nemo/streaming_diar.py0configuration parameters for diarization inference.STRING
HIGH…asks/diarization/neural_diarizer/e2e_diarize_speech.py0configuration parameters for diarization inference.STRING
HIGHnemo/collections/asr/parts/mixins/diarization.py0configuration parameters for diarization inference.STRING
HIGHnemo/agents/voice_agent/pipecat/services/nemo/utils.py0update the buffer with the new frame args: frame (frame): frame to update the buffer withSTRING
HIGH…ence/streaming/buffering/incremental_audio_bufferer.py0update the buffer with the new frame args: frame (frame): frame to update the buffer withSTRING
HIGH…ns/asr/inference/streaming/buffering/audio_bufferer.py0update the buffer with the new frame args: frame (frame): frame to update the buffer withSTRING
HIGHnemo/collections/speechlm2/models/salm_asr_decoder.py0returns the audio duration corresponding to a single frame/token at the output of ``self.perception``.STRING
HIGHnemo/collections/speechlm2/models/salm_automodel.py0returns the audio duration corresponding to a single frame/token at the output of ``self.perception``.STRING
HIGHnemo/collections/speechlm2/models/salm.py0returns the audio duration corresponding to a single frame/token at the output of ``self.perception``.STRING
HIGHnemo/collections/speechlm2/models/salm_asr_decoder.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/speechlm2/models/salm_automodel.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/speechlm2/models/duplex_ear_tts.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/speechlm2/models/salm.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/speechlm2/models/duplex_s2s_model.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGH…ns/speechlm2/models/duplex_s2s_speech_decoder_model.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/asr/models/ssl_models.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/asr/models/asr_eou_models.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/asr/models/aed_multitask_models.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/asr/models/asr_model.py0return a typing schema for optimal batch size calibration for various sequence lengths using oomptimizer.STRING
HIGHnemo/collections/speechlm2/models/duplex_ear_tts.py0return the size of the audio codec codebook including extra speech bos and eos tokens.STRING
HIGHnemo/collections/speechlm2/models/duplex_s2s_model.py0return the size of the audio codec codebook including extra speech bos and eos tokens.STRING
HIGH…ns/speechlm2/models/duplex_s2s_speech_decoder_model.py0return the size of the audio codec codebook including extra speech bos and eos tokens.STRING
HIGHnemo/collections/speechlm2/models/duplex_ear_tts.py0indicates start of utterance generation (not start of inference!).STRING
HIGHnemo/collections/speechlm2/models/duplex_s2s_model.py0indicates start of utterance generation (not start of inference!).STRING
HIGH…ns/speechlm2/models/duplex_s2s_speech_decoder_model.py0indicates start of utterance generation (not start of inference!).STRING
HIGHnemo/collections/speechlm2/models/duplex_ear_tts.py0indicates start of inference (the very first frame).STRING
HIGHnemo/collections/speechlm2/models/duplex_s2s_model.py0indicates start of inference (the very first frame).STRING
HIGH…ns/speechlm2/models/duplex_s2s_speech_decoder_model.py0indicates start of inference (the very first frame).STRING
HIGHnemo/collections/speechlm2/models/duplex_ear_tts.py0text pad id is used as a 'blank' for frames when the model is not speaking and for frames where the model is speaking buSTRING
HIGHnemo/collections/speechlm2/models/duplex_s2s_model.py0text pad id is used as a 'blank' for frames when the model is not speaking and for frames where the model is speaking buSTRING
HIGH…ns/speechlm2/models/duplex_s2s_speech_decoder_model.py0text pad id is used as a 'blank' for frames when the model is not speaking and for frames where the model is speaking buSTRING
HIGHnemo/collections/speechlm2/parts/metrics/asr_cer_wer.py0computes the final score and deallocates asr and partial results.STRING
HIGHnemo/collections/speechlm2/parts/metrics/asr_bleu.py0computes the final score and deallocates asr and partial results.STRING
HIGHnemo/collections/speechlm2/parts/metrics/secs.py0computes the final score and deallocates asr and partial results.STRING
HIGHnemo/collections/speechlm2/data/s2s_dataset.py0strips timestamp tokens from text, e.g. turns: '<|0|> hey <|3|> <|3|> how <|5|> <|7|> are <|8|> <|8|> <|10|> you? <|12|>STRING
HIGH…o/collections/speechlm2/data/duplex_ear_tts_dataset.py0strips timestamp tokens from text, e.g. turns: '<|0|> hey <|3|> <|3|> how <|5|> <|7|> are <|8|> <|8|> <|10|> you? <|12|>STRING
HIGHnemo/collections/common/data/lhotse/cutset.py0strips timestamp tokens from text, e.g. turns: '<|0|> hey <|3|> <|3|> how <|5|> <|7|> are <|8|> <|8|> <|10|> you? <|12|>STRING
384 more matches not shown…
Over-Commented Block1693 hits · 1671 pts
SeverityFileLineSnippetContext
LOWnemo_dependencies.py1#!/usr/bin/env python3COMMENT
LOW.pre-commit-config.yaml1# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWpyproject.toml1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW.readthedocs.yml1# =============================================================================COMMENT
LOWsetup.py1# ! /usr/bin/pythonCOMMENT
LOWtools/nemo_forced_aligner/align.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/align_eou.py1# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW…s/nemo_forced_aligner/tests/test_restore_token_case.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/tests/test_get_utt_obj.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW…orced_aligner/tests/test_add_t_start_end_to_utt_obj.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/utils/make_output_manifest.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/utils/constants.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/utils/data_prep.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/utils/make_ass_files.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/nemo_forced_aligner/utils/make_ctm_files.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/speech_data_simulator/multispeaker_simulator.py1# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/ctc_segmentation/run_segmentation.sh1#!/bin/bashCOMMENT
LOWtools/ctc_segmentation/run_filter.sh1#!/bin/bashCOMMENT
LOWtools/ctc_segmentation/scripts/prepare_data.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW…ols/ctc_segmentation/scripts/get_metrics_and_filter.py1# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/ctc_segmentation/scripts/normalization_helpers.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/ctc_segmentation/scripts/verify_segments.py1# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/ctc_segmentation/scripts/cut_audio.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/ctc_segmentation/scripts/utils.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/ctc_segmentation/scripts/run_ctc_segmentation.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/speech_data_explorer/data_explorer.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/customization_dataset_preparation/__init__.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW…taset_preparation/customization_dataset_preparation.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW…ration/tests/test_customization_dataset_preparation.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOW…ls/customization_dataset_preparation/tests/__init__.py1# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/rir_corpus_generator/rir_mix_generator.py1# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/rir_corpus_generator/rir_corpus_generator.py1# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/asr_evaluator/asr_evaluator.py1# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWtools/asr_evaluator/utils.py1# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/package_info.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/constants.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/__init__.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/lightning/base_callback.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/lightning/__init__.py1# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/lightning/callback_group.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/lightning/one_logger_callback.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/lightning/base.py1# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/__init__.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/connectors/save_restore_connector.py1# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/connectors/__init__.py1# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/neural_types/elements.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/neural_types/neural_type.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/neural_types/__init__.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/neural_types/comparison.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/neural_types/axes.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/base_config.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/__init__.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/optimizers.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/pytorch_lightning.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/pytorch.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/modelPT.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/hydra_runner.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/schedulers.py1# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/templates/__init__.py1# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.COMMENT
LOWnemo/core/config/templates/model_card.py1# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.COMMENT
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Decorative Section Separators228 hits · 825 pts
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MEDIUM.readthedocs.yml1# =============================================================================COMMENT
MEDIUM.readthedocs.yml15# =============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/vllm/salm/backends.py48# ── Base backend ────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/speechlm2/vllm/salm/backends.py79# ── Transformer backend (Qwen3, etc.) ────────────────────────────────COMMENT
MEDIUMnemo/collections/speechlm2/vllm/salm/backends.py216# ── Hybrid backend (NemotronH / Mamba+MoE) ──────────────────────────COMMENT
MEDIUMnemo/collections/speechlm2/vllm/salm/backends.py304# ── Factory ─────────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/speechlm2/vllm/salm/audio.py81# ── Helpers ─────────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/speechlm2/vllm/salm/audio.py187# ── Multimodal contract types ───────────────────────────────────────COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py38# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py40# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py104# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py106# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py436# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py438# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py34# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py36# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py95# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py97# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py237# ==============================================================================COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py239# ==============================================================================COMMENT
MEDIUMnemo/collections/common/data/lhotse/broadcasting.py55# ---------------------------------------------------------------------------COMMENT
MEDIUMnemo/collections/common/data/lhotse/broadcasting.py57# ---------------------------------------------------------------------------COMMENT
MEDIUMnemo/collections/common/data/lhotse/broadcasting.py183# ---------------------------------------------------------------------------COMMENT
MEDIUMnemo/collections/common/data/lhotse/broadcasting.py185# ---------------------------------------------------------------------------COMMENT
MEDIUMnemo/collections/audio/modules/transforms.py383 # ------------------------------------------------------------------COMMENT
MEDIUMnemo/collections/audio/modules/transforms.py385 # ------------------------------------------------------------------COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py34# ==============================================================================COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py36# ==============================================================================COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py60# ==============================================================================COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py91# ─── Type aliases ──────────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py100# ─── Constants ─────────────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py158# ─── RTTM / UEM parsing ───────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py316# ─── UEM manipulation helpers ─────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py543# ─── Speaker segment timeline ─────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py633# ─── Bipartite speaker matching ───────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py677# ─── Per-segment speaker scoring ─────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py796# ─── Main diarization scoring ─────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py904# ─── Output formatting ────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py973# ─── Top-level evaluate ───────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py1113# ─── DER result wrapper ────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/md_eval.py1119# ───────────────────────────────────────────────────────────────────────────COMMENT
MEDIUMnemo/collections/asr/metrics/der.py54# ─── Lhotse-backed annotation helpers ──────────────────────────────────────COMMENT
MEDIUMnemo/collections/tts/parts/utils/tts_dataset_utils.py463# =============================================================================COMMENT
MEDIUMnemo/collections/tts/parts/utils/tts_dataset_utils.py465# =============================================================================COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py612# ─── Tests: Multi-file scoring ───────────────────────────────────────────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py68# ─── Helpers ──────────────────────────────────────────────────────────────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py182# ─── Tests: md_eval low-level engine ──────────────────────────────────────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py417# ─── Tests: der.py public API (score_labels_from_rttm_labels) ────────────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py672# ─── Tests: External-engine-verified values (cross-validated) ────────────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py902# ─── Tests: regression for no-UEM scoring (parity with external lib) ─────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py1057# ─── Tests: lhotse-based replacement for the external annotation lib ─────COMMENT
MEDIUMtests/collections/speaker_tasks/utils/test_der.py1549# ─── Tests: audio_end clipping ────────────────────────────────────────────COMMENT
MEDIUMtests/collections/speechlm2/test_salm_automodel_lora.py123# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/collections/speechlm2/test_salm_automodel_lora.py125# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/collections/speechlm2/test_salm_automodel_lora.py231# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/collections/speechlm2/test_salm_automodel_lora.py233# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/collections/speechlm2/test_to_hf.py173# ──────────────────────────────────────────────────────────────────────COMMENT
MEDIUMtests/collections/speechlm2/test_to_hf.py175# ──────────────────────────────────────────────────────────────────────COMMENT
MEDIUMtests/collections/speechlm2/test_to_hf.py188# ──────────────────────────────────────────────────────────────────────COMMENT
MEDIUMtests/collections/speechlm2/test_to_hf.py190# ──────────────────────────────────────────────────────────────────────COMMENT
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Unused Imports755 hits · 730 pts
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LOWtools/speech_data_explorer/data_explorer.py20CODE
LOWtools/speech_data_explorer/data_explorer.py39CODE
LOWtools/speech_data_explorer/data_explorer.py40CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/__init__.py16CODE
LOWnemo/lightning/__init__.py20CODE
LOWnemo/lightning/__init__.py20CODE
LOWnemo/core/__init__.py15CODE
LOWnemo/core/__init__.py16CODE
LOWnemo/core/connectors/save_restore_connector.py15CODE
LOWnemo/core/neural_types/__init__.py16CODE
LOWnemo/core/neural_types/__init__.py17CODE
LOWnemo/core/neural_types/__init__.py18CODE
LOWnemo/core/neural_types/__init__.py19CODE
LOWnemo/core/config/__init__.py15CODE
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LOWnemo/core/config/__init__.py31CODE
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LOWnemo/core/classes/__init__.py16CODE
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LOWnemo/core/classes/__init__.py20CODE
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Deep Nesting720 hits · 677 pts
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LOWnemo_dependencies.py27CODE
LOWnemo_dependencies.py44CODE
LOWnemo_dependencies.py90CODE
LOWnemo_dependencies.py115CODE
LOWtools/nemo_forced_aligner/align_eou.py445CODE
LOW…orced_aligner/tests/test_add_t_start_end_to_utt_obj.py260CODE
LOWtools/nemo_forced_aligner/utils/data_prep.py68CODE
LOWtools/nemo_forced_aligner/utils/make_ass_files.py111CODE
LOWtools/nemo_forced_aligner/utils/make_ass_files.py179CODE
LOWtools/nemo_forced_aligner/utils/make_ass_files.py335CODE
LOWtools/nemo_forced_aligner/utils/make_ctm_files.py69CODE
LOWtools/ctc_segmentation/scripts/prepare_data.py125CODE
LOWtools/ctc_segmentation/scripts/prepare_data.py221CODE
LOWtools/ctc_segmentation/scripts/prepare_data.py227CODE
LOWtools/ctc_segmentation/scripts/cut_audio.py50CODE
LOWtools/ctc_segmentation/scripts/utils.py167CODE
LOWtools/ctc_segmentation/scripts/utils.py213CODE
LOWtools/ctc_segmentation/scripts/utils.py267CODE
LOWtools/ctc_segmentation/scripts/utils.py303CODE
LOWtools/speech_data_explorer/data_explorer.py552CODE
LOWtools/speech_data_explorer/data_explorer.py695CODE
LOWtools/speech_data_explorer/data_explorer.py829CODE
LOWtools/speech_data_explorer/data_explorer.py996CODE
LOWtools/speech_data_explorer/data_explorer.py1038CODE
LOW…taset_preparation/customization_dataset_preparation.py63CODE
LOW…taset_preparation/customization_dataset_preparation.py237CODE
LOWtools/asr_evaluator/utils.py37CODE
LOWtools/asr_evaluator/utils.py85CODE
LOWtools/asr_evaluator/utils.py270CODE
LOWnemo/lightning/one_logger_callback.py152CODE
LOWnemo/lightning/base.py55CODE
LOWnemo/core/connectors/save_restore_connector.py53CODE
LOWnemo/core/connectors/save_restore_connector.py98CODE
LOWnemo/core/connectors/save_restore_connector.py290CODE
LOWnemo/core/connectors/save_restore_connector.py362CODE
LOWnemo/core/connectors/save_restore_connector.py455CODE
LOWnemo/core/neural_types/elements.py99CODE
LOWnemo/core/neural_types/neural_type.py66CODE
LOWnemo/core/neural_types/neural_type.py93CODE
LOWnemo/core/neural_types/neural_type.py179CODE
LOWnemo/core/neural_types/axes.py60CODE
LOWnemo/core/config/hydra_runner.py53CODE
LOWnemo/core/config/hydra_runner.py69CODE
LOWnemo/core/config/hydra_runner.py71CODE
LOWnemo/core/classes/exportable.py138CODE
LOWnemo/core/classes/common.py118CODE
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LOWnemo/core/classes/modelPT.py627CODE
LOWnemo/core/classes/modelPT.py794CODE
LOWnemo/core/classes/modelPT.py961CODE
LOWnemo/core/classes/modelPT.py1057CODE
LOWnemo/core/classes/modelPT.py1262CODE
LOWnemo/core/classes/modelPT.py1959CODE
LOWnemo/core/classes/modelPT.py2016CODE
LOWnemo/core/classes/mixins/access_mixins.py77CODE
LOWnemo/core/classes/mixins/adapter_mixins.py125CODE
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Self-Referential Comments104 hits · 302 pts
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MEDIUMnemo/core/neural_types/axes.py86 """This class represents axis semantics and (optionally) it's dimensionalitySTRING
MEDIUMnemo/core/config/modelPT.py87 # Create the config builderSTRING
MEDIUM…e_agent/pipecat/transports/network/websocket_server.py172 # Create a task to monitor the websocket connectionCOMMENT
MEDIUMnemo/utils/import_utils.py15# This file is taken from https://github.com/NVIDIA-NeMo/Curator/blob/dask/nemo_curator/utils/import_utils.py,COMMENT
MEDIUMnemo/utils/exp_manager.py645 # Create the logging directory if it does not existCOMMENT
MEDIUMnemo/utils/exp_manager.py1338 # Create the callback and attach it to trainerCOMMENT
MEDIUMnemo/utils/metaclasses.py36 # Create a new object instance - one per class.COMMENT
MEDIUMnemo/utils/decorators/deprecated.py87 # Create a bannerCOMMENT
MEDIUMnemo/collections/speechlm2/models/duplex_ear_tts.py460 # Create a random dropout decision for each BOS instanceCOMMENT
MEDIUMnemo/collections/speechlm2/models/duplex_ear_tts.py471 # Create a mask of the same shape as target_text_tokensCOMMENT
MEDIUM…/collections/speechlm2/parts/metrics/results_logger.py216 # Create a wav with eou prediction for debug purposesCOMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py94 # Create a range tensor from 0 to max_length - 1COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_vae_codec.py146 # Create the window tensor on the same device as the waveform.COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py158 # Create a range tensor from 0 to max_length - 1COMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py331 # Create a new, dense character vocabulary sorted by the original token IDCOMMENT
MEDIUMnemo/collections/speechlm2/modules/ear_tts_model.py834 # Create a padded tensor for the character IDsCOMMENT
MEDIUM…o/collections/speechlm2/data/duplex_ear_tts_dataset.py715 # Create a deepcopy and update durationCOMMENT
MEDIUM…o/collections/speechlm2/data/duplex_ear_tts_dataset.py773 # Create a zero tensor of shape [T] (assuming mono audio)COMMENT
MEDIUMnemo/collections/common/prompts/canary.py74 # This method handles a level of indirection for Canary.COMMENT
MEDIUMnemo/collections/common/prompts/canary2.py104 # This method handles a level of indirection for Canary.COMMENT
MEDIUMnemo/collections/common/data/lhotse/cutset.py1504 # Create a stream for each dataset.COMMENT
MEDIUMnemo/collections/common/data/lhotse/cutset.py1671 # Create a new Recording with the extended audioCOMMENT
MEDIUMnemo/collections/common/data/lhotse/cutset.py1706 # Create a Recording from the silence audioCOMMENT
MEDIUMnemo/collections/audio/parts/submodules/diffusion.py160 """This class implements the Ornstein-Uhlenbeck SDE with variance exploding noise schedule.STRING
MEDIUMnemo/collections/audio/parts/submodules/diffusion.py512 # Create a copy of SDECOMMENT
MEDIUM…o/collections/audio/parts/submodules/conformer_unet.py159 # Create the self-attention and padding masksCOMMENT
MEDIUM…lections/audio/parts/submodules/schroedinger_bridge.py415 # Create a copy of the noise scheduleCOMMENT
MEDIUMnemo/collections/audio/data/data_simulation.py594 # Create a radom set of microphonesCOMMENT
MEDIUMnemo/collections/audio/data/data_simulation.py1685 # Define a window around the direct path delayCOMMENT
MEDIUMnemo/collections/asr/losses/rnnt_pytorch.py179 """This function implements Equation 7 in the TDT paper https://arxiv.org/pdf/2304.06795.pdf,STRING
MEDIUMnemo/collections/asr/models/aed_multitask_models.py1196 # This method is a legacy helper for Canary that checks whether prompt slot values were providedCOMMENT
MEDIUMnemo/collections/asr/parts/features.py34# This file contains code artifacts adapted from https://github.com/ryanleary/patterCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/transcription.py301 # Create a results of the same type as each element in processed_outputsCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/transcription.py308 # Create a results of the same type as each element in processed_outputsCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/transcription.py316 # Create a results of the same type as each element in processed_outputsCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/transcription.py382 # Create a DataLoader if not already presentCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/diarization.py255 # Create a results of the same type as each element in processed_outputsCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/diarization.py262 # Create a results of the same type as each element in processed_outputsCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/diarization.py323 # Create a DataLoader if not already presentCOMMENT
MEDIUMnemo/collections/asr/parts/utils/numba_utils.py33 # Create an empty output arrayCOMMENT
MEDIUM…llections/asr/parts/utils/multispk_transcribe_utils.py1227 # Initialize the instance manager with the batch size of the chunk audio.COMMENT
MEDIUM…llections/asr/parts/utils/multispk_transcribe_utils.py1114 # Initialize the instance manager with the batch size of the chunk audio.COMMENT
MEDIUMnemo/collections/asr/parts/utils/transcribe_utils.py245 # Create a preprocessor to convert audio samples into raw features,COMMENT
MEDIUMnemo/collections/asr/parts/utils/diarization_utils.py189 # Create a list containing string formatted transcriptCOMMENT
MEDIUMnemo/collections/asr/parts/utils/diarization_utils.py554 # Create a split segment and add it to the corresponding intervalCOMMENT
MEDIUMnemo/collections/asr/parts/utils/diarization_utils.py1139 # Create a transscript information json dictionary from the output variablesCOMMENT
MEDIUM…ections/asr/parts/utils/batched_beam_decoding_utils.py971 # Create a range tensor: [0, 1, 2, ..., max_other_len-1]COMMENT
MEDIUMnemo/collections/asr/parts/submodules/spectr_augment.py199 # Create a mask_tensor with all the indices.COMMENT
MEDIUMnemo/collections/asr/parts/submodules/spectr_augment.py206 # Create a final mask that aligns with the full tensorCOMMENT
MEDIUM…llections/asr/parts/submodules/multi_head_attention.py616 # Create a helper tensor to find the local indices of global attentionCOMMENT
MEDIUMnemo/collections/asr/parts/preprocessing/features.py34# This file contains code artifacts adapted from https://github.com/ryanleary/patterCOMMENT
MEDIUMnemo/collections/asr/parts/preprocessing/segment.py34# This file contains code artifacts adapted from https://github.com/ryanleary/patterCOMMENT
MEDIUMnemo/collections/asr/parts/preprocessing/perturb.py34# This file contains code artifacts adapted from https://github.com/ryanleary/patterCOMMENT
MEDIUMnemo/collections/asr/parts/preprocessing/perturb.py1354 """This function is used to iterate through utterances with different offsets for each file."""STRING
MEDIUMnemo/collections/asr/inference/utils/context_manager.py170 # Create a dummy context with None valuesCOMMENT
MEDIUMnemo/collections/asr/inference/utils/bpe_decoder.py139 # Create a text segmentCOMMENT
MEDIUMnemo/collections/asr/inference/utils/manifest_io.py135 # Create a mapping of audio filepaths to their index in the manifestCOMMENT
MEDIUMnemo/collections/asr/inference/utils/manifest_io.py140 # Define an order of the audio filepathsCOMMENT
MEDIUM…ctions/asr/inference/streaming/framing/multi_stream.py176 # Create a new streamCOMMENT
MEDIUMnemo/collections/asr/modules/conformer_encoder.py705 # Create the self-attention and padding masksCOMMENT
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Docstring Block Structure60 hits · 300 pts
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HIGHnemo/utils/dependency.py61Import an optional dependency, raising a clear error if it is not installed. Args: module_name: The module STRING
HIGHnemo/collections/speechlm2/models/duplex_ear_tts.py1108 Returns a dictionary of initial inputs for inference, using registered buffers. Args: B (iSTRING
HIGHnemo/collections/speechlm2/models/nemotron_voicechat.py435 Runs full offline duplex speech-to-speech inference. This method performs: 1. Streaming SSTRING
HIGHnemo/collections/speechlm2/parts/optim_setup.py170 Utility used to freeze select model parameters, and skip them for the purpose of initializing an optimizer's paSTRING
HIGHnemo/collections/speechlm2/modules/ear_tts_vae_codec.py173 Converts a spectrogram back into a waveform using the overlap-add method. This function is an approximate inverSTRING
HIGHnemo/collections/speechlm2/modules/ear_tts_vae_codec.py362 Computes a Mel-scaled spectrogram from an audio waveform. This function transforms a standard spectrogram intoSTRING
HIGHnemo/collections/speechlm2/data/salm_dataset.py48 A dataset for Speech-Augmented Language Models (SALM) that processes multimodal conversations containing both tSTRING
HIGHnemo/collections/speechlm2/data/s2s_dataset.py29 A dataset for duplex speech-to-speech models that handles bidirectional conversations. This dataset processes STRING
HIGH…o/collections/speechlm2/data/duplex_ear_tts_dataset.py34 A dataset for duplex speech-to-speech models that handles bidirectional conversations. This dataset processes STRING
HIGH…ctions/common/tokenizers/huggingface/auto_tokenizer.py236 Adds a dictionary of special tokens (eos, pad, cls...). If special tokens are NOT in the vocabulary, they are STRING
HIGHnemo/collections/common/callbacks/ema.py168 EMAOptimizer is a wrapper for torch.optim.Optimizer that computes Exponential Moving Average of parameters regiSTRING
HIGHnemo/collections/common/parts/preprocessing/manifest.py48Iterate through json lines of provided manifests. NeMo ASR pipelines often assume certain manifest files structure.STRING
HIGHnemo/collections/common/parts/preprocessing/parsers.py229Creates parser from labels, set of arguments and concise parser name. Args: labels: List of labels to allocSTRING
HIGHnemo/collections/asr/metrics/md_eval.py255Parse a UEM (Un-partitioned Evaluation Map) file. Args: uem_file: Path to the UEM file. If ``None``, returnSTRING
HIGHnemo/collections/asr/models/sortformer_diar_models.py748 One-step forward pass for diarization inference in streaming mode. Args: processed_signal STRING
HIGHnemo/collections/asr/models/rnnt_models.py193 Helper method to extract the rnnt loss name, and potentially its kwargs to be passed. Args: STRING
HIGHnemo/collections/asr/parts/utils/transcribe_utils.py348 Prepare audio data for transcription. Args: cfg (DictConfig): Configuration dictionary containing the fSTRING
HIGHnemo/collections/asr/parts/utils/asr_batching.py207 Instantiates a Semi Sorted (Batch) Sampler. Args: model: ASR Model. dataset: Dataset which allSTRING
HIGHnemo/collections/asr/parts/utils/speaker_utils.py777 Combine overlaps with floating point numbers. Since neighboring integers are considered as continuous range, weSTRING
HIGH…ections/asr/parts/utils/batched_beam_decoding_utils.py920 Merge two batched beam hypotheses structures by concatenating transcripts. Used for streaming/chunked iSTRING
HIGH…ons/asr/parts/submodules/rnnt_maes_batched_computer.py384 Combines acoustic model log probabilities with language model scores based on the specified blank LM score modeSTRING
HIGH…ons/asr/parts/submodules/rnnt_maes_batched_computer.py412 Performs top-k selection and pruning for language model (LM) and automatic speech recognition (ASR) outputs STRING
HIGHnemo/collections/asr/parts/submodules/tdnn_attention.py26Statistics and time average pooling (TAP) layer This computes mean and, optionally, standard deviation statistics aSTRING
HIGH…o/collections/asr/inference/pipelines/base_pipeline.py521 Resolve language_code to a strict prompt index; raise if invalid. Args: language_code: (strSTRING
HIGH…o/collections/asr/inference/pipelines/base_pipeline.py554 Build prompt vectors for a batch of states using one-hot encoding. Args: states: (list) LisSTRING
HIGHnemo/collections/asr/inference/nmt/llm_translator.py110 Setup device for the LLM model. Args: device: (str) device to run the model on STRING
HIGHnemo/collections/asr/inference/nmt/llm_translator.py139 Returns prompt template for the LLM model. Args: model_name: (str) name of the model to getSTRING
HIGHnemo/collections/asr/inference/nmt/llm_translator.py156 Load NMT model in vLLM format. Args: llm_params: (dict) parameters for the LLM model STRING
HIGHnemo/collections/asr/data/audio_to_text_dataset.py930 Normalize manifest or tarred audio file paths into a ``ListConfig`` of lists. Handles string inputs (comma-sepSTRING
HIGHnemo/collections/asr/data/audio_to_text_dataset.py963 Chain multiple bucketed datasets using the specified bucketing strategy. When multiple datasets are provided (STRING
HIGHnemo/collections/asr/data/audio_to_text_dataset.py1015 Calculate per-bucket batch sizes for adaptive bucketing. Supports two modes: linear scaling (integer ``bucketiSTRING
HIGHnemo/collections/tts/models/magpietts.py941Normalize speaker_indices to a tensor of shape (batch_size,). Args: speaker_indices: Speaker selectSTRING
HIGHnemo/collections/tts/models/magpietts.py993Get baked context embeddings for a batch, with per-element speaker selection. Args: batch_size: NumSTRING
HIGHnemo/collections/tts/models/magpietts.py1362 Convert attention probability matrices to numpy images for logging. Args: attention_prob_mSTRING
HIGHnemo/collections/tts/models/magpietts.py1404 Decode audio codes to waveforms and convert to numpy arrays for logging. Args: logits: ModSTRING
HIGHnemo/collections/tts/models/magpietts.py1886Prepare all context tensors for the decoder. This method orchestrates text encoding, context extraction, and moSTRING
HIGHnemo/collections/tts/models/magpietts.py3719 Generate speech from raw text transcript. This is a convenience method for single-utterance text-to-spSTRING
HIGHnemo/collections/tts/parts/utils/tts_dataset_utils.py484 Split a paragraph into sentences based on sentence-ending punctuation. Sentence separators are chosen from theSTRING
HIGHnemo/collections/tts/parts/utils/tts_dataset_utils.py779 Unified text chunking for inference: returns single chunk if below threshold, multiple sentence chunks if aboveSTRING
HIGH…o/collections/tts/modules/magpietts_inference/utils.py314Load a MagpieTTS model from checkpoint or NeMo archive. Supports two loading modes: 1. Checkpoint mode: hparamsSTRING
HIGH…o/collections/tts/modules/magpietts_inference/utils.py397Load an EasyMagpieTTSInferenceModel (decoder-only) from checkpoint or NeMo archive. Uses the inference-only base clSTRING
HIGHscripts/asr_language_modeling/ngram_lm/ngram_merge.py167 Calculates perplexity of a given ngram model on a test file. Args: ngram_mod (str): The paSTRING
HIGHscripts/asr_language_modeling/ngram_lm/ngram_merge.py201 Converts an ngram model in binary format to ARPA format. Args: - ngram_mod (str): The path to STRING
HIGHscripts/asr_language_modeling/ngram_lm/ngram_merge.py342 Function: make_symbol_list Create a symbol table for the input tokenizer model file. Args: nemo_mSTRING
HIGH…ognition/partial_conversion_to_tarred_audio_dataset.py66 Selects and returns a subset of shards from the tarred manifest file. Args: manifest_filepath (str): TSTRING
HIGH…ognition/partial_conversion_to_tarred_audio_dataset.py136 Creates tarred shards based on the provided configuration. Args: cfg (PartialASRTarredDatasetConfig): STRING
HIGH…/speech_recognition/convert_to_tarred_audio_dataset.py180 Creates a new tarred dataset from a given manifest file. Args: manifest_path (str): Path tSTRING
HIGH…/speech_recognition/convert_to_tarred_audio_dataset.py359 Creates a concatenated tarred dataset from the base manifest and additional manifest files. Args: STRING
HIGHscripts/tts_comparison_report/reporting/models.py83Create sample metadata from one filewise metrics item. Args: item: One entry from the filewise metrSTRING
HIGHscripts/tts_comparison_report/reporting/models.py167Create benchmark data by discovering benchmark artifacts in storage. Args: benchmark_name: Name of STRING
HIGHscripts/tts_comparison_report/reporting/models.py297Create bucket data by discovering benchmark artifacts in storage. Args: bucket_name: Display name oSTRING
HIGHscripts/tts_comparison_report/reporting/models.py370Return the aggregated value of a metric for one benchmark. Args: metric_name: Name of the metric toSTRING
HIGHscripts/tts_comparison_report/reporting/models.py449Return filewise samples for a metric from one or all benchmarks. Args: metric_name: Name of the metSTRING
HIGHscripts/tts_comparison_report/reporting/models.py469Return generated audio file paths for a benchmark. Args: benchmark_name: Name of the benchmark. STRING
HIGHscripts/tts_comparison_report/reporting/models.py495Return sample metadata for a benchmark derived from filewise metrics. Args: benchmark_name: Name ofSTRING
HIGHscripts/tts_comparison_report/reporting/orchestrator.py401Generate evaluation reports, upload report artifacts to S3, and return report URLs. This method performs the fuSTRING
HIGH…s_comparison_report/reporting/components/stat_tests.py92Run statistical tests for all distribution metrics. Args: bucket_baseline: Baseline bucket data. buSTRING
HIGH…comparison_report/reporting/components/audio_report.py70Prepare audio pairs for the selected benchmarks. Args: bucket_baseline: Baseline bucket data. buckeSTRING
HIGH…omparison_report/reporting/components/metrics_table.py64Prepare formatted metric rows for one benchmark comparison table. Args: benchmark_name: Name of the benchmaSTRING
HIGH…omparison_report/reporting/components/metrics_table.py105Prepare formatted metric rows for the summary comparison table. Args: bucket_baseline: Baseline bucket dataSTRING
AI Structural Patterns263 hits · 256 pts
SeverityFileLineSnippetContext
LOWtools/ctc_segmentation/scripts/prepare_data.py125CODE
LOWtools/speech_data_explorer/data_explorer.py996CODE
LOWnemo/core/classes/exportable.py60CODE
LOWnemo/core/classes/exportable.py138CODE
LOWnemo/core/classes/common.py988CODE
LOWnemo/core/classes/mixins/adapter_mixins.py606CODE
LOWnemo/core/classes/mixins/hf_io_mixin.py118CODE
LOWnemo/core/classes/mixins/adapter_mixin_strategies.py77CODE
LOWnemo/core/classes/mixins/adapter_mixin_strategies.py185CODE
LOWnemo/core/classes/mixins/adapter_mixin_strategies.py219CODE
LOWnemo/core/optim/adan.py80CODE
LOWnemo/core/optim/adafactor.py62CODE
LOWnemo/core/optim/novograd.py48CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/diar.py55CODE
LOW…nts/voice_agent/pipecat/services/nemo/streaming_asr.py43CODE
LOW…gents/voice_agent/pipecat/services/nemo/turn_taking.py45CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py498CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py309CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/stt.py71CODE
LOWnemo/utils/export_utils.py420CODE
LOWnemo/utils/export_utils.py453CODE
LOWnemo/utils/timers.py164CODE
LOWnemo/utils/callbacks/cuda_graph.py139CODE
LOWnemo/utils/callbacks/nemo_model_checkpoint.py47CODE
LOWnemo/utils/callbacks/dist_ckpt_io.py223CODE
LOWnemo/collections/speechlm2/vllm/salm/config.py70CODE
LOWnemo/collections/speechlm2/models/nemotron_voicechat.py421CODE
LOWnemo/collections/speechlm2/parts/parallel.py117CODE
LOWnemo/collections/speechlm2/parts/parallel.py200CODE
LOW…/collections/speechlm2/parts/metrics/results_logger.py158CODE
LOWnemo/collections/speechlm2/modules/ear_tts_vae_codec.py704CODE
LOWnemo/collections/speechlm2/modules/ear_tts_vae_codec.py785CODE
LOWnemo/collections/speechlm2/modules/ear_tts_model.py760CODE
LOWnemo/collections/speechlm2/modules/ear_tts_model.py1195CODE
LOW…o/collections/speechlm2/data/duplex_ear_tts_dataset.py125CODE
LOW…tions/common/losses/latent_speaker_supervision_loss.py89CODE
LOW…llections/common/tokenizers/sentencepiece_tokenizer.py464CODE
LOWnemo/collections/common/tokenizers/tokenizer_utils.py154CODE
LOWnemo/collections/common/tokenizers/char_tokenizer.py89CODE
LOWnemo/collections/common/tokenizers/char_tokenizer.py443CODE
LOW…mo/collections/common/tokenizers/tiktoken_tokenizer.py105CODE
LOW…mo/collections/common/tokenizers/tiktoken_tokenizer.py282CODE
LOW…ctions/common/tokenizers/huggingface/auto_tokenizer.py35CODE
LOW…ons/common/tokenizers/text_to_speech/tts_tokenizers.py429CODE
LOW…ons/common/tokenizers/text_to_speech/tts_tokenizers.py543CODE
LOW…ons/common/tokenizers/text_to_speech/tts_tokenizers.py782CODE
LOW…ons/common/tokenizers/text_to_speech/tts_tokenizers.py932CODE
LOW…ons/common/tokenizers/text_to_speech/tts_tokenizers.py1109CODE
LOW…ons/common/tokenizers/text_to_speech/tts_tokenizers.py1223CODE
LOWnemo/collections/common/parts/utils.py215CODE
LOWnemo/collections/common/parts/rnn.py25CODE
LOWnemo/collections/common/parts/rnn.py295CODE
LOWnemo/collections/common/data/dataset.py47CODE
LOWnemo/collections/common/data/dataset.py333CODE
LOWnemo/collections/audio/losses/audio.py220CODE
LOWnemo/collections/audio/losses/audio.py314CODE
LOWnemo/collections/audio/parts/utils/transforms.py98CODE
LOWnemo/collections/audio/parts/utils/transforms.py211CODE
LOWnemo/collections/audio/parts/submodules/diffusion.py498CODE
LOWnemo/collections/audio/parts/submodules/multichannel.py182CODE
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Excessive Try-Catch Wrapping214 hits · 241 pts
SeverityFileLineSnippetContext
LOWnemo_dependencies.py67 except Exception as e:CODE
MEDIUMnemo_dependencies.py68 print(f"Error analyzing {file_path}: {e}")CODE
LOWtools/ctc_segmentation/scripts/prepare_data.py121 except Exception as e:CODE
MEDIUMtools/ctc_segmentation/scripts/prepare_data.py90def _load_sox_transformer():CODE
LOW…ols/ctc_segmentation/scripts/get_metrics_and_filter.py188 except Exception as e:CODE
LOWtools/ctc_segmentation/scripts/utils.py131 except Exception as e:CODE
LOWtools/ctc_segmentation/scripts/run_ctc_segmentation.py175 except Exception as e:CODE
LOWtools/speech_data_explorer/data_explorer.py520 except Exception as e:CODE
LOWtools/speech_data_explorer/data_explorer.py2793 except Exception as ex:CODE
LOWtools/speech_data_explorer/data_explorer.py2855 except Exception as ex:CODE
LOWtools/speech_data_explorer/data_explorer.py2880 except Exception as ex:CODE
LOWtools/speech_data_explorer/data_explorer.py2907 except Exception as ex:CODE
LOWnemo/lightning/callback_group.py93 except Exception:CODE
LOWnemo/core/connectors/save_restore_connector.py763 except Exception as e:CODE
LOWnemo/core/classes/common.py129 except Exception:CODE
LOWnemo/core/classes/common.py133 except Exception as e2:CODE
LOWnemo/core/classes/common.py601 except Exception:CODE
LOWnemo/core/classes/common.py733 except Exception:CODE
LOWnemo/core/classes/common.py791 except Exception as e:CODE
LOWnemo/core/classes/common.py808 except Exception as e:CODE
LOWnemo/core/classes/modelPT.py769 except Exception as e:CODE
LOWnemo/core/utils/cuda_python_utils.py239 except Exception:CODE
LOWnemo/core/utils/numba_utils.py143 except Exception:CODE
LOWnemo/agents/voice_agent/utils/config_manager.py71 except Exception as e:CODE
LOW…o/agents/voice_agent/utils/tool_calling/basic_tools.py54 except Exception as e:CODE
LOW…e_agent/pipecat/transports/network/websocket_server.py191 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/diar.py176 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/diar.py181 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/diar.py210 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/diar.py241 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py223 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py279 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py383 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py393 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py496 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py549 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py608 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py738 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py777 except Exception as e:CODE
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py808 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py176 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py181 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py184 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py388 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py398 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/tts.py590 except Exception as e:CODE
LOW…ts/voice_agent/pipecat/services/nemo/streaming_diar.py203 except Exception as e:CODE
MEDIUM…ts/voice_agent/pipecat/services/nemo/streaming_diar.py204 print(f"Error in diarizer streaming step: {e}")CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py174 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py275 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py370 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py593 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py687 except Exception as e:CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/stt.py258 except Exception as e:CODE
LOWnemo/utils/export_utils.py265 except Exception: # there may ne size mismatch and it may be OKCODE
LOWnemo/utils/export_utils.py287 except Exception: # there may be size mismatch and it may be OKCODE
LOWnemo/utils/export_utils.py400except Exception:CODE
LOWnemo/utils/cast_utils.py118 except Exception:CODE
LOWnemo/utils/env_var_parsing.py105 except Exception:CODE
LOWnemo/utils/cloud.py145 except Exception as e:CODE
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Modern Structural Boilerplate213 hits · 218 pts
SeverityFileLineSnippetContext
LOWtools/ctc_segmentation/scripts/normalization_helpers.py15__all__ = ["LATIN_TO_RU", "RU_ABBREVIATIONS"]CODE
LOWnemo/lightning/base_callback.py83 def update_config(self, *args, **kwargs) -> None:CODE
LOWnemo/lightning/base_callback.py88__all__ = ["BaseCallback"]CODE
LOWnemo/lightning/__init__.py35__all__ = [CODE
LOWnemo/lightning/callback_group.py58 def update_config(self, nemo_version: str, trainer: Any, **kwargs) -> None:CODE
LOWnemo/lightning/callback_group.py185__all__ = ['CallbackGroup', 'hook_class_init_with_callbacks']CODE
LOWnemo/lightning/one_logger_callback.py34__all__ = ['OneLoggerNeMoCallback']CODE
LOWnemo/lightning/one_logger_callback.py247 def update_config(self, nemo_version: str, trainer: Trainer, **kwargs) -> None:CODE
LOWnemo/lightning/base.py74__all__ = ["get_vocab_size", "teardown"]CODE
LOWnemo/core/neural_types/elements.py22__all__ = [CODE
LOWnemo/core/neural_types/neural_type.py23__all__ = [CODE
LOWnemo/core/neural_types/comparison.py17__all__ = ['NeuralTypeComparisonResult']CODE
LOWnemo/core/neural_types/axes.py18__all__ = ['AxisKindAbstract', 'AxisKind', 'AxisType']CODE
LOWnemo/core/config/base_config.py18__all__ = ['Config']CODE
LOWnemo/core/config/optimizers.py21__all__ = [CODE
LOWnemo/core/config/pytorch_lightning.py20__all__ = ['TrainerConfig']CODE
LOWnemo/core/config/pytorch.py20__all__ = ['DataLoaderConfig']CODE
LOWnemo/core/classes/exportable.py36__all__ = ['ExportFormat', 'Exportable']CODE
LOWnemo/core/classes/dataset.py21__all__ = ['Dataset', 'IterableDataset']CODE
LOWnemo/core/classes/loss.py19__all__ = ['Loss']CODE
LOWnemo/core/classes/common.py52__all__ = ['Typing', 'FileIO', 'Model', 'Serialization', 'typecheck', 'PretrainedModelInfo']CODE
LOWnemo/core/classes/modelPT.py45__all__ = ['ModelPT']CODE
LOWnemo/core/classes/module.py23__all__ = ['NeuralModule', 'freeze', 'unfreeze']CODE
LOWnemo/core/classes/mixins/adapter_mixins.py403 def set_accepted_adapter_types(self, adapter_types: List[Union[type, str]]) -> None:CODE
LOWnemo/core/optim/adafactor.py28__all__ = ['Adafactor']CODE
LOWnemo/core/optim/optimizers.py77__all__ = ['get_optimizer', 'register_optimizer', 'parse_optimizer_args']CODE
LOWnemo/core/optim/novograd.py18__all__ = ['Novograd']CODE
LOWnemo/core/utils/optional_libs.py20__all__ = [CODE
LOWnemo/agents/voice_agent/pipecat/services/nemo/utils.py131 def _update_feature_buffer(self, feat_chunk: torch.Tensor) -> None:CODE
LOWnemo/utils/nemo_logging.py30__all__ = ["Logger", "LogMode"]CODE
LOWnemo/utils/env_var_parsing.py47__all__ = [CODE
LOWnemo/utils/timers.py25__all__ = ["NamedTimer", "SimpleTimer"]CODE
LOWnemo/utils/import_utils.py25logger = logging.getLogger(__name__)CODE
LOWnemo/utils/decorators/experimental.py16__all__ = ['experimental']CODE
LOWnemo/utils/decorators/deprecated.py16__all__ = [CODE
LOWnemo/utils/decorators/port_docs.py18__all__ = [CODE
LOWnemo/utils/callbacks/cuda_graph.py51__all__ = ["CUDAGraphCallback"]CODE
LOWnemo/utils/callbacks/nemo_model_checkpoint.py495 def set_checkpoint_unfinished_marker(checkpoint_path: Union[Path, str], barrier_after=False) -> None:CODE
LOWnemo/utils/formatters/utils.py21__all__ = ["check_color_support", "to_unicode"]CODE
LOWnemo/utils/formatters/base.py21__all__ = ["BaseNeMoFormatter"]CODE
LOWnemo/collections/speechlm2/__init__.py26__all__ = [CODE
LOWnemo/collections/speechlm2/models/__init__.py23__all__ = [CODE
LOWnemo/collections/speechlm2/streaming/__init__.py17__all__ = ['DuplexSTTStreamingInference']CODE
LOWnemo/collections/speechlm2/parts/metrics/__init__.py23__all__ = [CODE
LOWnemo/collections/speechlm2/modules/__init__.py17__all__ = [CODE
LOWnemo/collections/speechlm2/modules/perception.py94 def set_activation_checkpointing(self, enabled: bool) -> None:CODE
LOWnemo/collections/speechlm2/modules/perception.py215def _set_encoder_activation_checkpointing(encoder: nn.Module, enabled: bool) -> None:CODE
LOWnemo/collections/speechlm2/modules/perception.py292 def set_activation_checkpointing(self, enabled: bool) -> None:CODE
LOWnemo/collections/speechlm2/data/__init__.py20__all__ = [CODE
LOW…/collections/common/metrics/classification_accuracy.py24__all__ = ['TopKClassificationAccuracy']CODE
LOWnemo/collections/common/metrics/perplexity.py19__all__ = ['Perplexity']CODE
LOWnemo/collections/common/metrics/__init__.py24__all__ = [CODE
LOW…llections/common/metrics/global_average_loss_metric.py18__all__ = ['GlobalAverageLossMetric']CODE
LOW…ections/common/metrics/metric_string_to_torchmetric.py22__all__ = ['MetricStringToTorchMetric', 'TextMetricsSet', 'ClassificationMetricsSet']CODE
LOWnemo/collections/common/losses/cross_entropy.py22__all__ = ['CrossEntropyLoss', 'NLLLoss']CODE
LOWnemo/collections/common/losses/mse_loss.py20__all__ = ['MSELoss']CODE
LOWnemo/collections/common/losses/aggregator.py22__all__ = ['AggregatorLoss']CODE
LOW…tions/common/losses/latent_speaker_supervision_loss.py22__all__ = ["LatentSpeakerSupervisionLoss"]CODE
LOWnemo/collections/common/losses/multi_similarity_loss.py24__all__ = ['MultiSimilarityLoss']CODE
LOWnemo/collections/common/losses/spanning_loss.py20__all__ = ['SpanningLoss']CODE
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Redundant / Tautological Comments141 hits · 210 pts
SeverityFileLineSnippetContext
LOWtools/speech_data_simulator/conf/data_simulator.yaml64 add_seg_aug: False # Set True to enable augmentation on each speech segmentCODE
LOWtools/speech_data_simulator/conf/data_simulator.yaml72 add_sess_aug: False # Set True to enable audio augmentation on the whole sessionCODE
LOWtools/speech_data_explorer/data_explorer.py148 # Check if file existsCOMMENT
LOWnemo/core/connectors/save_restore_connector.py86 # Check if we are packing the folder into a nemo fileCOMMENT
LOWnemo/core/config/hydra_runner.py94 # Check if user set the schema.COMMENT
LOWnemo/core/classes/common.py137 # Check if this is a missing dependency issue vs a malicious targetCOMMENT
LOWnemo/core/classes/common.py145 # Check if the module path is in one of our approved prefixes.COMMENT
LOWnemo/core/classes/common.py488 # Check if keys exists in the defined input typesCOMMENT
LOWnemo/core/classes/common.py1029 # Check if nemo_model_file_in_cache is a directoryCOMMENT
LOWnemo/core/classes/common.py1143 # Check if api token exists, use if it doesCOMMENT
LOWnemo/core/classes/common.py1149 # Check if model exists in HFCOMMENT
LOWnemo/core/classes/modelPT.py704 # Check if caller provided optimizer name, default to Adam otherwiseCOMMENT
LOWnemo/core/classes/modelPT.py722 # Check if caller has optimizer kwargs, default to empty dictionaryCOMMENT
LOWnemo/core/classes/modelPT.py1364 # Check if model is being resumed or not - only works if `Trainer` is attached to modelCOMMENT
LOWnemo/core/classes/modelPT.py1550 # Assign trainer to the modelCOMMENT
LOWnemo/core/classes/mixins/adapter_mixins.py362 # Check if type is supported (if available) and is an enabled adapterCOMMENT
LOWnemo/core/classes/mixins/adapter_mixins.py477 # Check if adapter is enabled or notCOMMENT
LOWnemo/core/classes/mixins/hf_io_mixin.py108 # Check if api token exists, use if it doesCOMMENT
LOWnemo/core/classes/mixins/adapter_mixin_strategies.py241 # Check if globally allowed to compute aux lossCOMMENT
LOWnemo/core/optim/distributed_adam.py658 # Check if fragment needs to be updatedCOMMENT
LOWnemo/core/utils/process_launcher/launcher.py269 # Check if all processes are completed or notCOMMENT
LOW…ice_agent/pipecat/utils/text/simple_text_aggregator.py74 # Check if the only period is a bullet point (e.g., "1. Alpha" or incomplete "1.")COMMENT
LOW…ice_agent/pipecat/utils/text/simple_text_aggregator.py81 # Check if any of the abbreviations "e.", "i." "g.", "etc." are present in the textCOMMENT
LOW…ents/voice_agent/pipecat/services/nemo/audio_logger.py721 # Check if we need to start a new turn or append to existing turnCOMMENT
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py457 # Check if there's already a vLLM process running on the same port and modelCOMMENT
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py461 # Check if this process is using the same port and modelCOMMENT
LOWnemo/agents/voice_agent/pipecat/services/nemo/llm.py574 # Check if process is still runningCOMMENT
LOWnemo/utils/te_utils.py23# Check if Transformer Engine has quantized tensor classesCOMMENT
LOWnemo/utils/exp_manager.py1404 # Check if cuda is avialable as preemption is supported only on GPUsCOMMENT
LOWnemo/utils/decorators/deprecated.py48 # Check if we already warned about that function.COMMENT
LOWnemo/utils/callbacks/preemption.py57 # Check if torch distributed is initialised, required for broadcasting the preemption signal to all the ranksCOMMENT
LOWnemo/collections/speechlm2/models/duplex_ear_tts.py1409 # Check if we should use the custom groupingCOMMENT
LOWnemo/collections/speechlm2/parts/metrics/turn_taking.py64 # Check if within toleranceCOMMENT
LOW…o/collections/speechlm2/parts/metrics/mcq_evaluator.py238 # Check if response is emptyCOMMENT
LOW…o/collections/speechlm2/parts/metrics/mcq_evaluator.py282 # Check if correctCOMMENT
LOWnemo/collections/speechlm2/parts/metrics/empty_text.py49 # Check if hypothesis is empty or only whitespaceCOMMENT
LOWnemo/collections/speechlm2/data/force_align.py269 # Check if this is a Segment object (has words_and_tokens attribute)COMMENT
LOWnemo/collections/speechlm2/data/force_align.py273 # Check if this is a Word object (has 'text' and timing attributes)COMMENT
LOW…o/collections/speechlm2/data/duplex_ear_tts_dataset.py585 # Check if system prompt exists in custom fieldCOMMENT
LOWnemo/collections/common/parts/preprocessing/cleaners.py220 # Check if there are non-numbersCOMMENT
LOWnemo/collections/common/parts/preprocessing/cleaners.py248 # Check if it is a currencyCOMMENT
LOWnemo/collections/common/prompts/formatter.py343 # Check if the tokenizer is aggregate and perform extra checks.COMMENT
LOWnemo/collections/common/data/lhotse/cutset.py567 # Check if we have any attributes that are propagated downwards to each item in the group.COMMENT
LOWnemo/collections/audio/data/data_simulation.py1306 # Check if a feasible source is foundCOMMENT
LOWnemo/collections/asr/metrics/multitask.py131 # Check if string is for value assertion or equivalency of values.COMMENT
LOWnemo/collections/asr/models/ssl_models.py565 # Set flag to register tensorsCOMMENT
LOWnemo/collections/asr/models/label_models.py528 # Check if all outputs are non-emptyCOMMENT
LOWnemo/collections/asr/models/aed_multitask_models.py579 # Check if only one audio is provided with stringCOMMENT
LOWnemo/collections/asr/models/aed_multitask_models.py596 # Check if chunking will be enabledCOMMENT
LOWnemo/collections/asr/models/aed_multitask_models.py1012 # Check if we have defaults for this role.COMMENT
LOWnemo/collections/asr/models/aed_multitask_models.py1356 # Check if the model_restore_path is already an extracted directory (which happens during restore_from)COMMENT
LOWnemo/collections/asr/parts/mixins/asr_adapter_mixins.py89 # Check if default module name is None or notCOMMENT
LOWnemo/collections/asr/parts/mixins/asr_adapter_mixins.py99 # Check if encoder adapters should be addedCOMMENT
LOWnemo/collections/asr/parts/mixins/asr_adapter_mixins.py106 # Check if module existsCOMMENT
LOWnemo/collections/asr/parts/mixins/asr_adapter_mixins.py165 # Check if default module name is None or notCOMMENT
LOWnemo/collections/asr/parts/mixins/asr_adapter_mixins.py174 # Check if encoder adapters should be usedCOMMENT
LOWnemo/collections/asr/parts/mixins/asr_adapter_mixins.py197 # Check if encoder adapters should be used or are enabledCOMMENT
LOWnemo/collections/asr/parts/mixins/transcription.py285 # Check if internal config is validCOMMENT
LOWnemo/collections/asr/parts/mixins/transcription.py365 # Check if internal config is validCOMMENT
LOWnemo/collections/asr/parts/mixins/transcription.py489 # Check if audio is a list of strings (filepaths or manifests)COMMENT
81 more matches not shown…
Hallucination Indicators14 hits · 105 pts
SeverityFileLineSnippetContext
CRITICALnemo/core/connectors/save_restore_connector.py124 model = nemo.collections.asr.models.EncDecCTCModel.restore_from('asr.nemo')STRING
CRITICALnemo/core/connectors/save_restore_connector.py263 model = nemo.collections.asr.models.EncDecCTCModel.restore_from('asr.nemo')STRING
CRITICALnemo/core/connectors/save_restore_connector.py303 state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.nemo', './ckpts')STRING
CRITICALnemo/core/connectors/save_restore_connector.py312 state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from(STRING
CRITICALnemo/core/classes/modelPT.py462 model = nemo.collections.asr.models.EncDecCTCModel.restore_from('asr.nemo')STRING
CRITICALnemo/core/classes/modelPT.py1483 state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.nemo', './ckpts')STRING
CRITICALnemo/core/classes/modelPT.py1492 state_dict = nemo.collections.asr.models.EncDecCTCModel.extract_state_dict_from(STRING
CRITICAL…parts/submodules/aed_decoding/aed_batched_streaming.py109 pred_tokens_ids, batch_size, _ = self.asr_model.decoding.decoding.greedy_search._prepare_for_search(CODE
CRITICAL…parts/submodules/aed_decoding/aed_batched_streaming.py141 self.asr_model.decoding.decoding.greedy_search._one_step_forward(CODE
CRITICAL…parts/submodules/aed_decoding/aed_batched_streaming.py203 pred_tokens_ids, batch_size, _ = self.asr_model.decoding.decoding.greedy_search._prepare_for_search(CODE
CRITICAL…parts/submodules/aed_decoding/aed_batched_streaming.py231 self.asr_model.decoding.decoding.greedy_search._one_step_forward(CODE
CRITICALnemo/collections/tts/models/fastpitch.py879 n_speakers = self.fastpitch.speaker_emb.weight.data.size()[0]CODE
CRITICALtests/lightning/test_one_logger_callback.py101 mock_provider_instance.with_base_config.return_value.with_export_config.return_value.configure_provider.assert_cCODE
CRITICALtests/collections/tts/modules/test_transformer_2501.py1233 assert moe_ffn.router.router.weight.grad.abs().sum() > 0, "Router weight grad must be non-zero"CODE
Slop Phrases35 hits · 100 pts
SeverityFileLineSnippetContext
LOWnemo/collections/asr/losses/ctc.py45 # Don't forget to properly call base constructorCOMMENT
LOWexamples/asr/slurm_example.sh34CONTAINER=nvcr.io/nvidia/nemo:25.02.rc4 # Adjust to your needs. and make sure you have ngc key in ~/.config/enroot/.credCODE
MEDIUMexamples/tts/conf/fastpitch_ssl.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch_align_ipa.yaml2# If you want to train a model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch_align_44100_adapter.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/aligner.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch_align_44100.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch_align_v1.05.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch_align_ipa_adapter.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/hifigan_dataset/hifigan_44100.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/hifigan_dataset/hifigan_22050.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/hifigan/hifigan.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/hifigan/hifigan_44100.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch/fastpitch_44100.yaml2# If you want to train a model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/fastpitch/fastpitch_22050.yaml2# If you want to train a model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/mel_codec_22050.yaml3# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUM…conf/audio_codec/audio_codec_low_frame_rate_22050.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/mel_codec_44100.yaml3# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/audio_codec_44100.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/audio_codec_22050.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/audio_codec_16000.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/audio_codec_24000.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/audio_codec/encodec_24000.yaml2# If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUM…es/tts/conf/zh/fastpitch_align_multispeaker_22050.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/zh/fastpitch_align_22050.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/de/fastpitch_align_44100_phoneme.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/de/fastpitch_align_22050_mix.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUM…amples/tts/conf/de/fastpitch_align_44100_grapheme.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUM…amples/tts/conf/de/fastpitch_align_22050_grapheme.yaml2# rate. If you want to train model on other dataset, you can change config values according to your dataset.COMMENT
MEDIUMexamples/tts/conf/es/fastpitch_align_44100_ipa.yaml2# 44.1KHz sampling rate. If you want to train model on other dataset, you can change config values accordingCOMMENT
MEDIUM…mples/tts/conf/es/fastpitch_align_44100_ipa_multi.yaml2# 44.1KHz sampling rate. If you want to train model on other dataset, you can change config values accordingCOMMENT
MEDIUMexamples/tts/conf/es/fastpitch_align_44100.yaml2# 44.1KHz sampling rate. If you want to train model on other dataset, you can change config values accordingCOMMENT
MEDIUMscripts/tokenizers/process_asr_text_tokenizer.py37# In either case, you can add commas to concatenate different manifests or different data files.COMMENT
MEDIUM…pts/dataset_processing/process_speech_commands_data.py463 f'\n<<NOTE>> Duration computation was skipped for demonstration purposes on Colaboratory.\n'CODE
MEDIUMscripts/installers/install_opengrm.sh19# Alternatively, in the Linux Debian you can use: sudo apt install libngram-toolsCOMMENT
Verbosity Indicators49 hits · 93 pts
SeverityFileLineSnippetContext
LOW…/collections/speechlm2/parts/metrics/results_logger.py344 # Step 1: Each rank saves its own results with rank suffixCOMMENT
LOW…/collections/speechlm2/parts/metrics/results_logger.py352 # Step 2: Synchronize all ranks before mergingCOMMENT
LOW…/collections/speechlm2/parts/metrics/results_logger.py356 # Step 3: Only rank 0 merges all results and computes final metricsCOMMENT
LOW…/collections/speechlm2/parts/metrics/results_logger.py433 # Step 4: Broadcast metrics from rank 0 to all other ranksCOMMENT
LOW…tions/common/losses/latent_speaker_supervision_loss.py159 # Step 1: Identify speaker token positions and their speaker indices.COMMENT
LOW…tions/common/losses/latent_speaker_supervision_loss.py170 # Step 2: Forward-fill using cummax on position indices.COMMENT
LOW…tions/common/losses/latent_speaker_supervision_loss.py178 # Step 3: Gather the speaker index at the last speaker position.COMMENT
LOWnemo/collections/asr/models/online_diarizer.py546 # Step 1: Get subsegments for embedding extraction.COMMENT
LOWnemo/collections/asr/models/online_diarizer.py585 # Step 4: Generate RTTM style diarization labels from segment ranges and cluster labelsCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1233 # Step 2: diarize or get GT rttmsCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1251 # Step 3: update diar statesCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1270 # Step 4: find active speakersCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1285 # Step 5: generate instance for active speakersCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1301 # Step 6:COMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1315 # Step 7: ASR forward pass for active speakersCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1337 # Step 8: update ASR statesCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1352 # Step 9: update seglsts with timestampsCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py689 # Step 1: InitializationCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py699 # Step 2: Get most likely labels for current frameCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py703 # Step 3: Get fusion scoresCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py713 # Step 4: Get most likely labels with fusion scores. Labels that are blank or repeated are ignored.COMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py719 # Step 5: Update labels if they initially weren't blank or repeatedCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py724 # Step 6: Update fusion states and scores for non-blank and non-repeated labelsCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py771 # Step 2: Get most likely labels for current frameCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py776 # Step 3: Get fusion scoresCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py785 # Step 4: Get most likely labels with fusion scores. Labels that are blank or repeated are ignored.COMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py791 # Step 5: Update labels if they initially weren't blank or repeatedCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py799 # Step 6: Update fusion states and scores for non-blank and non-repeated labelsCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py745 # Step 1: Initialization for fusion modelsCOMMENT
LOWnemo/collections/asr/inference/streaming/state/state.py218 # we need to check if the last token is the same as the first token of the completed outputCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1020 # Step 1: Prepare parameters for sentence generationCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1027 # Step 2: Select a speakerCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1040 # Step 3: Generate a sentenceCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1044 # Step 4: Generate a timestamp for either silence or overlapCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1541 # Step 1: Prepare parameters for sentence generationCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1548 # Step 2: Select a speakerCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1563 # Step 3: Generate a sentenceCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1567 # Step 4: Generate a time-stamp for either silence or overlapCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1062 # Step 6: Build entries for output filesCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1132 # Step 7: Normalize and write to diskCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1145 # Step 8: Clean up memoryCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1588 # Step 6: Build entries for output filesCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1651 # Step 7: Normalize and write to diskCOMMENT
LOWnemo/collections/tts/models/magpietts.py1906 # Step 1: Encode text input (always needed)COMMENT
LOWnemo/collections/tts/models/magpietts.py1909 # Step 2: Get and scale attention priorCOMMENT
LOWnemo/collections/tts/models/magpietts.py1914 # Step 3: Process context based on model typeCOMMENT
LOWnemo/collections/tts/models/magpietts.py1962 # Step 5: Apply CTC prior layer filteringCOMMENT
LOWnemo/collections/tts/models/magpietts.py1965 # Step 6: Return typed outputCOMMENT
LOWnemo/collections/tts/models/magpietts.py1936 # Step 4: Dispatch to model-type-specific handlerCOMMENT
Structural Annotation Overuse48 hits · 91 pts
SeverityFileLineSnippetContext
LOW…/collections/speechlm2/parts/metrics/results_logger.py344 # Step 1: Each rank saves its own results with rank suffixCOMMENT
LOW…/collections/speechlm2/parts/metrics/results_logger.py352 # Step 2: Synchronize all ranks before mergingCOMMENT
LOW…/collections/speechlm2/parts/metrics/results_logger.py356 # Step 3: Only rank 0 merges all results and computes final metricsCOMMENT
LOW…/collections/speechlm2/parts/metrics/results_logger.py433 # Step 4: Broadcast metrics from rank 0 to all other ranksCOMMENT
LOW…tions/common/losses/latent_speaker_supervision_loss.py159 # Step 1: Identify speaker token positions and their speaker indices.COMMENT
LOW…tions/common/losses/latent_speaker_supervision_loss.py170 # Step 2: Forward-fill using cummax on position indices.COMMENT
LOW…tions/common/losses/latent_speaker_supervision_loss.py178 # Step 3: Gather the speaker index at the last speaker position.COMMENT
LOWnemo/collections/asr/models/online_diarizer.py546 # Step 1: Get subsegments for embedding extraction.COMMENT
LOWnemo/collections/asr/models/online_diarizer.py579 # Step 3 - Clustering: Perform an online version of clustering algorithmCOMMENT
LOWnemo/collections/asr/models/online_diarizer.py585 # Step 4: Generate RTTM style diarization labels from segment ranges and cluster labelsCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1233 # Step 2: diarize or get GT rttmsCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1251 # Step 3: update diar statesCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1270 # Step 4: find active speakersCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1285 # Step 5: generate instance for active speakersCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1315 # Step 7: ASR forward pass for active speakersCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1337 # Step 8: update ASR statesCOMMENT
LOW…llections/asr/parts/utils/multispk_transcribe_utils.py1352 # Step 9: update seglsts with timestampsCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py689 # Step 1: InitializationCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py699 # Step 2: Get most likely labels for current frameCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py703 # Step 3: Get fusion scoresCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py713 # Step 4: Get most likely labels with fusion scores. Labels that are blank or repeated are ignored.COMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py719 # Step 5: Update labels if they initially weren't blank or repeatedCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py724 # Step 6: Update fusion states and scores for non-blank and non-repeated labelsCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py771 # Step 2: Get most likely labels for current frameCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py776 # Step 3: Get fusion scoresCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py785 # Step 4: Get most likely labels with fusion scores. Labels that are blank or repeated are ignored.COMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py791 # Step 5: Update labels if they initially weren't blank or repeatedCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py799 # Step 6: Update fusion states and scores for non-blank and non-repeated labelsCOMMENT
LOW…ollections/asr/parts/submodules/ctc_greedy_decoding.py745 # Step 1: Initialization for fusion modelsCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1020 # Step 1: Prepare parameters for sentence generationCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1027 # Step 2: Select a speakerCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1040 # Step 3: Generate a sentenceCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1044 # Step 4: Generate a timestamp for either silence or overlapCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1541 # Step 1: Prepare parameters for sentence generationCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1548 # Step 2: Select a speakerCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1563 # Step 3: Generate a sentenceCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1567 # Step 4: Generate a time-stamp for either silence or overlapCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1062 # Step 6: Build entries for output filesCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1132 # Step 7: Normalize and write to diskCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1145 # Step 8: Clean up memoryCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1588 # Step 6: Build entries for output filesCOMMENT
LOWnemo/collections/asr/data/data_simulation.py1651 # Step 7: Normalize and write to diskCOMMENT
LOWnemo/collections/tts/models/magpietts.py1906 # Step 1: Encode text input (always needed)COMMENT
LOWnemo/collections/tts/models/magpietts.py1909 # Step 2: Get and scale attention priorCOMMENT
LOWnemo/collections/tts/models/magpietts.py1914 # Step 3: Process context based on model typeCOMMENT
LOWnemo/collections/tts/models/magpietts.py1962 # Step 5: Apply CTC prior layer filteringCOMMENT
LOWnemo/collections/tts/models/magpietts.py1965 # Step 6: Return typed outputCOMMENT
LOWnemo/collections/tts/models/magpietts.py1936 # Step 4: Dispatch to model-type-specific handlerCOMMENT
Cross-Language Confusion21 hits · 82 pts
SeverityFileLineSnippetContext
HIGH…ration/tests/test_customization_dataset_preparation.py268 assert df_dropped_unnecessary_fields.equals(drop_unrequired_fields(df))CODE
HIGH…ration/tests/test_customization_dataset_preparation.py305 assert df_prompt.equals(CODE
HIGH…ration/tests/test_customization_dataset_preparation.py310 assert df_prompt.equals(convert_into_prompt_completion_only(df_prompt))CODE
HIGH…ration/tests/test_customization_dataset_preparation.py326 assert expected_df.equals(warn_and_drop_long_samples(df, 10000)[0])CODE
HIGHtools/asr_evaluator/utils.py45 raise ValueError("decoder_type could only be null, ctc, rnnt or aed")CODE
HIGHtools/asr_evaluator/utils.py119 f"Hybrid models only support rnnt or ctc decoding! Current decoder_type: {cfg.inference.decoder_typeCODE
HIGHnemo/collections/audio/losses/audio.py86 min_scale || scale * target - estimate ||^2STRING
HIGHnemo/collections/audio/losses/audio.py136 min_filter || conv(filter, target) - estimate ||^2STRING
HIGHnemo/collections/audio/parts/utils/audio.py441 min_scale || scale * target - estimate ||^2STRING
HIGHnemo/collections/audio/parts/utils/audio.py466 min_filter || conv(filter, target) - estimate ||^2STRING
HIGHnemo/collections/asr/parts/utils/vad_utils.py126 "or a list of {'audio_filepath': i, 'offset': 0, 'duration': null}."CODE
HIGHnemo/collections/asr/modules/rnnt_abstract.py147 Stateful prediction of scores and state for a (possibly null) tokenset.STRING
HIGHnemo/collections/asr/modules/rnnt.py709 Stateful prediction of scores and state for a (possibly null) tokenset.STRING
HIGHnemo/collections/asr/data/data_simulation.py1265 orV_rcv (list or null): Microphone orientationsSTRING
HIGHnemo/collections/asr/data/audio_to_label.py1335 "duration": null, # not used, will load the whole audioSTRING
HIGHexamples/speechlm2/nemotron_voicechat_eval.py44 checkpoint_path (str | null)STRING
HIGHexamples/speechlm2/nemotron_voicechat_eval.py50 * inference_speaker_reference (str | null): Path to the reference audio used to condition the speaker's voice. SSTRING
HIGHexamples/asr/asr_adapters/train_asr_adapter.py25 model.adapter.adapter_module_name=<null, or str module. Type: encoder, decoder, joint, or multiple with + between thSTRING
HIGHexamples/asr/asr_adapters/train_asr_adapter.py51 model.adapter.adapter_module_name=<null, or str module. Type: encoder, decoder, joint, or multiple with + between thSTRING
HIGH…s/dataset_processing/g2p/convert_cmu_arpabet_to_ipa.py25 cd NeMo/scripts && python dataset_processing/g2p/convert_cmu_arpabet_to_ipa.pySTRING
HIGHscripts/installers/setup_os2s_decoders.py86 + " >/dev/null 2>/dev/null && rm "CODE
AI Slop Vocabulary35 hits · 66 pts
SeverityFileLineSnippetContext
LOWtools/speech_data_explorer/data_explorer.py1406 # If using tarred audio, just return the filename as-is.COMMENT
LOWnemo/lightning/callback_group.py162 # If we're already inside a wrapped __init__, just call the originalCOMMENT
LOWnemo/core/connectors/save_restore_connector.py430 # artifact is optional and we simply return NoneCOMMENT
LOWnemo/core/classes/common.py1353 # If types are not defined, skip type checks and just call the wrapped methodCOMMENT
LOWnemo/core/optim/optimizers.py143 # If we are provided just a Config object, simply return the dictionary of that objectCOMMENT
LOWnemo/core/optim/optimizers.py155 # simply return the dictionary that was providedCOMMENT
LOWnemo/agents/voice_agent/pipecat/services/nemo/diar.py311 # if diarization is disabled, just pass the frame throughCOMMENT
MEDIUMnemo/collections/speechlm2/models/duplex_ear_tts.py377 # EOS dropout to make the model more robustCOMMENT
MEDIUMnemo/collections/speechlm2/models/duplex_ear_tts.py425 # BOS dropout to make the model more robustCOMMENT
MEDIUMnemo/collections/speechlm2/models/duplex_ear_tts.py446 # BOS dropout to make the model more robustCOMMENT
LOWnemo/collections/speechlm2/modules/speech_generation.py114 # ToDo: move it to cache to need to just create a 1 frame tensor in inferenceCOMMENT
LOWnemo/collections/common/data/dataset.py464 # if min_monolingual fires, it means we will just return a single, original monolingual utteranceCOMMENT
LOWnemo/collections/common/data/lhotse/dataloader.py839 # Bucket duration bins are provided: just use them.COMMENT
MEDIUMnemo/collections/common/data/lhotse/cutset.py1078 # Normalize for robust matchingCOMMENT
LOWnemo/collections/common/data/lhotse/nemo_adapters.py355 # just return self.COMMENT
MEDIUMnemo/collections/audio/data/audio_to_audio_lhotse.py68 # TODO: use fault_tolerant=True for robust loading of targetCOMMENT
MEDIUMnemo/collections/audio/data/audio_to_audio_lhotse.py72 # TODO: use fault_tolerant=True for robust loading of targetCOMMENT
LOW…ctions/asr/models/hybrid_rnnt_ctc_bpe_models_prompt.py350 # RNNT Path - just use encoded outputs directlyCOMMENT
MEDIUMnemo/collections/asr/parts/mixins/diarization.py485 # Be robust to callers accidentally passing "an array of arrays" (dtype=object),COMMENT
MEDIUM…mo/collections/asr/parts/utils/asr_confidence_utils.py404 """Implemented by subclass in order to aggregate token confidence to a word-level confidence.STRING
LOWnemo/collections/asr/parts/submodules/jasper.py143 # simply return symmetric padding for this scenarioCOMMENT
MEDIUMnemo/collections/asr/parts/submodules/ctc_decoding.py804 # If the exact timestep information is available, utilize the 1st non-ctc blank token timestepCOMMENT
LOW…/collections/tts/data/text_to_speech_dataset_lhotse.py378 # If context audio is not available, just use a dummy context_audio_codesCOMMENT
LOWnemo/collections/tts/data/text_to_speech_dataset.py598 # If context audio is not available, just use a dummy context_audio_codesCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1546 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1623 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1650 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1751 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1778 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1868 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWtests/collections/common/test_lhotse_dataloading.py1895 # in this test we'll just use 0.1 for simplicityCOMMENT
LOWexamples/speechlm2/salm_eval.py149 # If no user prompt is provided, just use the audio placeholder.COMMENT
MEDIUM…t/server/parsers/nemotron_toolcall_parser_streaming.py509 # re-set stuff pertaining to progress in the current toolCOMMENT
MEDIUMscripts/tokenizers/conf/tabular_data_tokenizer.yaml9 transform: yeo-johnson # can be ['yeo-johnson', 'quantile', 'robust'], check https://scikit-learn.org/stable/modulCODE
MEDIUM…/speech_recognition/convert_to_tarred_audio_dataset.py31# supplied to the config in order to utilize webdataset for efficient large dataset handling.STRING
Modern AI Meta-Vocabulary11 hits · 31 pts
SeverityFileLineSnippetContext
MEDIUMnemo/collections/asr/models/online_diarizer.py107 # Set speaker embedding model in eval modeCOMMENT
MEDIUM…ns/asr/parts/context_biasing/boosting_graph_batched.py65 1.0 # The score for eos token after detected end of context phrase to prevent hallucination for AED modelsCODE
MEDIUM…collections/asr/parts/submodules/multitask_decoding.py637 hallucinations_detector: bool = True # detect hallucinations in the predicted tokensCODE
MEDIUMnemo/collections/asr/parts/submodules/jasper.py460 # Set default context windowCOMMENT
MEDIUM…parts/submodules/aed_decoding/aed_batched_streaming.py169 # check for hallucinationsCOMMENT
MEDIUM…parts/submodules/aed_decoding/aed_batched_streaming.py303 # check for hallucinationsCOMMENT
MEDIUM…parts/submodules/aed_decoding/aed_batched_streaming.py346 # we need to have at least 8 tokens to run hallucinations detectorCOMMENT
MEDIUMnemo/collections/asr/inference/nmt/llm_translator.py275 # Remove hallucinations if ASR transcript is empty stringCOMMENT
MEDIUMnemo/collections/tts/models/easy_magpietts_inference.py388 # This enables keeping the zero-shot conditioning module private at release time.COMMENT
MEDIUM…odules/magpietts_inference/evaluate_generated_audio.py199 # the embedding model doesn't accept NumPy arrays, so we write to a temporary fileCOMMENT
MEDIUMtests/collections/asr/test_parallel_expert_encoder.py166# _forward_online orchestration (stubbed ASR encoder, provided spk_targets)COMMENT
Fake / Example Data12 hits · 17 pts
SeverityFileLineSnippetContext
LOWtests/setup/data/create_sample_jsonl.py35 "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore "CODE
LOWtests/setup/data/create_sample_jsonl.py35 "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore "CODE
LOWtests/collections/asr/test_text_to_text_dataset.py65 "lorem ipsum dolor sit amet consectetur adipiscing elit",CODE
LOWtests/collections/asr/test_text_to_text_dataset.py65 "lorem ipsum dolor sit amet consectetur adipiscing elit",CODE
LOWtests/collections/asr/test_text_to_text_dataset.py79 "lorem ipsum dolor sit amet consectetur adipiscing elit",CODE
LOWtests/collections/asr/test_text_to_text_dataset.py79 "lorem ipsum dolor sit amet consectetur adipiscing elit",CODE
LOWtests/collections/asr/test_text_to_text_dataset.py80 "Lorem ipsum dolor sit amet, consectetur adipiscing elit.",CODE
LOWtests/collections/asr/test_text_to_text_dataset.py80 "Lorem ipsum dolor sit amet, consectetur adipiscing elit.",CODE
LOWtests/collections/asr/inference/test_bpe_decoder.py49 "lorem ipsum dolor sit amet",CODE
LOWtests/collections/asr/inference/test_bpe_decoder.py49 "lorem ipsum dolor sit amet",CODE
LOWtests/collections/asr/inference/test_bpe_decoder.py77 "lorem ipsum dolor sit amet",CODE
LOWtests/collections/asr/inference/test_bpe_decoder.py77 "lorem ipsum dolor sit amet",CODE
Synthetic Comment Markers1 hit · 8 pts
SeverityFileLineSnippetContext
HIGHnemo/collections/common/data/lhotse/cutset.py772 # as requested by pzelaskoCOMMENT
TODO Padding4 hits · 6 pts
SeverityFileLineSnippetContext
LOWnemo/collections/asr/parts/submodules/subsampling.py460 # TODO: implement lengths inside conv_split_by_channelCOMMENT
LOWnemo/collections/asr/data/text_to_text.py327 # TODO: implement, if we really need normalization inplaceCOMMENT
LOW…s/speaker_tasks/utils/test_data_simul_utils_speaker.py296 # TODO: add tests for all util functionsCOMMENT
LOW…sts/collections/asr/utils/test_data_simul_utils_asr.py296 # TODO: add tests for all util functionsCOMMENT
Example Usage Blocks3 hits · 2 pts
SeverityFileLineSnippetContext
LOWnemo/core/classes/dataset.py84 # Usage:STRING
LOWexamples/asr/speech_classification/frame_vad_infer.py21## Usage:STRING
LOW…/speech_recognition/convert_to_tarred_audio_dataset.py34# Usage:STRING