> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server.

# Audio Concepts

## Docs

- [Curation Pipeline](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/curation-pipeline.md): Modality-level overview of ingest, validation, optional ASR, metrics, filtering, and export
- [Audio Task](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/audio-task.md): Understanding the AudioTask data structure for audio file management and validation in NeMo Curator
- [ASR Pipeline](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/asr-pipeline.md): Comprehensive overview of the automatic speech recognition pipeline architecture and workflow in NeMo Curator
- [Quality Metrics](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/quality-metrics.md): Core concepts for evaluating speech transcription quality using WER, CER, duration analysis, and speech rate metrics
- [Manifests and Ingest](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/manifests-ingest.md): Concepts for constructing manifests and ingesting audio datasets in NeMo Curator
- [ALM Pipeline](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/alm-pipeline.md): Audio Language Model (ALM) data curation pipeline for creating training windows from diarized audio segments
- [Text Integration](https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/text-integration.md): Concepts for integrating audio processing with text curation workflows in multimodal applications


> **Note:** This page contains both a page directory (above) and the landing page content (below). The page directory is generated for agent use and does not appear on the landing page.

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/v26.09/about/concepts/audio/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server.

> Essential concepts for audio data curation including ASR inference, quality assessment, and speech processing workflows

# Audio Curation Concepts

This guide covers the essential concepts for audio data curation in NVIDIA NeMo Curator. These concepts assume basic familiarity with speech processing and machine learning principles.

## Core Concept Areas

Audio curation in NVIDIA NeMo Curator focuses on these key areas:

#### [Audio Curation Pipeline](/about/concepts/audio/curation-pipeline)

Modality-level overview of ingest, validation, optional ASR, metrics, filtering, and export
overview map

#### [AudioTask Structure](/about/concepts/audio/audio-task)

Understanding the AudioTask data structure and audio file management
data-structures validation

#### [ASR Pipeline](/about/concepts/audio/asr-pipeline)

Comprehensive overview of the automatic speech recognition pipeline and workflow
overview architecture

#### [Quality Metrics](/about/concepts/audio/quality-metrics)

Core concepts for evaluating speech transcription quality and audio characteristics
wer cer metrics

#### [Dataset Manifests and Ingest](/about/concepts/audio/manifests-ingest)

Concepts for constructing manifests and ingesting audio datasets
manifests ingest

#### [ALM Pipeline](/about/concepts/audio/alm-pipeline)

Audio Language Model data curation pipeline for extracting training windows from diarized segments
alm windowing speaker-diarization

#### [Text Integration](/about/concepts/audio/text-integration)

Concepts for integrating audio processing with text curation workflows
multimodal integration

## Infrastructure Components

The audio curation concepts build on NVIDIA NeMo Curator's core infrastructure components, which are shared across all modalities. These components include:

#### [Memory Management](/reference/infra/memory-management)

Optimize memory usage when processing large audio datasets
partitioning
batching
monitoring

#### [GPU Acceleration](/reference/infra/gpu-processing)

Leverage NVIDIA GPUs for faster ASR inference and audio processing
cuda
nemo-toolkit
performance

#### [Resumable Processing](/reference/infra/resumable-processing)

Continue interrupted operations across large audio datasets
checkpoints
recovery
batching