> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/alm/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server.

# ALM Data Curation

## Docs

- [Data Builder](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/alm/data-builder.md): ALMDataBuilderStage reference for constructing training windows from diarized audio segments with quality filtering
- [Overlap Filtering](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/alm/overlap-filtering.md): ALMDataOverlapStage reference for removing overlapping training windows based on configurable thresholds


> **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/v26.04/curate-audio/process-data/alm/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server.

> ALM data curation stages for constructing and filtering training windows from diarized audio segments

# ALM Data Curation

Curate training data for audio language models by extracting fixed-duration windows from diarized audio segments. The ALM stages read JSONL manifests, build candidate windows that meet quality constraints, remove overlapping windows, and write the filtered results.

## How it Works

The ALM pipeline processes audio manifests through a four-stage chain:

1. **ALMManifestReader** reads JSONL manifests line-by-line, producing one `AudioTask` per entry
2. **ALMDataBuilderStage** constructs candidate windows from consecutive segments, applying sample rate, bandwidth, speaker count, and duration constraints
3. **ALMDataOverlapStage** removes windows that share too much audio content, keeping windows closest to the target duration
4. **ALMManifestWriterStage** writes filtered results as JSONL

All stages run on CPU and support both Xenna and Ray Data backends.

## ALM Stages

#### [ALM Data Builder](/curate-audio/process-data/alm/data-builder)

Construct candidate training windows from diarized audio segments with quality filtering
windowing
speaker-count
bandwidth

#### [ALM Overlap Filtering](/curate-audio/process-data/alm/overlap-filtering)

Remove redundant overlapping windows based on configurable thresholds
deduplication
overlap-ratio
target-duration

## Quick Example

```python
from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.audio.alm import (
    ALMManifestReader,
    ALMDataBuilderStage,
    ALMDataOverlapStage,
    ALMManifestWriterStage,
)

pipeline = Pipeline(name="alm_curation")

# Read input manifests
pipeline.add_stage(ALMManifestReader(manifest_path="/data/manifests/"))

# Build 120-second training windows
pipeline.add_stage(
    ALMDataBuilderStage(
        target_window_duration=120.0,
        tolerance=0.1,
        min_speakers=2,
        max_speakers=5,
    )
)

# Remove windows with more than 50% overlap
pipeline.add_stage(
    ALMDataOverlapStage(
        overlap_percentage=50,
        target_duration=120.0,
    )
)

# Write results
pipeline.add_stage(ALMManifestWriterStage(output_path="/data/output/alm.jsonl"))
```

## Related Topics

* **[ALM Pipeline Concepts](/about/concepts/audio/alm-pipeline)**: Architectural overview of the ALM pipeline
* **[ALM Tutorial](/curate-audio/tutorials/alm)**: Step-by-step guide with sample data
* **[Manifests and Ingest](/about/concepts/audio/manifests-ingest)**: General manifest format concepts