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

Quick Example

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from nemo_curator.pipeline import Pipeline
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from nemo_curator.stages.audio.alm import (
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    ALMManifestReader,
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    ALMDataBuilderStage,
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    ALMDataOverlapStage,
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    ALMManifestWriterStage,
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)
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pipeline = Pipeline(name="alm_curation")
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# Read input manifests
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pipeline.add_stage(ALMManifestReader(manifest_path="/data/manifests/"))
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# Build 120-second training windows
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pipeline.add_stage(
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    ALMDataBuilderStage(
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        target_window_duration=120.0,
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        tolerance=0.1,
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        min_speakers=2,
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        max_speakers=5,
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    )
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)
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# Remove windows with more than 50% overlap
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pipeline.add_stage(
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    ALMDataOverlapStage(
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        overlap_percentage=50,
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        target_duration=120.0,
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    )
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)
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# Write results
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pipeline.add_stage(ALMManifestWriterStage(output_path="/data/output/alm.jsonl"))

Related Topics

• ALM Pipeline Concepts: Architectural overview of the ALM pipeline
• ALM Tutorial: Step-by-step guide with sample data
• Manifests and Ingest: General manifest format concepts