Normalize the origin of segmented audio file results

[antonsoo]

Segment.process_file() accepts numeric and string start positions through its underlying Process, then adds the original argument to the returned timedelta values. Calls such as start=0.25 or start="250ms" therefore raise after the audio has been processed.

Use the normalized start time from the Process result when offsetting the segments. This also handles sample-count strings without repeating their conversion and preserves precise Timedelta origins.

Add regression cases for integer/float seconds, unit strings, sample-count strings, missing starts and nanosecond precision.

Validation:

• Full pytest: 678 passed, 54 xfailed, 100% coverage.
• All configured pre-commit checks pass.
• 88 recorded-speech configurations check segmentation timestamps and downstream mean-square features against independent sample-based references. All 24 formerly failing calls complete, and all 64 working control records remain identical.

[sourcery-ai]

Reviewer's Guide

Normalize segment origins through the underlying Process result before combining them with relative segment timestamps, fixing numeric and string starts while preserving precision; add broad regression coverage for supported start types.

Sequence diagram for normalized segmented audio origins

sequenceDiagram
    participant Caller
    participant Segment
    participant Process
    participant Pandas
    participant AudioFormat

    Caller->>Segment: process_file(file, start, end)
    Segment->>Process: process_file(file, start, end, root, process_func_args)
    Process-->>Segment: y with normalized start index
    Segment->>Pandas: y.values[0]
    Segment->>Pandas: y.index.get_level_values(start)[0]
    Segment->>AudioFormat: segmented_index(files, starts=index starts + normalized start)
    AudioFormat-->>Caller: segmented audio results

Loading

File-Level Changes

Change	Details	Files
Offset segmented results using the normalized origin returned by the underlying process.	Retain the complete process result before extracting segment offsets. Read the normalized start level from the returned index and add it to relative segment timestamps. Avoid re-converting sample-count strings and preserve nanosecond-precision timedeltas.	audinterface/core/segment.py
Add regression coverage for all supported segment start representations.	Verify missing, numeric, unit-string, sample-count-string, and Timedelta starts normalize to the expected timestamps. Cover integer and floating-point seconds plus nanosecond precision.	tests/test_segment.py

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[sourcery-ai]

Hey - I've reviewed your changes and they look great!

Sourcery assessment

Approved.

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