pyflink.multimodal.expression.AudioExpressionAccessor.split_by_speech#

AudioExpressionAccessor.split_by_speech(speech_activity: pyflink.table.expression.Expression, *, segment_type: Literal['audio', 'ref'] = 'audio', pre_padding_ms: int = 0, post_padding_ms: int = 0, merge_gap_ms: int = 0, min_segment_ms: int = 0, max_segment_ms: Optional[int] = None, max_segments: int = 1024, concurrency: Optional[int] = None) → pyflink.dataframe.udf.DataFrameUDTFCall[source]#

Split audio using caller-provided speech activity ranges.

Equivalent to audio_split_by_speech(). See that function for full parameter details.

Parameters

speech_activity – Expression containing speech ranges, such as output from audio_detect_speech.
segment_type – "audio" for waveform segments or "ref" for lazy reference segments.
pre_padding_ms – Milliseconds to extend before each speech range.
post_padding_ms – Milliseconds to extend after each speech range.
merge_gap_ms – Merge adjacent ranges separated by this gap or less.
min_segment_ms – Drop segments shorter than this value.
max_segment_ms – Optional maximum segment length.
max_segments – Maximum allowed number of returned segments.
concurrency – Optional execution concurrency for this operation. None uses the framework default.

Returns

A DataFrame UDTF call for join_lateral. Alias the output as one segment column before consuming it downstream.