feature/speaker-classifier-apply-function #197
Conversation
evamaxfield
added
enhancement
| transcript: Union[str, Path, Transcript], | ||
| audio: Union[str, Path, AudioSegment], | ||
| model: str = DEFAULT_MODEL, | ||
| min_intra_sentence_chunk_duration: float = 0.5, |
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Do you think 0.5 is enough for a minimum? For certain things like roundtable one-word answers like 'yes/no' I could see it taking slightly less than half a second
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I think this is one of those things of... "is it valuable to the downstream analysis"
anything less than 0.5 seconds to me seems non-valuable to tag.
Though I am trying too find a better justification for this. Part of the reason is that I only trained on data that ranged from 0.5 - 2 seconds. And that was because anything less than 0.5 seconds worried me for "how much data to predict with" the smaller the clip the less information to use.
| model: str = DEFAULT_MODEL, | ||
| min_intra_sentence_chunk_duration: float = 0.5, | ||
| max_intra_sentence_chunk_duration: float = 2.0, | ||
| min_sentence_mean_confidence: float = 0.985, |
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Is this based on the confidence you've been seeing with the existing model you trained?
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Hah. Great question and one that I am struggling with. Right now, this is 0.985 is based off: "i tried a bunch of different thresholds and this one seemed good" but I would love to know if there is a formula from confidence -> p-value??
| The maximum duration for a sentences audio to split to. This should match | ||
| whatever was used during model training | ||
| (i.e. trained on 2 second audio chunks, apply on 2 second audio chunks) | ||
| Default: 2 seconds |
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What's the reasoning behind this?
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Originally couldn't fit more than 2 seconds of audio into GPU during training.
Codecov Report
@@ Coverage Diff @@
## main #197 +/- ##
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- Coverage 93.38% 91.24% -2.15%
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Files 51 53 +2
Lines 2677 2740 +63
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Hits 2500 2500
- Misses 177 240 +63
Continue to review full report at Codecov.
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Link to Relevant Issue
WIP #131
Description of Changes
Include a description of the proposed changes.
Adds the function to annotate a transcript with a trained speakerbox model!
I am already using this and applying the highest accuracy model for Seattle to all of 2021-01-01 to 2022-01-01 data: