lesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copylesson 1 of 3 · the voice fingerprint — a client's voice, measured from their own copythe voice fingerprint — a client's voice, measured from their own copy
Checkpoint
One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Checkpoint: all three Windrow samples are fingerprinted. Write
derive_pack(prints) that turns the measurements into the rules
section of the voice pack:
house_avg: mean of the samples'avg_words, rounded to 1 decimal.max_sentence_words:round(house_avg * 1.5)— using the rounded house_avg.exclamations_allowed: True if ANY sample hasexclaims > 0.contractions_required: True if the pooled contraction rate — total contractions across the samples divided by total sentences — is at least 0.2. Same threshold as step 5, now computed from the raw counts.
Expected output:
house avg: 10.2 words/sentence
max_sentence_words: 15
exclamations_allowed: False
contractions_required: True