mirror of
https://github.com/yusufipk/dikte.git
synced 2026-09-11 19:06:11 +00:00
huseyin-emre-tigciandClaude Fable 5
e8147f49f8
doctor judged the local provider by the API key it does not use, so a fully local machine always saw a red mark, and it crashed outright with local cleanup picked; both lines now ask readiness. config list printed the Groq key in plaintext while masking the other two. The one subprocess decoded with the locale codepage gets its UTF-8 back, so a Turkish filename cannot hang a file transcription, and a redirected stdout on Windows replaces what it cannot encode instead of failing after the work succeeded. meeting-cancel stops advertising a --wait the server never honoured. "you" and "bye" leave the hallucination list: people dictate them. The minutes stage failing no longer burns the transcription checkpoint, and an untouched meeting-length dial no longer rewrites a value the command line set in seconds. A prompt box compared against the wrong language's default after a switch no longer fossilizes the old default as a custom prompt. The 67 strings of the local-model box, the whole first-run screen of the shipped default, get their Turkish. The hub cache moves to the platform's cache directory instead of ~/.cache on every system; the old directory is a few orphaned kilobytes with a six-hour shelf life. The last NO_WINDOW spellings collapse into the constant paths already carries, one windowed-executable lookup, one session-file reader, one install-record reader, one download progress signal carrying its destination, and the KDE conflict scan loses the branch its other branch already covered. Co-Authored-By: Claude Fable 5 <[email protected]>
113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
"""Deciding whether a recording actually contains speech.
|
||
|
||
Absolute thresholds don't travel between machines: one laptop's built-in mic
|
||
sits at -70 dBFS when the room is quiet, another clips the same room at -35.
|
||
So the main test is relative: speech has to rise clearly above *this
|
||
recording's own* noise floor, and it has to last long enough to be a word.
|
||
|
||
The transcription models are the reason this matters: fed near-silence they
|
||
don't return an empty string, they invent one. Whisper is famous for it
|
||
("Thanks for watching", "Altyazı M.K."), which is what the phrase list below
|
||
catches as a second line of defence.
|
||
"""
|
||
|
||
import math
|
||
import re
|
||
import unicodedata
|
||
|
||
# Stock phrases the models produce when handed silence. Kept deliberately
|
||
# narrow: only sentences nobody dictates on purpose in a two-second clip.
|
||
# Whisper does invent "you" and "bye" too, but people dictate both as whole
|
||
# answers, so a single word never belongs here.
|
||
HALLUCINATIONS = {
|
||
"altyazi mk", "altyazi m k", "altyazi", "altyazilar",
|
||
"abone olmayi unutmayin", "izlediginiz icin tesekkurler",
|
||
"izlediginiz icin tesekkur ederim", "izlediginiz icin tesekkur ederiz",
|
||
"kanalima abone olmayi unutmayin", "altyazi mk altyazi mk",
|
||
"thanks for watching", "thank you for watching", "thanks for watching!",
|
||
"please subscribe", "subscribe to my channel",
|
||
"mbc masr", "sous titres realises par la communaute damara org",
|
||
"amara org community", "sous titrage st 501",
|
||
}
|
||
_PUNCTUATION = re.compile(r"[^\w\s]", re.UNICODE)
|
||
_SPACES = re.compile(r"\s+")
|
||
|
||
|
||
def to_db(value):
|
||
return 20 * math.log10(value) if value > 0 else -120.0
|
||
|
||
|
||
def _percentile(values, fraction):
|
||
if not values:
|
||
return 0.0
|
||
index = min(len(values) - 1, max(0, int(len(values) * fraction)))
|
||
return values[index]
|
||
|
||
|
||
def analyse(rms_values, chunk_seconds, margin_db=10.0):
|
||
"""Turn per-chunk RMS levels into the numbers the decision needs."""
|
||
if not rms_values:
|
||
return {"noise_db": -120.0, "speech_db": -120.0,
|
||
"dynamic_db": 0.0, "voiced_seconds": 0.0}
|
||
|
||
ordered = sorted(rms_values)
|
||
noise = _percentile(ordered, 0.10)
|
||
speech = _percentile(ordered, 0.90)
|
||
noise_db, speech_db = to_db(noise), to_db(speech)
|
||
|
||
# Anything this far above the recording's own floor counts as voice.
|
||
gate_db = noise_db + margin_db
|
||
voiced = sum(1 for value in rms_values if to_db(value) >= gate_db)
|
||
|
||
return {
|
||
"noise_db": noise_db,
|
||
"speech_db": speech_db,
|
||
"dynamic_db": speech_db - noise_db,
|
||
"voiced_seconds": voiced * chunk_seconds,
|
||
}
|
||
|
||
|
||
def is_silent(stats, silence_db=-55.0, margin_db=10.0, min_voiced_seconds=0.3):
|
||
"""True when the recording holds no speech worth sending to the API.
|
||
|
||
Three independent reasons, any one of which is enough:
|
||
* the loud end of the recording is below the absolute floor
|
||
* nothing rose far enough above the noise floor for long enough
|
||
* the level never moved, meaning steady hiss, hum or fan noise
|
||
"""
|
||
if stats["speech_db"] < silence_db:
|
||
return True
|
||
if stats["voiced_seconds"] < min_voiced_seconds:
|
||
return True
|
||
# Only distrust flat dynamics near the floor; a loud, evenly-spoken
|
||
# sentence legitimately has a narrow range.
|
||
if stats["speech_db"] < silence_db + 12 and stats["dynamic_db"] < margin_db * 0.6:
|
||
return True
|
||
return False
|
||
|
||
|
||
def _normalise(text):
|
||
folded = unicodedata.normalize("NFKD", text.lower())
|
||
folded = "".join(c for c in folded if not unicodedata.combining(c))
|
||
folded = folded.replace("ı", "i").replace("ş", "s").replace("ğ", "g")
|
||
return _SPACES.sub(" ", _PUNCTUATION.sub("", folded)).strip()
|
||
|
||
|
||
def looks_like_hallucination(text, duration_seconds, max_duration=6.0):
|
||
"""A stock phrase returned for a short clip is almost certainly invented."""
|
||
if duration_seconds > max_duration:
|
||
return False
|
||
normalised = _normalise(text)
|
||
if not normalised:
|
||
return True
|
||
if normalised in HALLUCINATIONS:
|
||
return True
|
||
# "Altyazı M.K. Altyazı M.K. Altyazı M.K.": the same stock line repeated.
|
||
words = normalised.split()
|
||
for phrase in HALLUCINATIONS:
|
||
parts = phrase.split()
|
||
if len(parts) >= 2 and words and len(words) % len(parts) == 0:
|
||
if " ".join(words) == " ".join(parts * (len(words) // len(parts))):
|
||
return True
|
||
return False
|