mirror of
https://github.com/yusufipk/dikte.git
synced 2026-09-11 19:06:11 +00:00
yusufipek
2f59029261
Twenty-two files at the top of the tree was the first thing anybody saw of this repository. They are one package now, imported relatively, and the three scripts that are not the front door moved under scripts/. install.sh stays where the README has always said it is. What starts the application is dikte/__main__.py: python3 -m dikte runs it, and so does naming the file, which is what the launcher symlink, both .desktop files, the macOS bundle and every registered shortcut do. Run by path there is no package around it, so it puts the checkout on sys.path itself. The installers now keep the keys you chose when they are given none, which is what an update is: update.sh no longer has to read them out and pass them back. An updater from before this commit cannot read them at all, so the one thing it can say, the default key with an empty discard key, is read as "nothing was asked for" rather than obeyed. That guard can go once nobody is updating across this commit.
111 lines
4.2 KiB
Python
111 lines
4.2 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.
|
||
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", "you", "bye",
|
||
"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
|