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from .vers import calc_vers
import librosa
import numpy as np
import math
from .filler_analyzer import detect_fillers
from .find_valence import get_valence_score
from filler_count.filler_score import analyze_fillers
import pyworld
def compute_vers_score(file_path: str, whisper_model, filler_count = None) -> dict:
"""
Compute VERS (Vocal Emotional Regulation Score) and its components from a speech sample.
"""
result = whisper_model.transcribe(file_path, word_timestamps=False, fp16=False)
transcript = result.get("text", "").strip()
segments = result.get("segments", [])
if filler_count is None:
# Filler count
result = analyze_fillers(file_path,'base', transcript)
filler_count = result.get("filler_count", 0)
# Load audio
y, sr = librosa.load(file_path, sr=None)
duration = len(y) / sr if sr else 0.0
# Volume (RMS)
rms = librosa.feature.rms(y=y)[0]
mean_rms = float(np.mean(rms))
mean_volume_db = 20 * math.log10(mean_rms + 1e-6) if mean_rms > 0 else -80.0
volume_std = np.std(20 * np.log10(rms + 1e-6))
# Max volume
vol_max = np.max(np.abs(y)) if y.size > 0 else 0.0
vol_max_db = 20 * math.log10(vol_max + 1e-6) if vol_max > 0 else -80.0
# Calculate pitch variation (in semitones) using pyworld
_f0, t = pyworld.harvest(y.astype(np.float64), sr, f0_floor=80.0, f0_ceil=400.0, frame_period=1000 * 256 / sr)
f0 = pyworld.stonemask(y.astype(np.float64), _f0, t, sr)
voiced_f0 = f0[f0 > 0]
voiced_f0 = voiced_f0[
(voiced_f0 > np.percentile(voiced_f0, 5)) &
(voiced_f0 < np.percentile(voiced_f0, 95))
]
pitch_variation = 0.0
if voiced_f0.size > 0:
median_f0 = np.median(voiced_f0)
median_f0 = max(median_f0, 1e-6)
semitone_diffs = 12 * np.log2(voiced_f0 / median_f0)
pitch_variation = float(np.std(semitone_diffs))
# Pause analysis
total_speaking_time = 0.0
long_pause_count = 0
if segments:
for seg in segments:
total_speaking_time += (seg["end"] - seg["start"])
for i in range(len(segments) - 1):
pause_dur = segments[i+1]["start"] - segments[i]["end"]
if pause_dur > 1.0:
long_pause_count += 1
first_start = segments[0]["start"]
last_end = segments[-1]["end"]
if first_start > 1.0:
long_pause_count += 1
if duration - last_end > 1.0:
long_pause_count += 1
# WPM
words = transcript.split()
word_count = len(words)
words_per_min = (word_count / duration) * 60.0 if duration > 0 else 0.0
valence_scores = get_valence_score(file_path)
# Calculate VERS
vers_result = calc_vers(
filler_count=filler_count,
long_pause_count=long_pause_count,
pitch_variation=pitch_variation,
mean_volume_db=mean_volume_db,
vol_max_db=vol_max_db,
wpm=words_per_min,
volume_std=volume_std,
valence_scores=valence_scores
)
return vers_result
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