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import re
import whisper
from pydub import AudioSegment # For accurate duration calculation
def analyze_fillers(file_path: str, model_size: str = "base", transcript = None ) -> dict:
"""
Analyzes English filler words in audio with proper duration handling.
"""
try:
FILLER_WORDS = [
"um", "uh", "hmm", "ah", "er", "eh",
"umm", "uhh", "mmm", "ahh", "err",
"like", "you know", "well", "so", "actually", "basically",
"right", "okay", "sort of", "kind of"
]
# First get accurate duration using pydub
audio = AudioSegment.from_file(file_path)
duration = len(audio) / 1000 # Convert ms to seconds
if transcript is None:
# Then run Whisper transcription
model = whisper.load_model(model_size)
result = model.transcribe(file_path, word_timestamps=False, fp16=False)
transcript = result["text"]
# Case-insensitive regex matching
pattern = r"(?<!\w)(" + "|".join(map(re.escape, FILLER_WORDS)) + r")(?!\w)"
matches = re.findall(pattern, transcript, re.IGNORECASE)
# Count occurrences
filler_counts = {}
for word in matches:
key = word.lower()
filler_counts[key] = filler_counts.get(key, 0) + 1
total_fillers = sum(filler_counts.values())
# Calculate rate per minute
filler_per_min = (total_fillers / duration) * 60 if duration > 0 else 0
# Scoring
if total_fillers == 0:
filler_score = 100
elif filler_per_min < 1:
filler_score = 90
elif filler_per_min < 3:
filler_score = 80
elif filler_per_min < 5:
filler_score = 60
elif filler_per_min < 10:
filler_score = 40
else:
filler_score = 20
# Generate insight
top_fillers = sorted(filler_counts.items(), key=lambda x: x[1], reverse=True)[:2]
if total_fillers == 0:
insight = "Excellent! No filler words detected."
elif total_fillers <= 2:
insight = f"Minimal fillers ({total_fillers} total), mostly '{top_fillers[0][0]}'."
elif total_fillers <= 5:
examples = ", ".join(f"'{f[0]}'" for f in top_fillers)
insight = f"Moderate fillers ({total_fillers} total), mainly {examples}."
else:
examples = ", ".join(f"'{f[0]}'" for f in top_fillers)
insight = f"Excessive fillers ({total_fillers} total), dominated by {examples}."
return {
"filler_counts": filler_counts,
"total_fillers": total_fillers,
"filler_score": filler_score,
"filler_rate_per_min": round(filler_per_min, 1),
}
except Exception as e:
raise RuntimeError(f"Analysis failed: {str(e)}") |