Sebastjan Artič
8512076b91
- New analyze.py: full transcript + energy + structural analysis - Smart clip range: includes pre-chorus, can exceed 30s up to max_duration (default 45s) - Audio fade in/out: auto-detected from vocal boundaries - Instrumental detection: auto-disables subs if vocals < 10% of duration - Multi-language: auto-detect via Whisper or explicit (DE/SL/HR/BS/SR/EN/IT/ES/FR) - Frontend: cleaner UX, added bs language, smart selection description - reframe.py: --fade-in --fade-out args - clip.py: propagates fade params - app/main.py: replaces find_chorus.py call with analyze.py
468 lines
15 KiB
Python
468 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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analyze.py — Predhodna analiza CELEGA videa pred trim-anjem.
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Naredi:
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1. Whisper transcript celega videa (auto-detect jezika ali user-specified)
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2. Energy profile (RMS dB na 1s windows)
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3. Structural detection (vocal/instrumental sections, energy peaks)
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4. Pametno izbere clip range (lahko >30s, vključi pre-chorus)
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5. Detekcija instrumentalnih pesmi (no_subs auto)
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Output: JSON s podatki za clip.py
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"""
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import argparse
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import json
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import os
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import re
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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def get_video_duration(path):
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r = subprocess.run(
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["ffprobe", "-v", "error", "-show_entries", "format=duration",
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"-of", "default=nw=1:nokey=1", str(path)],
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capture_output=True, text=True
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)
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try:
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return float(r.stdout.strip())
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except ValueError:
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return 0.0
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def extract_audio(video_path):
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"""Extract avdio v 16kHz mono WAV za Whisper + energy."""
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audio = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
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audio.close()
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subprocess.run(
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["ffmpeg", "-y", "-i", str(video_path), "-vn",
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"-ac", "1", "-ar", "16000", "-c:a", "pcm_s16le", audio.name],
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check=True, capture_output=True
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)
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return audio.name
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def transcribe_full(audio_path, lang=None, model_size="small"):
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"""Whisper transcript celega avdia. lang=None → auto-detect."""
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from faster_whisper import WhisperModel
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print(f"🧠 Whisper {model_size}, lang={lang or 'auto'}", file=sys.stderr)
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m = WhisperModel(model_size, device="cpu", compute_type="int8")
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segs, info = m.transcribe(
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audio_path,
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language=lang,
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word_timestamps=True,
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vad_filter=True,
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)
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detected_lang = info.language
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detected_prob = info.language_probability
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print(f" Detekcija: {detected_lang} (p={detected_prob:.2f})", file=sys.stderr)
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segments = []
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for s in segs:
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words = []
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if s.words:
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for w in s.words:
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words.append({
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"start": w.start,
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"end": w.end,
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"text": w.word,
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})
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segments.append({
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"start": s.start,
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"end": s.end,
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"text": s.text.strip(),
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"words": words,
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})
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return {
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"language": detected_lang,
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"language_probability": detected_prob,
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"segments": segments,
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}
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def compute_energy_profile(audio_path, window_sec=1.0):
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"""RMS dB na window_sec sekund. Vrne list (timestamp, rms_db)."""
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cmd = [
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"ffmpeg", "-i", audio_path,
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"-af", f"asetnsamples=n={int(16000 * window_sec)}:p=0,"
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f"astats=metadata=1:reset={window_sec},"
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f"ametadata=print:key=lavfi.astats.Overall.RMS_level:file=-",
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"-f", "null", "-",
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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output = result.stdout + "\n" + result.stderr
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energies = []
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current_pts = 0.0
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for line in output.split("\n"):
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line = line.strip()
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m = re.search(r"pts_time:(\S+)", line)
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if m:
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try:
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current_pts = float(m.group(1))
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except ValueError:
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pass
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continue
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if "RMS_level=" in line:
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val = line.split("RMS_level=")[-1].strip()
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try:
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rms = float(val)
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# -inf zamenjamo z -90
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if rms < -90 or rms != rms: # NaN check
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rms = -90.0
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energies.append((current_pts, rms))
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current_pts += window_sec
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except ValueError:
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pass
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return energies
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def detect_vocal_sections(segments, max_gap=3.0):
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"""Združi consecutive segmente v "vokalne sekcije"."""
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if not segments:
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return []
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sections = []
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current = {
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"start": segments[0]["start"],
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"end": segments[0]["end"],
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"segments": [segments[0]],
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"text": segments[0]["text"],
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}
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for seg in segments[1:]:
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if seg["start"] - current["end"] > max_gap:
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sections.append(current)
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current = {
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"start": seg["start"],
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"end": seg["end"],
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"segments": [seg],
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"text": seg["text"],
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}
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else:
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current["end"] = seg["end"]
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current["segments"].append(seg)
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current["text"] += " " + seg["text"]
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sections.append(current)
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return sections
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def avg_energy_in_range(energies, start, end):
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"""Povprečna RMS v rangeu."""
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vals = [r for (t, r) in energies if start <= t <= end]
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if not vals:
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return -90.0
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return sum(vals) / len(vals)
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def score_section_as_chorus(section, all_sections, energies, avg_rms):
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"""Score sekcijo kot kandidat za refren.
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Faktorji:
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- Ponavljajoče besede (low unique-word-ratio) = refren
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- Visoka energija
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- Sekcija se pojavi večkrat v pesmi (refren se ponovi)
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- Krajše vrstice (3-8 besed)
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"""
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text = section["text"].lower()
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words = re.findall(r"\b\w+\b", text)
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if not words:
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return 0
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unique_ratio = len(set(words)) / len(words)
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# Refren = nizko unique ratio (ponovitve)
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chorus_signal = max(0, (1.0 - unique_ratio) * 30)
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# Energija
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sec_energy = avg_energy_in_range(energies, section["start"], section["end"])
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energy_above = max(0, sec_energy - avg_rms)
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energy_score = energy_above * 8
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# Kako pogosto se pojavi podobno besedilo
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repeat_count = 0
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for other in all_sections:
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if other is section:
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continue
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other_text = other["text"].lower()
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other_words = set(re.findall(r"\b\w+\b", other_text))
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common = set(words) & other_words
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# Če imata >50% besed skupnih, je verjetno isti refren
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if len(common) >= len(set(words)) * 0.5 and len(common) >= 3:
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repeat_count += 1
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repeat_score = repeat_count * 25
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# Dolžina vrstice
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duration = section["end"] - section["start"]
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if 3 <= duration <= 25:
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length_score = 10
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elif duration > 25:
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length_score = 5
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else:
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length_score = 2
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return chorus_signal + energy_score + repeat_score + length_score
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def find_chorus(transcript, energies, video_duration):
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"""Najde najbolj verjeten refren."""
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sections = detect_vocal_sections(transcript["segments"])
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if not sections:
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return None
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avg_rms = sum(r for (_, r) in energies) / len(energies) if energies else -30.0
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candidates = []
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for sec in sections:
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score = score_section_as_chorus(sec, sections, energies, avg_rms)
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candidates.append({
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"start": sec["start"],
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"end": sec["end"],
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"duration": sec["end"] - sec["start"],
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"text_preview": sec["text"][:80],
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"score": round(score, 2),
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"avg_rms": round(avg_energy_in_range(energies, sec["start"], sec["end"]), 2),
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})
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# Sort by score descending
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candidates.sort(key=lambda c: -c["score"])
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if not candidates:
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return None
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return {
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"best": candidates[0],
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"all_candidates": candidates[:10],
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"avg_rms_total": round(avg_rms, 2),
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}
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def smart_clip_range(chorus, transcript, video_duration,
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target_duration=30, max_duration=45, min_duration=20):
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"""Inteligentno določi clip range.
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Logika:
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1. Začni z refrenom kot core
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2. Če je krajši od min_duration, razširi na obeh straneh
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3. Če imamo prostor, dodaj pre-chorus pred refrenom
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4. Cap na max_duration
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"""
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if not chorus or not chorus.get("best"):
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# Fallback: vzemi sredino videa
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mid = video_duration / 2
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start = max(0, mid - target_duration / 2)
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return {
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"start": start,
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"end": min(video_duration, start + target_duration),
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"reason": "fallback_middle",
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}
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best = chorus["best"]
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sections = detect_vocal_sections(transcript["segments"])
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actual_start = best["start"]
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actual_end = best["end"]
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# 1. Če je core refren prekratek, razširi
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if actual_end - actual_start < min_duration:
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# Najdi naslednjo sekcijo (verjetno se refren ponovi)
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for sec in sections:
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if sec["start"] > actual_end and sec["start"] - actual_end < 5:
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# Sekcija blizu, dodaj jo
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if sec["end"] - actual_start <= max_duration:
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actual_end = sec["end"]
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if actual_end - actual_start >= min_duration:
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break
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# 2. Dodaj pre-chorus pred refrenom (build-up)
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pre_section = None
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for sec in sections:
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if sec["end"] <= actual_start and actual_start - sec["end"] < 8:
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pre_section = sec # zadnja pred refrenom
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if pre_section:
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candidate_start = pre_section["start"]
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if actual_end - candidate_start <= max_duration:
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actual_start = candidate_start
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# 3. Če je res prekratek, razširi simetrično
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if actual_end - actual_start < min_duration:
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deficit = min_duration - (actual_end - actual_start)
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actual_start = max(0, actual_start - deficit / 2)
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actual_end = min(video_duration, actual_end + deficit / 2)
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# 4. Trim na max
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if actual_end - actual_start > max_duration:
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actual_end = actual_start + max_duration
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# Snap to video bounds
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actual_start = max(0, actual_start)
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actual_end = min(video_duration, actual_end)
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return {
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"start": round(actual_start, 2),
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"end": round(actual_end, 2),
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"duration": round(actual_end - actual_start, 2),
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"reason": "smart_chorus_with_prebuild",
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"chorus_start": round(best["start"], 2),
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"chorus_end": round(best["end"], 2),
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}
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def detect_audio_fade(clip_range, transcript):
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"""Določi fade-in/fade-out trajanje.
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Logika:
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- Če clip začne sredi vokala → 0.5s fade in
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- Če se konča sredi vokala → 1.0s fade out
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- Sicer manj fade
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"""
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cs, ce = clip_range["start"], clip_range["end"]
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# Vokal pri začetku?
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starts_in_vocal = False
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ends_in_vocal = False
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for seg in transcript["segments"]:
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# Začetek clip-a znotraj segmenta
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if seg["start"] <= cs <= seg["end"]:
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starts_in_vocal = True
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# Konec clip-a znotraj segmenta
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if seg["start"] <= ce <= seg["end"]:
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ends_in_vocal = True
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fade_in = 0.5 if starts_in_vocal else 0.2
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fade_out = 1.5 if ends_in_vocal else 0.3
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return {"fade_in": fade_in, "fade_out": fade_out}
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def is_instrumental(transcript, video_duration, threshold=0.1):
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"""Detekcija ali je pesem instrumentalna.
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Če je vsota trajanja vokalnih segmentov < threshold * video_duration,
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je pesem instrumentalna.
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"""
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if not transcript.get("segments"):
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return True
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vocal_duration = sum(
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s["end"] - s["start"] for s in transcript["segments"]
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)
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ratio = vocal_duration / max(video_duration, 1)
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return ratio < threshold
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("video", help="Vhod video file")
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ap.add_argument("--lang", default=None, help="ISO 639-1 ali 'auto' (default: auto)")
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ap.add_argument("--model", default="small", help="Whisper model")
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ap.add_argument("--target-duration", type=float, default=30.0)
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ap.add_argument("--max-duration", type=float, default=45.0)
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ap.add_argument("--min-duration", type=float, default=20.0)
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ap.add_argument("--json", action="store_true", help="Output JSON")
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ap.add_argument("--output", help="Path za JSON output")
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args = ap.parse_args()
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video = Path(args.video)
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if not video.exists():
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print(f"❌ Video ne obstaja: {video}", file=sys.stderr)
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sys.exit(1)
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duration = get_video_duration(video)
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print(f"📹 Video: {video.name}, {duration:.1f}s", file=sys.stderr)
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# 1. Extract avdio
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audio = extract_audio(video)
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try:
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# 2. Whisper transcript
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lang = None if args.lang in (None, "auto", "") else args.lang
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transcript = transcribe_full(audio, lang=lang, model_size=args.model)
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print(f" Transkripcija: {len(transcript['segments'])} segmentov", file=sys.stderr)
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# 3. Energy profile
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print(f"⚡ Energy profile...", file=sys.stderr)
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energies = compute_energy_profile(audio)
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print(f" Energy samples: {len(energies)}", file=sys.stderr)
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# 4. Instrumental detection
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instrumental = is_instrumental(transcript, duration)
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print(f"🎵 Instrumentalna: {instrumental}", file=sys.stderr)
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# 5. Find chorus (samo če ni instrumental)
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if not instrumental:
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chorus = find_chorus(transcript, energies, duration)
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else:
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# Za instrumentalne: najdi sekcijo z najvišjo energijo
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window = args.target_duration
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best_start = 0
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best_avg = -100
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t = 0
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while t + window <= duration:
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avg = avg_energy_in_range(energies, t, t + window)
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if avg > best_avg:
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best_avg = avg
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best_start = t
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t += 5 # step 5s
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chorus = {
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"best": {
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"start": best_start,
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"end": best_start + window,
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"duration": window,
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"text_preview": "(instrumental — energy peak)",
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"score": 0,
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"avg_rms": round(best_avg, 2),
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},
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"all_candidates": [],
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"avg_rms_total": round(
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sum(r for (_, r) in energies) / len(energies) if energies else -30, 2
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),
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}
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# 6. Smart clip range
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clip_range = smart_clip_range(
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chorus, transcript, duration,
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target_duration=args.target_duration,
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max_duration=args.max_duration,
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min_duration=args.min_duration,
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)
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print(f"✂ Clip range: {clip_range['start']:.1f}s - {clip_range['end']:.1f}s "
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f"(duration: {clip_range['duration']}s)", file=sys.stderr)
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# 7. Fade params
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fade = detect_audio_fade(clip_range, transcript)
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print(f"🎚 Fade: in={fade['fade_in']}s, out={fade['fade_out']}s", file=sys.stderr)
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result = {
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"video": str(video),
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"video_duration": duration,
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"language": transcript["language"],
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"language_probability": transcript["language_probability"],
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"instrumental": instrumental,
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"transcript": transcript,
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"chorus": chorus,
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"clip_range": clip_range,
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"fade": fade,
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}
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if args.output:
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with open(args.output, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f"💾 Saved: {args.output}", file=sys.stderr)
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if args.json:
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print(json.dumps(result, ensure_ascii=False))
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finally:
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try:
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os.unlink(audio)
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except Exception:
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pass
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if __name__ == "__main__":
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main()
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