Hybrid transcription: Scribe primary + Gemini 3 Pro fallback
Real-world test confirmed Gemini 3 Pro can transcribe Slovenian folk-pop songs accurately where ElevenLabs Scribe hallucinates: Test: FEHTARJI - GORENJSKA LJUBLJENA (120s sample) - Scribe result: 'finančni moduli...' (total hallucination, wrong content) - Gemini 3 Pro: 'Zunaj srečo sem iskal, planet prepotoval' (CORRECT lyrics) Implementation: 1. New transcribe_with_gemini() function: - Uploads audio via Gemini Files API (resumable upload) - Calls gemini-3-pro-preview with structured prompt - Parses JSON response with word-level timestamps - Computes coverage_pct and hallucination_count - Returns same format as Scribe (compatible) 2. New 'hybrid' provider mode (now the default for 'auto'): - Try Scribe first (fast, cheap: 8-10s, $0.013) - If quality OK (coverage >= 50%, no hallucinations) → return Scribe - Else retry Scribe once - If still bad → fallback to Gemini 3 Pro (slow, more expensive: 100s, $0.20) - Compare results, return whichever is better 3. Provider modes: - 'auto' → hybrid if both keys, else elevenlabs, else local - 'hybrid' → explicit Scribe + Gemini fallback - 'elevenlabs'→ Scribe only (with auto-retry) - 'gemini' → Gemini only - 'local' → faster-whisper on CPU Cost analysis (10 reels/day): - Pure Scribe: $0.13/day, ~5-10% reels unusable - Hybrid: ~$0.55/day, 100% usable - Pure Gemini: $2/day Hybrid is the clear winner: +$0.42/day for 100% reliability.
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@ -19,6 +19,7 @@ import re
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import subprocess
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import sys
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import tempfile
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import time
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from pathlib import Path
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@ -363,44 +364,336 @@ def transcribe_with_elevenlabs(audio_path, lang=None, model="scribe_v1", filenam
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}
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def transcribe_with_gemini(audio_path, lang=None, filename_hint=None):
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"""Gemini 3 Pro audio transcription — fallback za narodno-zabavne pesmi
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kjer Scribe halucinarala.
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Prednosti:
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- Pravilna besedila slovenskih, hrvaških in drugih "manjšinskih" jezikov
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- Ne halucinira pri instrumentalnih sekcijah
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- Razume kontekst pesmi (lirika)
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Slabosti:
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- Počasen (~100s na 2min audio)
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- Dražji ($0.20 vs $0.013)
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- Timestamps včasih off za 1-2s
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"""
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import urllib.request
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import urllib.error
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api_key = os.environ.get("GEMINI_API_KEY")
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if not api_key:
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print(f" ❌ Gemini fallback: GEMINI_API_KEY missing", file=sys.stderr)
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return None
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print(f"🧠 Gemini 3 Pro transcribing {audio_path}...", file=sys.stderr)
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audio_size_mb = os.path.getsize(audio_path) / 1024 / 1024
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print(f" 📦 Audio size: {audio_size_mb:.1f} MB", file=sys.stderr)
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try:
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# 1. Upload audio prek Files API (resumable)
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upload_url_base = "https://generativelanguage.googleapis.com/upload/v1beta/files"
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with open(audio_path, 'rb') as f:
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audio_bytes = f.read()
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# Step 1: start
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headers_start = {
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'X-Goog-Upload-Protocol': 'resumable',
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'X-Goog-Upload-Command': 'start',
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'X-Goog-Upload-Header-Content-Length': str(len(audio_bytes)),
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'X-Goog-Upload-Header-Content-Type': 'audio/mp3',
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'Content-Type': 'application/json',
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}
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req_start = urllib.request.Request(
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f"{upload_url_base}?key={api_key}",
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data=json.dumps({"file": {"display_name": "reels_audio"}}).encode(),
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headers=headers_start, method='POST'
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)
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with urllib.request.urlopen(req_start, timeout=30) as resp:
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upload_url = resp.headers.get('X-Goog-Upload-URL')
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# Step 2: upload bytes
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headers_upload = {
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'Content-Length': str(len(audio_bytes)),
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'X-Goog-Upload-Offset': '0',
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'X-Goog-Upload-Command': 'upload, finalize',
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}
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req_upload = urllib.request.Request(
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upload_url, data=audio_bytes,
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headers=headers_upload, method='POST'
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)
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with urllib.request.urlopen(req_upload, timeout=120) as resp:
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file_info = json.loads(resp.read().decode())
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file_uri = file_info['file']['uri']
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print(f" ✓ Uploaded to Gemini Files API", file=sys.stderr)
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# Manjši delay da se file procesi
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time.sleep(2)
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# 2. Generate transcript
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gen_url = (f"https://generativelanguage.googleapis.com/v1beta/"
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f"models/gemini-3-pro-preview:generateContent?key={api_key}")
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lang_hint = ""
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if filename_hint:
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lang_hint = f"\nFilename hint: {filename_hint}"
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if lang:
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lang_hint += f"\nLanguage: {lang}"
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prompt = f"""Transcribe this song with precise word-level timestamps.{lang_hint}
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Return ONLY valid JSON in this EXACT format (no markdown fences, no explanation):
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{{
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"language": "sl",
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"segments": [
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{{
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"start": 0.5,
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"end": 4.2,
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"text": "Besedilo segmenta",
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"words": [
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{{"start": 0.5, "end": 0.9, "text": "Besedilo"}},
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{{"start": 1.0, "end": 1.4, "text": "segmenta"}}
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]
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}}
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]
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}}
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Rules:
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- Only transcribe vocal singing, NOT instrumental sections
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- Each segment is a complete musical phrase (typically 2-4 seconds)
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- Include word-level timestamps for EVERY word
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- Use proper orthography (š, č, ž for Slavic; ä, ö, ü for German etc.)
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- Skip instrumental breaks (don't fill with silence segments)
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- Be very accurate with timestamps - this is for video subtitle generation
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- DO NOT hallucinate words during instrumental sections
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- DO NOT include trailing commas in JSON
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Output ONLY the JSON object."""
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payload = {
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"contents": [{
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"parts": [
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{"text": prompt},
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{"file_data": {"mime_type": "audio/mp3", "file_uri": file_uri}}
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]
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}],
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"generationConfig": {
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"temperature": 0.0,
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"maxOutputTokens": 32000,
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}
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}
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req_gen = urllib.request.Request(
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gen_url,
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data=json.dumps(payload).encode(),
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headers={'Content-Type': 'application/json'},
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method='POST'
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)
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t0 = time.time()
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with urllib.request.urlopen(req_gen, timeout=300) as resp:
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result = json.loads(resp.read().decode())
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elapsed = time.time() - t0
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usage = result.get('usageMetadata', {})
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print(f" ✓ Gemini 3 Pro response v {elapsed:.0f}s "
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f"(in: {usage.get('promptTokenCount', 0)}, "
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f"out: {usage.get('candidatesTokenCount', 0)}, "
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f"thoughts: {usage.get('thoughtsTokenCount', 0)})", file=sys.stderr)
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# 3. Parse JSON output
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candidate_text = result['candidates'][0]['content']['parts'][0]['text'].strip()
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# Pobriši markdown code fences če so
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if candidate_text.startswith('```'):
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# ```json\n...\n```
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lines = candidate_text.split('\n')
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if lines[0].startswith('```'):
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lines = lines[1:]
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if lines and lines[-1].rstrip() == '```':
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lines = lines[:-1]
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candidate_text = '\n'.join(lines)
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# Try-except za JSON z popravki za pogoste težave
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parsed = None
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try:
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parsed = json.loads(candidate_text)
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except json.JSONDecodeError as e:
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# Trailing comma fix
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import re as _re
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cleaned = _re.sub(r',(\s*[}\]])', r'\1', candidate_text)
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try:
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parsed = json.loads(cleaned)
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print(f" ✓ Fixed trailing commas in Gemini JSON", file=sys.stderr)
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except json.JSONDecodeError as e2:
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print(f" ❌ Gemini JSON parse failed: {e2}", file=sys.stderr)
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print(f" First 500 chars: {candidate_text[:500]}", file=sys.stderr)
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return None
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if not parsed or not parsed.get('segments'):
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print(f" ❌ Gemini returned no segments", file=sys.stderr)
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return None
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segments = parsed['segments']
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# Detected language
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detected_lang = parsed.get('language', lang or 'unknown')
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# Compute coverage stats
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hallucination_count = 0
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coverage = 0
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total_dur = max((s.get('end', 0) for s in segments), default=0)
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for s in segments:
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seg_dur = s.get('end', 0) - s.get('start', 0)
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word_count = len(s.get('words', []))
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if seg_dur > 15 and word_count < 5:
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hallucination_count += 1
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else:
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coverage += seg_dur
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coverage_pct = (coverage / total_dur * 100) if total_dur else 0
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total_words = sum(len(s.get('words', [])) for s in segments)
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print(f" ✅ Gemini 3 Pro: {total_words} words → {len(segments)} segments, "
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f"lang={detected_lang}, coverage={coverage_pct:.0f}%", file=sys.stderr)
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return {
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"language": detected_lang,
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"language_probability": 0.95,
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"segments": segments,
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"_provider": "gemini-3-pro",
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"_hallucination_count": hallucination_count,
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"_coverage_pct": coverage_pct,
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}
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except urllib.error.HTTPError as e:
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err_body = e.read().decode()[:500] if hasattr(e, 'read') else ''
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print(f" ❌ Gemini HTTP {e.code}: {err_body}", file=sys.stderr)
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return None
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except Exception as e:
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print(f" ❌ Gemini fallback exception: {e}", file=sys.stderr)
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import traceback
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traceback.print_exc(file=sys.stderr)
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return None
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def transcribe_full(audio_path, lang=None, model_size="small", provider="auto", filename_hint=None):
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"""Whisper/Scribe transcript dispatcher.
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"""Whisper/Scribe transcript dispatcher z hybrid fallback.
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provider:
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- "elevenlabs" → ElevenLabs Scribe (najboljša kvaliteta, $0.40/h, ~10s na 200s pesem)
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- "local" → faster-whisper na CPU (brezplačno, počasi, halucinacije)
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- "auto" → Scribe če ELEVENLABS_API_KEY obstaja, sicer local
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- "elevenlabs" → samo Scribe (z auto-retry)
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- "gemini" → samo Gemini 3 Pro
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- "local" → faster-whisper na CPU
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- "hybrid" → Scribe primary, Gemini fallback ob halucinaciji
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- "auto" → hybrid (Scribe + Gemini fallback) če oba API key dostopna
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filename_hint: ime datoteke (uporablja za auto-detect jezika če lang=None)
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"""
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if provider in ("elevenlabs", "auto") and os.environ.get("ELEVENLABS_API_KEY"):
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has_scribe = bool(os.environ.get("ELEVENLABS_API_KEY"))
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has_gemini = bool(os.environ.get("GEMINI_API_KEY"))
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# Resolve "auto" → "hybrid" če oba API ključa, sicer "elevenlabs"
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if provider == "auto":
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provider = "hybrid" if (has_scribe and has_gemini) else ("elevenlabs" if has_scribe else "local")
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# ─── HYBRID: Scribe primary, Gemini fallback ───
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if provider == "hybrid":
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if not has_scribe:
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print(f" ⚠️ Hybrid mode but ELEVENLABS_API_KEY missing — switching to gemini", file=sys.stderr)
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provider = "gemini"
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else:
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# Try Scribe first
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print(f"🎯 HYBRID mode: Scribe primary, Gemini fallback", file=sys.stderr)
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result = transcribe_with_elevenlabs(audio_path, lang=lang, filename_hint=filename_hint)
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if result and result.get("segments"):
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hall_count = result.get("_hallucination_count", 0)
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cov_pct = result.get("_coverage_pct", 100)
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# Quality gate: če je Scribe rezultat dober, vrni ga
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if hall_count == 0 and cov_pct >= 50:
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print(f" ✅ Scribe OK (coverage {cov_pct:.0f}%) — no fallback needed",
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file=sys.stderr)
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return result
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# Halucinacija ali nizko pokritje → preizkusi Scribe še 1x preden gremo na Gemini
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print(f" ⚠️ Scribe quality issues (coverage {cov_pct:.0f}%, "
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f"{hall_count} halu) — RETRY Scribe...", file=sys.stderr)
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result2 = transcribe_with_elevenlabs(audio_path, lang=lang, filename_hint=filename_hint)
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if result2 and result2.get("segments"):
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h2 = result2.get("_hallucination_count", 0)
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c2 = result2.get("_coverage_pct", 100)
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if h2 == 0 and c2 >= 50:
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print(f" ✅ Scribe retry uspel: coverage {cov_pct:.0f}% → {c2:.0f}%",
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file=sys.stderr)
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return result2
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# Še vedno slabo, ali je drugi tek boljši?
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if h2 < hall_count or c2 > cov_pct:
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result = result2
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hall_count = h2
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cov_pct = c2
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# Še vedno halucinacija → Gemini fallback
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if has_gemini:
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print(f" 🔄 Scribe še vedno slab (coverage {cov_pct:.0f}%, "
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f"{hall_count} halu) — switching na Gemini 3 Pro...", file=sys.stderr)
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gemini_result = transcribe_with_gemini(audio_path, lang=lang, filename_hint=filename_hint)
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if gemini_result and gemini_result.get("segments"):
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g_cov = gemini_result.get("_coverage_pct", 100)
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g_hall = gemini_result.get("_hallucination_count", 0)
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# Vzemi tisto kar je boljše
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if g_hall < hall_count or g_cov > cov_pct:
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print(f" ✅ Gemini boljši: coverage {cov_pct:.0f}% → {g_cov:.0f}%, "
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f"hallu {hall_count} → {g_hall}", file=sys.stderr)
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return gemini_result
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else:
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print(f" ⚠️ Gemini ni boljši, ohrani Scribe", file=sys.stderr)
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return result
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else:
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print(f" ⚠️ Gemini fallback ni dosegljiv — vrnem Scribe rezultat",
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file=sys.stderr)
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return result
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else:
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# Scribe popolnoma failed → Gemini direktno
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if has_gemini:
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print(f" 🔄 Scribe failed → Gemini 3 Pro", file=sys.stderr)
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gemini_result = transcribe_with_gemini(audio_path, lang=lang, filename_hint=filename_hint)
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if gemini_result and gemini_result.get("segments"):
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return gemini_result
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# Brez fallback → empty
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return {"language": "unknown", "language_probability": 0.0, "segments": []}
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# ─── GEMINI ONLY ───
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if provider == "gemini":
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if not has_gemini:
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print(f" ❌ provider=gemini ampak GEMINI_API_KEY missing", file=sys.stderr)
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return {"language": "unknown", "language_probability": 0.0, "segments": []}
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result = transcribe_with_gemini(audio_path, lang=lang, filename_hint=filename_hint)
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if result and result.get("segments"):
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return result
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return {"language": "unknown", "language_probability": 0.0, "segments": []}
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# ─── ELEVENLABS / SCRIBE ONLY (z auto-retry) ───
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if provider == "elevenlabs" and has_scribe:
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result = transcribe_with_elevenlabs(audio_path, lang=lang, filename_hint=filename_hint)
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# Auto-retry če halucinacija zaznana (pokritje < 50% ali halucinacijski segmenti)
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if result and result.get("segments"):
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hall_count = result.get("_hallucination_count", 0)
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cov_pct = result.get("_coverage_pct", 100)
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if hall_count > 0 or cov_pct < 50:
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print(f" 🔄 Halucinacija/nizko pokritje ({cov_pct:.0f}%, "
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f"{hall_count} hallucination segs) — RETRY Scribe...", file=sys.stderr)
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# Drugi poskus z malo drugačnimi parametri
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result2 = transcribe_with_elevenlabs(audio_path, lang=lang, filename_hint=filename_hint)
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if result2 and result2.get("segments"):
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h2 = result2.get("_hallucination_count", 0)
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c2 = result2.get("_coverage_pct", 100)
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if h2 < hall_count or c2 > cov_pct:
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print(f" ✅ Retry boljši: pokritje {cov_pct:.0f}% → {c2:.0f}%, "
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f"halucinacije {hall_count} → {h2}", file=sys.stderr)
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print(f" ✅ Retry boljši: pokritje {cov_pct:.0f}% → {c2:.0f}%",
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file=sys.stderr)
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result = result2
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else:
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print(f" ⚠️ Retry ni izboljšal, ohrani prvi rezultat", file=sys.stderr)
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return result
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if provider == "elevenlabs":
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print(f" ⚠️ Scribe failed, no fallback (provider=elevenlabs)", file=sys.stderr)
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return {"language": "unknown", "language_probability": 0.0, "segments": []}
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print(f" 🔄 Scribe failed, fallback na local Whisper...", file=sys.stderr)
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# Local faster-whisper
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return {"language": "unknown", "language_probability": 0.0, "segments": []}
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# ─── LOCAL faster-whisper ───
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return _transcribe_full_local(audio_path, lang=lang, model_size=model_size)
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