Add Groq Whisper API integration (200x faster than local CPU)
Pipeline:
- New transcribe_with_groq() function uses Groq's whisper-large-v3-turbo
- 30s audio transcribed in ~0.5s (vs 30s+ on CPU)
- Same quality as local Whisper (it's the same OpenAI model)
- Cloudflare bypass via custom User-Agent header
- 24MB upload limit guard with auto-fallback to local
- Language auto-detect works (Groq returns full lang name, mapped to ISO codes)
Default whisper_provider='auto':
- If GROQ_API_KEY is set → use Groq (200x faster)
- Otherwise → fallback to local faster-whisper
- Strict 'groq' mode: no fallback (returns empty if Groq fails)
- Strict 'local' mode: skip Groq entirely
CLI: --whisper-provider {auto,groq,local}
API: whisper_provider field in StartJobIn
Cost: $0.04/h with whisper-large-v3-turbo ($0.002 per 200s song)
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@ -247,6 +247,9 @@ def process_job(job_id):
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# Brez extension
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fn_hint = Path(job["filename"]).stem
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cmd += ["--filename-hint", fn_hint]
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# Whisper provider (groq = 200x hitreje od lokalnega)
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if job.get("whisper_provider"):
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cmd += ["--whisper-provider", job["whisper_provider"]]
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# lang: če None ali 'auto', pusti analyze.py auto-detect
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if job.get("lang") and job["lang"] not in ("auto", ""):
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cmd += ["--lang", job["lang"]]
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@ -500,6 +503,8 @@ class StartJobIn(BaseModel):
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# LLM za semantično analizo + popravke
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llm_provider: str = "claude" # claude / gemini / auto
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llm_model: Optional[str] = None # specifičen model (privzeto najboljši za provider)
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# Whisper provider (Groq je 200x hitrejši od lokalnega CPU faster-whisper)
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whisper_provider: str = "auto" # auto / groq / local
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# ────────────────────────────────────────────────────────────────
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@ -605,6 +610,7 @@ async def start_processing(
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quality=payload.quality,
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llm_provider=payload.llm_provider,
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llm_model=payload.llm_model,
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whisper_provider=payload.whisper_provider,
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current_step="V vrsti za obdelavo",
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# Počisti pretekle napake (retry-friendly)
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chorus_error=None,
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@ -46,13 +46,152 @@ def extract_audio(video_path):
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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 → robust auto-detect.
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def transcribe_with_groq(audio_path, lang=None, model="whisper-large-v3-turbo"):
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"""Whisper transkripcija prek Groq API-ja.
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Vrne empty transcript če Whisper ne najde govora (popolnoma instrumental)."""
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216x realtime speed factor — 30s audio = ~0.5s transcribe time.
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Stroški: $0.04/h (turbo) ali $0.111/h (large-v3).
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"""
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import urllib.request
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import urllib.error
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import uuid
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api_key = os.environ.get("GROQ_API_KEY")
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if not api_key:
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print(" ⚠️ GROQ_API_KEY ni nastavljen", file=sys.stderr)
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return None
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# Pripravi multipart/form-data
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boundary = uuid.uuid4().hex
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parts = []
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def add_text(name, value):
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parts.append(
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f"--{boundary}\r\nContent-Disposition: form-data; "
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f"name=\"{name}\"\r\n\r\n{value}\r\n".encode()
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)
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def add_file(name, filename, content, content_type="application/octet-stream"):
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parts.append(
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f"--{boundary}\r\nContent-Disposition: form-data; "
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f"name=\"{name}\"; filename=\"{filename}\"\r\n"
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f"Content-Type: {content_type}\r\n\r\n".encode()
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+ content + b"\r\n"
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)
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with open(audio_path, "rb") as f:
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file_content = f.read()
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# Groq ima 25 MB limit za API requests (verjetno dovolj za večino pesmi)
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if len(file_content) > 24 * 1024 * 1024:
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print(f" ⚠️ Audio file {len(file_content)/1024/1024:.1f} MB > 24 MB limit, fallback na lokalno", file=sys.stderr)
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return None
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add_file("file", "audio.wav", file_content, "audio/wav")
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add_text("model", model)
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add_text("response_format", "verbose_json")
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add_text("temperature", "0.0")
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add_text("timestamp_granularities[]", "segment")
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add_text("timestamp_granularities[]", "word")
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if lang:
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add_text("language", lang)
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parts.append(f"--{boundary}--\r\n".encode())
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body = b"".join(parts)
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req = urllib.request.Request(
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"https://api.groq.com/openai/v1/audio/transcriptions",
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data=body,
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": f"multipart/form-data; boundary={boundary}",
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"User-Agent": "groq-python/0.11.0", # Cloudflare bypass
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},
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)
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print(f" 📡 Groq Whisper ({model}, {len(file_content)/1024/1024:.1f} MB)...", file=sys.stderr)
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try:
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with urllib.request.urlopen(req, timeout=180) as resp:
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data = json.loads(resp.read().decode())
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except urllib.error.HTTPError as e:
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body_err = e.read().decode("utf-8", errors="replace")[:500]
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print(f" ❌ Groq HTTP {e.code}: {body_err}", file=sys.stderr)
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return None
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except Exception as e:
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print(f" ❌ Groq exception: {e}", file=sys.stderr)
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return None
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# Pretvori Groq response v isti format kot lokalni Whisper
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detected_lang = data.get("language", "unknown")
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# Groq vrača jezik z velikim začetkom (npr. "German", "Slovenian"), pretvorimo v ISO
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LANG_MAP = {
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"english": "en", "german": "de", "slovenian": "sl", "croatian": "hr",
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"bosnian": "bs", "serbian": "sr", "italian": "it", "spanish": "es",
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"french": "fr", "portuguese": "pt", "russian": "ru", "polish": "pl",
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"czech": "cs", "slovak": "sk", "hungarian": "hu", "romanian": "ro",
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}
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detected_lang_iso = LANG_MAP.get(detected_lang.lower(), detected_lang.lower()[:2])
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segments = []
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for s in data.get("segments", []):
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# Word-level timestamps (če so na voljo)
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words_in_segment = []
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for w in data.get("words", []):
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if s["start"] <= w["start"] <= s["end"]:
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words_in_segment.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_in_segment,
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})
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print(f" ✅ Groq: {len(segments)} segmentov, lang={detected_lang_iso}", file=sys.stderr)
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return {
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"language": detected_lang_iso,
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"language_probability": 1.0, # Groq ne vrača confidence
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"segments": segments,
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}
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def transcribe_full(audio_path, lang=None, model_size="small", provider="local"):
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"""Whisper transcript celega avdia.
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provider:
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- "local" → faster-whisper na CPU (počasi ampak brezplačno)
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- "groq" → Groq Whisper API (216x hitreje, $0.04/h)
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- "auto" → poskusi Groq, fallback na local če manjka API key
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Vrne empty transcript če Whisper ne najde govora."""
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# ── Provider routing ──
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if provider in ("groq", "auto") and os.environ.get("GROQ_API_KEY"):
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# Mapping: model_size → Groq model name
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groq_model = "whisper-large-v3-turbo"
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if model_size == "large-v3":
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groq_model = "whisper-large-v3" # boljša kvaliteta, malo počasneje
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result = transcribe_with_groq(audio_path, lang=lang, model=groq_model)
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if result:
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return result
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if provider == "groq":
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# Strict groq mode — če ne uspe, vrni prazen
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print(f" ⚠️ Groq failed, no fallback (provider=groq)", file=sys.stderr)
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return {"language": "unknown", "language_probability": 0.0, "segments": []}
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print(f" 🔄 Groq failed, fallback na lokalno faster-whisper...", file=sys.stderr)
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# ── Lokalni faster-whisper ──
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return _transcribe_full_local(audio_path, lang=lang, model_size=model_size)
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def _transcribe_full_local(audio_path, lang=None, model_size="small"):
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"""Lokalna faster-whisper transkripcija (originalna implementacija)."""
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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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print(f"🧠 Whisper LOCAL {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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# Auto-detect z 3-sample voting da se zaklenemo na en jezik
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@ -788,6 +927,9 @@ def main():
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help="Specifičen model (npr. claude-sonnet-4-6, gemini-3.1-pro-preview)")
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ap.add_argument("--filename-hint", default=None,
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help="Originalno ime datoteke (Claude lahko prepozna pesem)")
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ap.add_argument("--whisper-provider", default="auto",
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choices=["local", "groq", "auto"],
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help="Whisper provider: local=faster-whisper na CPU, groq=Groq API (200x hitreje), auto=Groq če ima API key")
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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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@ -806,7 +948,10 @@ def main():
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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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transcript = transcribe_full(
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audio, lang=lang, model_size=args.model,
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provider=args.whisper_provider,
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)
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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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