first commit

.dockerignore

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+__pycache__/
+*.pyc
+.DS_Store
+.venv/
+env/
+build/
+dist/
+tts_segments/
+audio.wav
+dub_mix.wav
+dubbed_output.mp4
+*.json
+*.mp4
+*.wav

Dockerfile

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+ARG TARGETPLATFORM
+FROM --platform=${TARGETPLATFORM:-linux/amd64} python:3.11-slim-bookworm
+
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1
+
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends ffmpeg libsndfile1 \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --upgrade pip \
+ && pip install -r requirements.txt
+
+COPY . .
+
+ENTRYPOINT ["python", "main.py"]

main.py

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+#!/usr/bin/env python3
+import os
+import sys
+import json
+import subprocess
+import numpy as np
+import soundfile as sf
+import torchaudio
+import torch
+import whisper
+from deep_translator import GoogleTranslator
+from pyannote.audio import Pipeline
+from rich.console import Console
+from rich.progress import Progress, BarColumn, TextColumn, TimeRemainingColumn, TimeElapsedColumn
+
+console = Console()
+
+# --- Safe-global engedélyezés TorchVersion számára (checkpoint hack) ---
+from torch.serialization import add_safe_globals
+from torch.torch_version import TorchVersion
+add_safe_globals([TorchVersion])
+
+# --- FFmpeg és Whisper a saját rendszered pipeline-jához, HF Token kötelező ---
+if len(sys.argv) < 2:
+    console.print("❌ Használat: python3 dub_pipeline.py <video.mp4>")
+    sys.exit(1)
+
+video_path = sys.argv[1]
+if not os.path.exists(video_path):
+    console.print("❌ A videó nem található!")
+    sys.exit(1)
+
+token = os.environ.get("HF_TOKEN")
+if not token:
+    console.print("❌ HF_TOKEN NINCS beállítva!")
+    sys.exit(1)
+
+AUDIO_WAV = "audio.wav"
+
+# --- 1. Hang kinyerés FFmpeg-gel mono 16kHz WAV-ba ---
+console.print("\n🎬 [1/6] Hang kinyerés videóból (mono, 16kHz WAV)…")
+
+if os.path.exists(AUDIO_WAV):
+    os.remove(AUDIO_WAV)
+
+with Progress(
+    TextColumn("{task.description}"),
+    BarColumn(),
+    TimeElapsedColumn(),
+    TimeRemainingColumn(),
+) as progress:
+    progress.add_task("FFmpeg extract fut…", total=None)
+    subprocess.run([
+        "ffmpeg","-y","-i",video_path,
+        "-map","0:a:0","-ac","1","-ar","16000","-c:a","pcm_s16le",AUDIO_WAV
+    ], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+if not os.path.exists(AUDIO_WAV):
+    console.print("❌ Hang extract sikertelen!")
+    sys.exit(1)
+
+console.print("✔ Hang kinyerve: audio.wav\n")
+
+
+# --- 2. Whisper STT (angol beszéd felismerés) ---
+console.print("🎤 [2/6] Speech-to-Text (Whisper, EN felismerés)…")
+
+model = whisper.load_model("large")  # stabil, többnyelvű
+with Progress(TextColumn("Whisper STT…"), BarColumn(), TimeElapsedColumn(), TimeRemainingColumn()) as p:
+    p.add_task("STT fut…", total=None)
+    result = model.transcribe(AUDIO_WAV, language="en")
+
+segments = result.get("segments", [])
+console.print(f"✔ STT kész, szegmensek száma: {len(segments)}")
+
+# --- 2.5 Fordítás angol → magyarra ---
+console.print("\n🌍 Angol → Magyar fordítás…")
+translator = GoogleTranslator(source="en", target="hu")
+
+translated = []
+for seg in segments:
+    text_en = seg["text"].strip()
+    text_hu = translator.translate(text_en) if text_en else ""
+    translated.append({
+        "speaker": "UNKNOWN",  # később diarization fogja kitölteni
+        "start": seg["start"],
+        "end": seg["end"],
+        "en": text_en,
+        "hu": text_hu
+    })
+
+with open("segments_translated.json","w",encoding="utf-8") as f:
+    json.dump(translated,f,ensure_ascii=False,indent=2)
+
+console.print("✔ Fordított szegmensek mentve: segments_translated.json\n")
+
+
+# --- 3. Beszélők felismerése (Pyannote diarization) ---
+console.print("👥 [3/6] Beszélők diarization (pyannote/speaker-diarization-3.1)…")
+
+waveform, sample_rate = torchaudio.load(AUDIO_WAV)
+audio_dict = {"waveform": waveform, "sample_rate": sample_rate}
+
+diar_pipeline = Pipeline.from_pretrained(
+    "pyannote/speaker-diarization-3.1",
+    token=token
+)
+
+with Progress(TextColumn("Diarization fut…"), BarColumn(), TimeElapsedColumn(), TimeRemainingColumn()) as p:
+    p.add_task("Beszélők szétválasztása…", total=None)
+    diarization = diar_pipeline(audio_dict)
+
+# speaker szegmensek bejárása
+speaker_segments = []
+speakers = set()
+
+for segment, _, speaker in diarization.itertracks(yield_label=True):
+    speakers.add(speaker)
+    speaker_segments.append({
+        "speaker": speaker,
+        "start": float(segment.start),
+        "end": float(segment.end)
+    })
+
+with open("diar.json","w",encoding="utf-8") as f:
+    json.dump(speaker_segments,f,ensure_ascii=False,indent=2)
+
+console.print(f"✔ Diarization kész, beszélők: {sorted(list(speakers))}\n")
+
+# --- 4. Speaker idő alapján speaker kitöltése a translated szegmensekhez ---
+console.print("🧠 [4/6] Speaker ID STT → speaker idő alapján…")
+
+def find_speaker_for_time(t):
+    for s in speaker_segments:
+        if s["start"] <= t <= s["end"]:
+            return s["speaker"]
+    return "UNKNOWN"
+
+aligned = []
+for item in translated:
+    spk = find_speaker_for_time(item["start"])
+    aligned.append({
+        "speaker": spk,
+        "start": item["start"],
+        "end": item["end"],
+        "en": item["en"],
+        "hu": item["hu"]
+    })
+
+with open("aligned_segments.json","w",encoding="utf-8") as f:
+    json.dump(aligned,f,ensure_ascii=False,indent=2)
+
+console.print("✔ Speakerhez rendelt szegmensek mentve: aligned_segments.json\n")
+
+
+# --- 5. TTS: speakerenként külön magyar hang (edge-tts) ---
+console.print("🎙 [5/6] Magyar hang generálás speakerenként külön voice-al…")
+
+available_voices = [
+    "hu-HU-NoemiNeural",
+    "hu-HU-TamasNeural",
+    "hu-HU-LillaNeural",
+    "hu-HU-ImreNeural",
+]
+
+spk_list = sorted(list({s["speaker"] for s in aligned}))
+voice_map = {}
+
+for i, spk in enumerate(spk_list):
+    voice_map[spk] = available_voices[i % len(available_voices)]
+
+console.print("🗣 Speaker → Voice kiosztás:")
+for k,v in voice_map.items():
+    console.print(f"  {k} → {v}")
+
+os.makedirs("tts_segments", exist_ok=True)
+tts_meta = []
+
+for i, seg in enumerate(aligned):
+    spk = seg["speaker"]
+    text = seg["hu"].strip()
+    if not text:
+        continue
+
+    voice = voice_map.get(spk, available_voices[0])
+    out_path = os.path.join("tts_segments", f"tts_{i:04d}.wav")
+
+    subprocess.run([
+        "edge-tts", "--voice", voice,
+        "--text", text,
+        "--write-media", out_path
+    ], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+    if os.path.exists(out_path):
+        tts_meta.append({"path": out_path, "start": seg["start"]})
+
+console.print(f"\n✔ {len(tts_meta)} db TTS hang-szegmens elkészült\n")
+
+
+# --- 6. Magyar TTS sáv összeépítés + videóba muxolás ---
+console.print("🎚 [6/6] Magyar dub hangsáv létrehozása és visszaillesztése videóra…")
+
+if not tts_meta:
+    console.print("❌ Nincs TTS, kilépek.")
+    sys.exit(1)
+
+video_duration = aligned[-1]["end"]
+target_sr = 48000
+samples = int((video_duration+1)*target_sr)
+mix = np.zeros(samples, dtype=np.float32)
+
+for tts in tts_meta:
+    data_tts, sr_tts = sf.read(tts["path"])
+    if sr_tts != target_sr:
+        res = torchaudio.transforms.Resample(sr_tts, target_sr)
+        data_tts = res(torch.from_numpy(data_tts).float().unsqueeze(0)).squeeze(0).numpy()
+
+    start_i = int(tts["start"]*target_sr)
+    end_i = start_i + len(data_tts)
+    if end_i > len(mix):
+        data_tts = data_tts[:len(mix)-start_i]
+        end_i = len(mix)
+
+    mix[start_i:end_i] += data_tts
+
+mix = mix / (np.max(np.abs(mix))+1e-9) * 0.9
+sf.write("dub_mix.wav", mix, target_sr)
+console.print("✔ Dub sáv mentve: dub_mix.wav")
+
+subprocess.run([
+    "ffmpeg","-y","-i",video_path,
+    "-i","dub_mix.wav",
+    "-map","0:v:0","-map","1:a:0",
+    "-c:v","copy","-c:a","aac","-shortest",
+    "dubbed_output.mp4"
+], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+console.print("\n✅ Magyar AI dub kész: dubbed_output.mp4")

requirements.txt

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+openai-whisper
+pyannote.audio
+torch
+torchaudio
+numpy
+soundfile
+rich
+deep-translator
+edge-tts