98e829c995
Full pipeline: Reddit sourcing → Groq text optimization → Edge-TTS voice generation → Whisper transcription → FFmpeg video rendering with word-level subtitles. Includes SQLite deduplication and .env-based config. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import os
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import sys
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from src.reddit_client import get_next_post, mark_processed
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from src.processor import optimize_text
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from src.voice_gen import generate_audio
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from src.subtitler import transcribe
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from src.video_engine import render_video
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WHISPER_MODEL = os.getenv("WHISPER_MODEL", "base")
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TEMP_AUDIO = "temp_audio.mp3"
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def run():
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print("[main] Fetching next Reddit post...")
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post = get_next_post()
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if not post:
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print("[main] No new viral posts found. Try again later.")
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sys.exit(0)
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print(f"[main] Found: r/{post['subreddit']} — {post['title'][:60]} (score: {post['score']})")
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print("[main] Optimizing text with Groq...")
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script = optimize_text(post["title"], post["text"])
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print(f"[main] Script preview: {script[:120]}...")
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print("[main] Generating voice...")
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generate_audio(script, TEMP_AUDIO)
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print("[main] Transcribing with Whisper...")
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words = transcribe(TEMP_AUDIO, model_name=WHISPER_MODEL)
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print(f"[main] Got {len(words)} word timestamps.")
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print("[main] Rendering video...")
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output_path = render_video(TEMP_AUDIO, words, post["id"])
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mark_processed(post["id"])
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print(f"[main] Done! Video saved to: {output_path}")
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if os.path.exists(TEMP_AUDIO):
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os.remove(TEMP_AUDIO)
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if __name__ == "__main__":
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run()
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