120 lines
4.5 KiB
Python
Executable File
120 lines
4.5 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import sys
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import shutil
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import subprocess
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from pathlib import Path
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from faster_whisper import WhisperModel
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from faster_whisper.utils import format_timestamp
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# Prevent OpenMP thread conflicts from crashing the script
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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# 5 minutes per chunk (300 seconds) keeps RAM usage low and stable
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CHUNK_DURATION_SEC = 300
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print("🔧 Initializing C++ Engine...")
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model = WhisperModel(
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"base",
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device="cpu",
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compute_type="int8",
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cpu_threads=0, # Let CTranslate2 auto-detect safe core counts
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num_workers=1
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)
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print("✅ C++ Model loaded successfully.")
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def extract_audio_and_chunk(video_path: str, output_dir: Path) -> list:
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"""Extracts and splits audio into 5-minute chunks using a single FFmpeg pass."""
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print("🚀 Extracting and chunking audio with FFmpeg...")
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output_dir.mkdir(parents=True, exist_ok=True)
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# Segment format output: chunk_000.wav, chunk_001.wav, etc.
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chunk_pattern = str(output_dir / "chunk_%03d.wav")
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command = [
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"ffmpeg", "-y", "-i", video_path,
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"-vn", "-ac", "1", "-ar", "16000",
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"-acodec", "pcm_s16le", "-sn", "-map_chapters", "-1",
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"-f", "segment", "-segment_time", str(CHUNK_DURATION_SEC),
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chunk_pattern
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]
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result = subprocess.run(command, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE, text=True)
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if result.returncode != 0:
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print(f"❌ FFmpeg Error Output:\n{result.stderr}")
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raise RuntimeError("FFmpeg extraction and chunking failed.")
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# Return sorted list of generated chunk files
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return sorted(list(output_dir.glob("chunk_*.wav")))
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def transcribe_to_srt(video_path: str, force: bool = False):
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video_path_obj = Path(video_path)
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srt_path = video_path_obj.with_suffix(".srt")
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temp_dir = video_path_obj.parent / f"temp_chunks_{video_path_obj.stem}"
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if srt_path.exists():
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if not force:
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print(f"❌ Error: Transcription file already exists: {srt_path}")
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return
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else:
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srt_path.unlink()
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try:
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# Step 1: Split audio into bite-sized pieces
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audio_chunks = extract_audio_and_chunk(str(video_path_obj), temp_dir)
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if not audio_chunks:
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print("❌ Error: No audio chunks were generated.")
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return
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print(f"📦 Successfully split audio into {len(audio_chunks)} chunks.")
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print("🎙️ Starting safe chunk-by-chunk transcription...")
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global_segment_index = 1
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with open(srt_path, "w", encoding="utf-8") as srt_file:
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for chunk_idx, chunk_path in enumerate(audio_chunks):
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# Calculate the time offset for the current chunk
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time_offset = chunk_idx * CHUNK_DURATION_SEC
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print(f"\n⏳ Processing chunk {chunk_idx + 1}/{len(audio_chunks)} ({chunk_path.name})...")
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segments_generator, info = model.transcribe(
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str(chunk_path),
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beam_size=1,
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vad_filter=True,
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temperature=0.0
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)
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# Consume chunk generator and shift timestamps instantly
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for segment in segments_generator:
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# Shift timestamps relative to the original video timeline
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actual_start = segment.start + time_offset
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actual_end = segment.end + time_offset
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start_str = format_timestamp(actual_start, always_include_hours=True)
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end_str = format_timestamp(actual_end, always_include_hours=True)
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srt_file.write(f"{global_segment_index}\n{start_str} --> {end_str}\n{segment.text.strip()}\n\n")
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global_segment_index += 1
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# Free up space as we go by deleting the processed chunk
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chunk_path.unlink()
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print(f"\n✅ All chunks combined! SRT subtitle file saved in: {srt_path}")
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except Exception as e:
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print(f"\n❌ Execution Error: {e}")
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finally:
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# Clean up the temporary folder entirely
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if temp_dir.exists():
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shutil.rmtree(temp_dir)
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if __name__ == "__main__":
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target_video = "download/videos/2813112936/2813112936.mp4"
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if not os.path.exists(target_video):
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print(f"❌ System Error: Target video file does not exist at path: {target_video}")
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else:
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transcribe_to_srt(target_video, force=True)
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