55f5e5f5a8
- Auto language detection using OpenAI Whisper during video upload
- Editable transcript interface for reviewing/correcting transcriptions
- Updated translation pipeline to use edited transcripts when available
- Migrated from JWT to Google OAuth-only authentication for better security
- Added complete Docker containerization with docker-compose.yml
- Updated database schema with language detection and transcript editing fields
- Enhanced API documentation and workflow in README
- Added comprehensive environment variable configuration
🤖 Generated with BackendIM
Co-Authored-By: Claude <noreply@anthropic.com>
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import os
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import tempfile
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import whisper
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import ffmpeg
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from typing import Optional
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async def detect_language_from_video(video_content: bytes) -> Optional[str]:
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"""
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Detect language from video using OpenAI Whisper
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"""
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try:
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# Create temporary files
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with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as video_temp:
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video_temp.write(video_content)
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video_temp_path = video_temp.name
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as audio_temp:
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audio_temp_path = audio_temp.name
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try:
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# Extract audio from video using ffmpeg
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(
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ffmpeg
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.input(video_temp_path)
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.output(audio_temp_path, acodec='pcm_s16le', ac=1, ar='16000')
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.overwrite_output()
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.run(quiet=True)
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)
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# Load Whisper model (using base model for language detection)
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model = whisper.load_model("base")
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# Detect language
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audio = whisper.load_audio(audio_temp_path)
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audio = whisper.pad_or_trim(audio)
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mel = whisper.log_mel_spectrogram(audio).to(model.device)
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# Detect language
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_, probs = model.detect_language(mel)
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detected_language = max(probs, key=probs.get)
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return detected_language
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finally:
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# Clean up temporary files
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if os.path.exists(video_temp_path):
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os.unlink(video_temp_path)
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if os.path.exists(audio_temp_path):
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os.unlink(audio_temp_path)
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except Exception as e:
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# Log error but don't fail the upload
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print(f"Language detection failed: {e}")
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return None |