73 lines
1.7 KiB
Python
73 lines
1.7 KiB
Python
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import subprocess
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import sys
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from datetime import datetime
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from pathlib import Path
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# Configuration
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OUTPUT_DIR = Path(r"C:\Users\antoi\antoine\My Libraries\Antoine Brain Extension\+\Transcripts")
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MODEL = "openai/whisper-large-v3"
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def transcribe(audio_path: str):
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audio_file = Path(audio_path)
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timestamp = datetime.now().strftime("%Y-%m-%d %H-%M")
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note_name = f"Voice Note {timestamp}.md"
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temp_file = Path.home() / "AppData/Local/Temp/whisper_output.txt"
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print(f"\n🎙️ Transcribing: {audio_file.name}")
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print(f"📝 Output: {note_name}\n")
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# Run whisper
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subprocess.run([
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"insanely-fast-whisper",
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"--file-name", str(audio_file),
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"--transcript-path", str(temp_file),
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"--model-name", MODEL
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])
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# Read transcript
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transcript = temp_file.read_text(encoding="utf-8")
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# Create markdown note
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note_content = f"""---
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created: {datetime.now().strftime("%Y-%m-%d %H:%M")}
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type: voice-note
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status: raw
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tags:
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- transcript
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- voice-memo
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---
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# Voice Note - {datetime.now().strftime("%Y-%m-%d")} at {datetime.now().strftime("%H:%M")}
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## Metadata
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- **Source file:** `{audio_file.name}`
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- **Transcribed:** {datetime.now().strftime("%Y-%m-%d %H:%M")}
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---
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## Raw Transcript
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{transcript}
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---
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## Notes distillees
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<!-- Coller le transcript dans Claude pour organiser et distiller -->
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"""
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output_path = OUTPUT_DIR / note_name
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output_path.write_text(note_content, encoding="utf-8")
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print(f"\n✅ Done! Created: {note_name}")
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print(f"📁 Location: {OUTPUT_DIR}")
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if __name__ == "__main__":
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if len(sys.argv) > 1:
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transcribe(sys.argv[1])
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else:
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audio = input("Enter audio file path: ").strip('"')
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transcribe(audio)
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