Floke
68f263978a
- Backend: Switched prompt to JSON output for structured data
- Backend: Added PUT /chunks/{id} endpoint for persistence
- Backend: Fixed app.py imports and initialization logic
- Frontend: Complete rewrite for Unified View (flattened chunks)
- Frontend: Added Inline Editing (Text/Speaker) and Row Deletion
- Docs: Updated TRANSCRIPTION_TOOL.md with v0.4 features
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
import os
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import time
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import logging
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from google import genai
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from google.genai import types
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from ..config import settings
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logger = logging.getLogger(__name__)
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class TranscriptionService:
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def __init__(self):
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if not settings.GEMINI_API_KEY:
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raise Exception("Gemini API Key missing.")
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self.client = genai.Client(api_key=settings.GEMINI_API_KEY)
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def transcribe_chunk(self, file_path: str, offset_seconds: int = 0) -> dict:
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"""
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Uploads a chunk to Gemini and returns the transcription with timestamps.
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"""
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logger.info(f"Uploading chunk {file_path} to Gemini...")
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# 1. Upload file (positional argument)
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media_file = self.client.files.upload(file=file_path)
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# 2. Wait for processing (usually fast for audio)
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while media_file.state == "PROCESSING":
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time.sleep(2)
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media_file = self.client.files.get(name=media_file.name)
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if media_file.state == "FAILED":
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raise Exception("File processing failed at Gemini.")
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# 3. Transcribe with Diarization and Timestamps
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prompt = """
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Transkribiere dieses Audio wortgetreu.
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Identifiziere die Sprecher (Speaker A, Speaker B, etc.).
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Gib das Ergebnis als JSON-Liste zurück.
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Format:
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[
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{
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"time": "MM:SS",
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"speaker": "Speaker A",
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"text": "..."
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}
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]
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"""
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logger.info(f"Generating transcription for {file_path}...")
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response = self.client.models.generate_content(
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model="gemini-2.0-flash",
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contents=[media_file, prompt],
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config=types.GenerateContentConfig(
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temperature=0.1,
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response_mime_type="application/json"
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)
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)
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# Cleanup: Delete file from Gemini storage
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self.client.files.delete(name=media_file.name)
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return {
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"raw_text": response.text, # This is now a JSON string
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"offset": offset_seconds
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}
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