mirror of
https://github.com/suitenumerique/meet.git
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lebaudantoine
857b4bd1f1
Video files are heavy recording files, sometimes several hours long. Previously, recordings were naively submitted to the Whisper API without chunking, resulting in very large requests that could take a long time to process. Video files are much larger than audio-only files, which could cause performance issues during upload. Introduce an extra step to extract the audio component from MP4 files, producing a lighter audio-only file (to be confirmed). No re-encoding is done, just a minimal FFmpeg extraction based on community guidance, since I’m not an FFmpeg expert. This feature is experimental and may introduce regressions, especially if audio quality or sampling is impacted, which could reduce Whisper’s accuracy. Early tests with the ASR model worked, but it has not been tested on long recordings (e.g., 3-hour meetings), which some users have.
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
"""Application configuration and settings."""
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from functools import lru_cache
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from typing import Annotated, List, Optional, Set
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from fastapi import Depends
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from pydantic import SecretStr
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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"""Configuration settings loaded from environment variables and .env file."""
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model_config = SettingsConfigDict(env_file=".env")
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app_name: str = "app"
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app_api_v1_str: str = "/api/v1"
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app_api_token: SecretStr
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# Audio recordings
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recording_max_duration: Optional[int] = None
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recording_allowed_extensions: Set[str] = {".ogg", ".mp4"}
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recording_video_extensions: Set[str] = {".mp4"}
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# Celery settings
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celery_broker_url: str = "redis://redis/0"
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celery_result_backend: str = "redis://redis/0"
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celery_max_retries: int = 1
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transcribe_queue: str = "transcribe-queue"
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summarize_queue: str = "summarize-queue"
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# Minio settings
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aws_storage_bucket_name: str
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aws_s3_endpoint_url: str
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aws_s3_access_key_id: str
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aws_s3_secret_access_key: SecretStr
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aws_s3_secure_access: bool = True
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# AI-related settings
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whisperx_api_key: SecretStr
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whisperx_base_url: str = "https://api.openai.com/v1"
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whisperx_asr_model: str = "whisper-1"
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whisperx_max_retries: int = 0
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# ISO 639-1 language code (e.g., "en", "fr", "es")
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whisperx_default_language: Optional[str] = None
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whisperx_allowed_languages: Set[str] = {"en", "fr"}
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llm_base_url: str
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llm_api_key: SecretStr
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llm_model: str
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# Transcription processing
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hallucination_patterns: List[str] = ["Vap'n'Roll Thierry"]
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hallucination_replacement_text: str = "[Texte impossible à transcrire]"
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# Webhook-related settings
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webhook_max_retries: int = 2
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webhook_status_forcelist: List[int] = [502, 503, 504]
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webhook_backoff_factor: float = 0.1
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webhook_api_token: SecretStr
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webhook_url: str
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# Output related settings
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document_default_title: Optional[str] = "Transcription"
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document_title_template: Optional[str] = (
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'Réunion "{room}" du {room_recording_date} à {room_recording_time}'
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)
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summary_title_template: Optional[str] = "Résumé de {title}"
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# Summary related settings
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is_summary_enabled: bool = True
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# Sentry
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sentry_is_enabled: bool = False
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sentry_dsn: Optional[str] = None
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# Posthog (analytics)
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posthog_enabled: bool = False
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posthog_api_key: Optional[str] = None
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posthog_api_host: Optional[str] = "https://eu.i.posthog.com"
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posthog_event_failure: str = "transcript-failure"
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posthog_event_success: str = "transcript-success"
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# Langfuse (LLM Observability)
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langfuse_enabled: bool = False
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langfuse_host: Optional[str] = None
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langfuse_public_key: Optional[str] = None
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langfuse_secret_key: Optional[SecretStr] = None
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langfuse_environment: Optional[str] = "development"
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# TaskTracker
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task_tracker_redis_url: str = "redis://redis/0"
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task_tracker_prefix: str = "task_metadata:"
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@lru_cache
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def get_settings():
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"""Load and cache application settings."""
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return Settings()
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SettingsDeps = Annotated[Settings, Depends(get_settings)]
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