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61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
from pathlib import Path
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from typing import Any, Dict, Optional
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from openai import OpenAI
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from application.core.settings import settings
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from application.stt.base import BaseSTT
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class OpenAISTT(BaseSTT):
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def __init__(
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self,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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model: Optional[str] = None,
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):
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self.api_key = api_key or settings.OPENAI_API_KEY or settings.API_KEY
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self.base_url = base_url or settings.OPENAI_BASE_URL or "https://api.openai.com/v1"
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self.model = model or settings.OPENAI_STT_MODEL
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self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
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def transcribe(
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self,
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file_path: Path,
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language: Optional[str] = None,
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timestamps: bool = False,
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diarize: bool = False,
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) -> Dict[str, Any]:
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_ = diarize
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request: Dict[str, Any] = {
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"file": file_path,
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"model": self.model,
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"response_format": "verbose_json",
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}
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if language:
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request["language"] = language
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if timestamps:
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request["timestamp_granularities"] = ["segment"]
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with open(file_path, "rb") as audio_file:
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request["file"] = audio_file
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response = self.client.audio.transcriptions.create(**request)
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response_dict = self._to_dict(response)
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segments = response_dict.get("segments") or []
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return {
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"text": response_dict.get("text", ""),
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"language": response_dict.get("language") or language,
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"duration_s": response_dict.get("duration"),
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"segments": [self._to_dict(segment) for segment in segments],
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"provider": "openai",
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}
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@staticmethod
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def _to_dict(value: Any) -> Dict[str, Any]:
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if hasattr(value, "model_dump"):
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return value.model_dump()
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if isinstance(value, dict):
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return value
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return {}
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