mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-03 18:46:54 +00:00
Alex
7da46c2bea
- Ship tiktoken's cl100k_base inside the package and build the encoding from it, so token counting never downloads anything. - Default EMBEDDINGS_CACHE_DIR to <data home>/models instead of FastEmbed's temp dir, and read tokenizer.json and repo metadata from that cache, so a model downloads once and survives reboots. - TTS_PROVIDER=none and STT_PROVIDER=none switch the speech features off: the endpoints return 404, audio files fail to ingest with a clear message, /api/config reports tts_available/stt_available, and the UI hides the Speak and microphone buttons. - Drop the Google Fonts Roboto import from the web UI. - prefetch-models fills the cache the app reads; verify-offline checks the packaged encoding. - Docs: new Air-Gapped Deployment guide, settings and cache notes.
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
from pathlib import Path
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from typing import Dict, Union
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from docsgpt.core.settings import settings
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from docsgpt.parser.file.base_parser import BaseParser, DocumentParseError
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from docsgpt.stt.stt_creator import STTCreator
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from docsgpt.stt.upload_limits import enforce_audio_file_size_limit
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class AudioParser(BaseParser):
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def __init__(self, parser_config=None):
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super().__init__(parser_config=parser_config)
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self._transcript_metadata: Dict[str, Dict] = {}
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def _init_parser(self) -> Dict:
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return {}
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def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
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_ = errors
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if not STTCreator.is_enabled(settings.STT_PROVIDER):
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raise DocumentParseError(
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f"{file.name}: audio files need speech-to-text, which is disabled (STT_PROVIDER=none)."
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)
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try:
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enforce_audio_file_size_limit(file.stat().st_size)
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except OSError:
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pass
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stt = STTCreator.create_stt(settings.STT_PROVIDER)
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result = stt.transcribe(
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file,
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language=settings.STT_LANGUAGE,
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timestamps=settings.STT_ENABLE_TIMESTAMPS,
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diarize=settings.STT_ENABLE_DIARIZATION,
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)
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transcript_metadata = {
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"transcript_duration_s": result.get("duration_s"),
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"transcript_language": result.get("language"),
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"transcript_provider": result.get("provider"),
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}
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if result.get("segments"):
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transcript_metadata["transcript_segments"] = result["segments"]
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self._transcript_metadata[str(file)] = {
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key: value
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for key, value in transcript_metadata.items()
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if value not in (None, [], {})
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}
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return result.get("text", "")
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def get_file_metadata(self, file: Path) -> Dict:
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return self._transcript_metadata.get(str(file), {})
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