Logan
137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
"""
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Speech-to-text transcription for recorded calls using OpenAI Whisper.
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Audio is downloaded from GCS then sent to the Whisper API. Falls back to
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returning None on any failure so the intelligence pipeline can still run.
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"""
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import asyncio
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import tempfile
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import os
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from typing import Optional
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from app.internal.logger import logger
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from app.internal import firestore as fstore
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# Whisper treats `prompt` as preceding transcript text, not instructions.
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# Writing it as actual radio speech primes the vocabulary toward P25 codes
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# and phrasing before the model hears the audio.
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_WHISPER_PROMPT = (
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"10-4. 10-23. 10-20. 10-97. 10-8. 10-7. 10-34. 10-50. 10-52. "
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"Post 4, I'm out. Post 3. En route. On scene. In route. "
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"Copy. Negative. Stand by. Be advised. Go ahead. "
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"Units responding. Dispatch. Talkgroup. "
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"Engine. Ladder. Medic. Rescue. Car. Unit. "
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"MVA. MVC. Structure fire. Working fire."
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)
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async def transcribe_call(
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call_id: str,
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gcs_uri: str,
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talkgroup_name: Optional[str] = None,
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system_id: Optional[str] = None,
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) -> tuple[Optional[str], list[dict]]:
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"""
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Transcribe audio at the given GCS URI and store the result in Firestore.
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Returns:
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(transcript, segments) — segments is a list of {start, end, text} dicts,
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one per detected transmission. Empty list if transcription failed.
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"""
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if not gcs_uri or not gcs_uri.startswith("gs://"):
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return None, []
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# Load vocabulary for this system (empty list if none yet)
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vocabulary: list[str] = []
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if system_id:
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from app.internal.vocabulary_learner import get_vocabulary
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vocab_data = await get_vocabulary(system_id)
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vocabulary = vocab_data.get("vocabulary") or []
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try:
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transcript, segments = await asyncio.to_thread(
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_sync_transcribe, gcs_uri, talkgroup_name, vocabulary
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)
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except Exception as e:
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logger.warning(f"Transcription failed for call {call_id}: {e}")
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return None, []
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if transcript:
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updates: dict = {"transcript": transcript}
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if segments:
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updates["segments"] = segments
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try:
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await fstore.doc_set("calls", call_id, updates)
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logger.info(
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f"Transcript saved for call {call_id} "
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f"({len(transcript)} chars, {len(segments)} segment(s))"
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)
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except Exception as e:
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logger.warning(f"Could not save transcript for {call_id}: {e}")
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return transcript, segments
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def _sync_transcribe(
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gcs_uri: str,
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talkgroup_name: Optional[str] = None,
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vocabulary: Optional[list[str]] = None,
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) -> tuple[Optional[str], list[dict]]:
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"""Download audio from GCS and transcribe with OpenAI Whisper."""
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from google.cloud import storage as gcs
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from google.oauth2 import service_account
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from openai import OpenAI
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from app.config import settings
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if not settings.openai_api_key:
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logger.warning("OPENAI_API_KEY not set — transcription disabled.")
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return None
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without_scheme = gcs_uri[len("gs://"):]
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bucket_name, blob_path = without_scheme.split("/", 1)
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if settings.gcp_credentials_path:
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creds = service_account.Credentials.from_service_account_file(
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settings.gcp_credentials_path,
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scopes=["https://www.googleapis.com/auth/cloud-platform"],
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)
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gcs_client = gcs.Client(credentials=creds)
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else:
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gcs_client = gcs.Client()
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bucket = gcs_client.bucket(bucket_name)
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blob = bucket.blob(blob_path)
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suffix = os.path.splitext(blob_path)[1] or ".mp3"
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with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
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tmp_path = tmp.name
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try:
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blob.download_to_filename(tmp_path)
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from app.internal.vocabulary_learner import build_whisper_vocab_prompt
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vocab_prefix = build_whisper_vocab_prompt(vocabulary or [])
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tg_prefix = f"Talkgroup: {talkgroup_name}. " if talkgroup_name else ""
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prompt = tg_prefix + vocab_prefix + _WHISPER_PROMPT
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openai_client = OpenAI(api_key=settings.openai_api_key)
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with open(tmp_path, "rb") as f:
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response = openai_client.audio.transcriptions.create(
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model="whisper-1",
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file=f,
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language="en",
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prompt=prompt,
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response_format="verbose_json",
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)
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text = response.text.strip() or None
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segments = [
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{"start": round(s.start, 2), "end": round(s.end, 2), "text": s.text.strip()}
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for s in (response.segments or [])
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if s.text.strip()
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]
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return text, segments
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finally:
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try:
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os.unlink(tmp_path)
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except OSError:
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pass
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