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@@ -1,13 +1,12 @@
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"""
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Speech-to-text transcription for recorded calls.
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Speech-to-text transcription for recorded calls using OpenAI Whisper.
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Uses Google Cloud Speech-to-Text v1 (authenticated via the same ADC / service
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account used by firebase-admin and google-cloud-storage).
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Triggered as a background task from the upload endpoint after a call audio
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file has been successfully stored in GCS.
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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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@@ -44,36 +43,51 @@ async def transcribe_call(call_id: str, gcs_uri: str) -> Optional[str]:
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def _sync_transcribe(gcs_uri: str) -> Optional[str]:
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"""Synchronous STT call — run in a thread via asyncio.to_thread."""
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from google.cloud import speech
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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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# Parse gs://bucket/path/to/file.mp3
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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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# Download to a temp file
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if settings.gcp_credentials_path:
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from google.oauth2 import service_account
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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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client = speech.SpeechClient(credentials=creds)
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gcs_client = gcs.Client(credentials=creds)
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else:
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client = speech.SpeechClient()
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gcs_client = gcs.Client()
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audio = speech.RecognitionAudio(uri=gcs_uri)
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config = speech.RecognitionConfig(
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encoding=speech.RecognitionConfig.AudioEncoding.MP3,
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sample_rate_hertz=22050,
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language_code="en-US",
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enable_automatic_punctuation=True,
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model="latest_long",
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)
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bucket = gcs_client.bucket(bucket_name)
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blob = bucket.blob(blob_path)
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# Use long_running_recognize for reliability; it handles both short and long audio
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operation = client.long_running_recognize(config=config, audio=audio)
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response = operation.result(timeout=120)
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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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parts = [
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result.alternatives[0].transcript
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for result in response.results
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if result.alternatives
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]
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return " ".join(parts).strip() or None
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try:
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blob.download_to_filename(tmp_path)
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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="Public safety radio communication. May include police codes, fire, EMS, talkgroup IDs, unit numbers, addresses.",
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)
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return response.text.strip() or None
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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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Logan