pipelines go to gravenhollow now.

voice_cloning_pipeline.py

@@ -29,7 +29,7 @@ def transcribe_and_diarise(
     s3_key: str,
     whisper_url: str = "http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper",
 ) -> NamedTuple("TranscriptOutput", [("transcript_json", str), ("speakers", str), ("audio_path", str)]):
-    """Download audio from Quobjects S3, transcribe via Whisper with timestamps."""
+    """Download audio from S3, transcribe via Whisper with timestamps."""
     import json
     import os
     import subprocess
@@ -41,7 +41,7 @@ def transcribe_and_diarise(
     out = NamedTuple("TranscriptOutput", [("transcript_json", str), ("speakers", str), ("audio_path", str)])
     work = tempfile.mkdtemp()
 
-    # ── Download audio from Quobjects S3 ─────────────────────
+    # ── Download audio from S3 ─────────────────────
     ext = os.path.splitext(s3_key)[-1] or ".wav"
     audio_path = os.path.join(work, f"audio_raw{ext}")
 
@@ -609,7 +609,7 @@ def voice_cloning_pipeline(
     # MLflow
     mlflow_tracking_uri: str = "http://mlflow.mlflow.svc.cluster.local:80",
 ):
-    # 1 - Download from Quobjects S3 and transcribe with Whisper
+    # 1 - Download from S3 and transcribe with Whisper
     transcribed = transcribe_and_diarise(
         s3_endpoint=s3_endpoint,
         s3_bucket=s3_bucket,