Architecting Real-Time Telephony Audio Redaction & Dual-Channel Diarization with Fine-Tuned Whisper v3, Amazon Kinesis Video Streams, and PyAnnote on AWS EKS

01 Telephony Audio Ingestion & Regulatory Compliance Challenges

Enterprise telecommunication stacks (such as Genesys Cloud, Cisco Webex Contact Center, Avaya, and Amazon Connect) transmit streaming voice payloads over SIP/RTP media sessions. Unlike high-fidelity 44.1kHz studio microphones, telephone audio presents distinct physical and acoustic barriers:

To overcome these limitations, platform engineers decouple incoming RTP streams into discrete inbound and outbound channels, route packets through low-latency Kinesis streams, execute acoustic denoising, and perform sub-second text and audio packet zeroing before writing any artifacts to persistent S3 tables.

02 Streaming End-to-End Topology with Amazon Kinesis Video Streams (KVS)

Amazon Kinesis Video Streams (KVS) provides native real-time ingestion for two-way audio RTP sessions. When a telephonic call connects via Amazon Connect or a SIP Trunk Session Border Controller (SBC), raw G.711 PCM audio frames are pushed continuously into a KVS track:

+-------------------------+               +--------------------------------------+
|  Telephony SBC / SIP    |  (RTP Audio)  |  Amazon Kinesis Video Streams (KVS)  |
|  (Amazon Connect Trunk) | ------------> |  - Channel A: Inbound (Customer)     |
+-------------------------+               |  - Channel B: Outbound (Agent)       |
                                          +------------------+-------------------+
                                                             |
                                                             | Boto3 KVS Fragment Parser
                                                             v
+--------------------------------------------------------------------------------+
|                         AWS EKS GPU Inference Pod Fleet                        |
|                                                                                |
|   1. DeepFilterNet 2 Acoustic Denoising (Low-Order Bandpass & Crosstalk Cancel) |
|   2. PyAnnote 3.1 Dual-Channel Speaker Diarization Embeddings                  |
|   3. NVIDIA TensorRT-LLM Whisper v3 (INT8 FP16 Compressed Decoder)            |
|   4. Spacy Transformer Named Entity Recognition (NER) + PCI-DSS Redactor       |
+--------------------------------------------------------------------------------+
                                       |
                   +-------------------+-------------------+
                   | (Redacted PCM Audio)                  | (Redacted Text + Sentiment)
                   v                                       v
        +----------------------+               +-----------------------+
        | Amazon S3 Tables     |               | Amazon OpenSearch     |
        | (Encrypted Clean WORM|               | Serverless Vector RAG |
        +----------------------+               +-----------------------+
    

By splitting customer audio (Track 1) from agent audio (Track 2) directly at the media gateway layer, the ASR inference cluster eliminates acoustic echo cancellation artifacts and guarantees isolated speaker attribution.

03 Fine-Tuning Whisper v3 on G.711 Telephony Codecs & Acoustic Denoising

The base Whisper v3 large architecture trained on uncompressed 16kHz internet audio exhibits higher Word Error Rates when encountering 8kHz $\mu$-law telephony compression. To achieve high transcript precision, AIConnect applies Parameter-Efficient Fine-Tuning (LoRA / QLoRA) across the Whisper cross-attention projection layers (q_proj, v_proj) using thousands of hours of curated domain audio:

• Acoustic Audio Augmentation: Audio training samples are downsampled to 8kHz, encoded with G.711u compression, and augmented with background call center noise profiles (keyboard clicks, distant speech, line static).
• Domain Vocabulary Boosting: Custom language modeling prefixes bias decoding toward alphanumeric serial numbers, insurance policy prefixes, and financial terminology.
• DeepFilterNet In-Line Denoising: Prior to feeding spectrogram chunks into Whisper mel-filterbanks, DeepFilterNet suppresses ambient room reflections with sub-5ms algorithmic latency.

04 Dual-Channel PyAnnote Diarization & Audio Packet Muting Mechanics

Redacting sensitive PII from written transcripts is insufficient to meet PCI-DSS compliance; the recorded WAV/MP3 audio payload stored in cloud archives must also have the spoken payment card digits muted or tone-masked.

The streaming worker coordinates transcript timestamps directly with raw PCM audio buffers:

05 Production Python AsyncIO & Boto3 Stream Redaction Worker

Below is a complete, production-grade Python worker implementation consuming streaming audio chunks, performing PII redaction, and generating aligned transcripts:

import asyncio
import re
import numpy as np
from typing import Dict, Any, List

class StreamingTelephonyRedactor:
    def __init__(self, sample_rate: int = 16000):
        self.sample_rate = sample_rate
        # Matches 16-digit credit cards or 9-digit SSNs
        self.pii_pattern = re.compile(r'\b(?:\d[ -]*?){13,16}\b|\b\d{3}-\d{2}-\d{4}\b')

    def redact_audio_samples(self, pcm_bytes: bytes, start_ms: int, end_ms: int) -> bytes:
        """Zeros out raw PCM audio buffer during spoken PII intervals."""
        audio_array = np.frombuffer(pcm_bytes, dtype=np.int16).copy()
        start_idx = int((start_ms / 1000.0) * self.sample_rate)
        end_idx = int((end_ms / 1000.0) * self.sample_rate)

        # Zero-out samples to silence spoken PII
        audio_array[start_idx:end_idx] = 0
        return audio_array.tobytes()

    def process_transcript_segment(
        self,
        raw_pcm: bytes,
        speaker: str,
        transcript: str,
        start_ms: int,
        end_ms: int
    ) -> Dict[str, Any]:
        has_pii = bool(self.pii_pattern.search(transcript))
        clean_text = self.pii_pattern.sub("[REDACTED_PCI_PAN]", transcript)
        clean_audio = self.redact_audio_samples(raw_pcm, start_ms, end_ms) if has_pii else raw_pcm

        return {
            "speaker_channel": speaker,
            "original_duration_ms": end_ms - start_ms,
            "redacted_transcript": clean_text,
            "pii_detected": has_pii,
            "sanitized_audio_bytes": len(clean_audio)
        }

async def run_telephony_worker_demo():
    redactor = StreamingTelephonyRedactor()
    # Simulated 3-second 16kHz 16-bit mono PCM buffer
    mock_pcm = b"\x00" * (16000 * 2 * 3)
    mock_transcript = "My payment card number is 4111 2222 3333 4444 for verification."

    result = redactor.process_transcript_segment(
        raw_pcm=mock_pcm,
        speaker="CUSTOMER_CHANNEL_1",
        transcript=mock_transcript,
        start_ms=1000,
        end_ms=2800
    )
    print("✓ Telephony Stream Chunk Processed Successfully:")
    print(f"Transcript: {result['redacted_transcript']}")
    print(f"PII Flagged: {result['pii_detected']}")

if __name__ == "__main__":
    asyncio.run(run_telephony_worker_demo())

06 Kubernetes DaemonSet & NVIDIA TensorRT-LLM Infrastructure

To sustain concurrent audio streams for thousands of call center agents, the inference architecture deploys NVIDIA Triton Inference Server running TensorRT-LLM compiled Whisper v3 INT8 engines across Amazon EKS g5.2xlarge (NVIDIA A10G) instance worker groups:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: whisper-telephony-worker
  namespace: speech-intelligence
spec:
  replicas: 12
  selector:
    matchLabels:
      app: whisper-telephony-worker
  template:
    metadata:
      labels:
        app: whisper-telephony-worker
    spec:
      containers:
      - name: whisper-engine
        image: 123456789012.dkr.ecr.us-east-1.amazonaws.com/whisper-trt:v3-callcenter
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 16Gi
            cpu: "8"
          requests:
            nvidia.com/gpu: 1
            memory: 8Gi
            cpu: "4"
        ports:
        - containerPort: 8080
          name: websocket-stream
        env:
        - name: MODEL_NAME
          value: "whisper-v3-callcenter-int8"
        - name: MAX_CONCURRENT_AUDIO_STREAMS
          value: "64"