Low-latency speech-to-text, real-time speaker diarization, and automated PII redaction for high-volume contact centers.
Standard foundation speech models struggle in noisy telephonic environments. We fine-tune OpenAI Whisper v3 models specifically for enterprise contact centers to deliver sub-20ms streaming latency, multi-speaker separation, and instant PCI-DSS/HIPAA PII masking.
Production-tested execution flow designed for multi-region reliability and security.
Consumes raw 8kHz G.711 / 16kHz PCM audio streams over WebSockets.
DeepFilterNet removes background office noise and crosstalk.
Accelerated FP16/INT8 inference delivers transcript tokens continuously.
Identifies agent vs customer speaker channels and redacts sensitive PII.
Adapts Whisper v3 weights to specialized industry terms, medical jargon, product SKUs, and accents.
Tracks speaker embeddings continuously using PyAnnote 3.1 to attribute transcripts accurately.
Redacts credit card numbers, Social Security Numbers, and addresses before text touches database logs.
Feeds real-time sentiment signals into manager dashboards and agent co-pilot recommendations.
import asyncio
import websockets
from whisper_tensorrt import WhisperTRTEngine
engine = WhisperTRTEngine("whisper-v3-callcenter-int8.engine")
async def handle_telephony_stream(websocket, path):
async for audio_chunk in websocket:
# 16-bit 16kHz PCM audio buffer
denoised_pcm = engine.denoise(audio_chunk)
tokens = engine.transcribe_chunk(denoised_pcm)
redacted_text = engine.redact_pii(tokens)
await websocket.send_json({
"speaker": tokens.speaker_id,
"transcript": redacted_text,
"latency_ms": tokens.latency
})
print("✓ Call Center Speech AI Server Online on port 8080")We fine-tune Whisper v3 cross-attention layers on augmented contact center audio containing ambient chatter and phone compression codecs (G.711), dropping Word Error Rate (WER) dramatically compared to off-the-shelf APIs.
Yes. Our pipeline executes regular expression pattern matching and named entity recognition (NER) in memory prior to sending transcripts to database storage or WebSocket clients.
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