20 minute read

“Deploying speech models close to users for low-latency voice experiences.”

1. The Challenge: Speech Requires Real-Time Performance

Speech applications have strict latency requirements:

  • Voice Assistants: Users expect responses in < 300ms.
  • Live Captioning: Must transcribe as the speaker talks (< 100ms lag).
  • Voice Calls: Any delay > 150ms is noticeable and disruptive.

Why Multi-Region Deployment?

  • A single data center in Virginia serves users in Tokyo with 250ms network latency.
  • Deploying ASR models in Tokyo reduces latency to 20ms.

2. Multi-Region Architecture Overview

Architecture:

        Global Load Balancer (AWS Route 53, Cloudflare)
                    |
    ---------------------------------
    |               |               |
  US-West         EU-West        Asia-Pacific
  (Oregon)        (Frankfurt)     (Tokyo)
    |               |               |
  ASR Model       ASR Model       ASR Model
  TTS Model       TTS Model       TTS Model
  Speaker ID      Speaker ID      Speaker ID

Key Components:

  1. Global Load Balancer: Routes users to the nearest region (GeoDNS).
  2. Regional Clusters: Each region has a full stack of speech models.
  3. Model Sync: Ensures all regions serve the same model version.

3. GeoDNS Routing

Concept: Direct users to the closest data center based on their geographic location.

Implementation:

  • AWS Route 53: Geolocation routing policy.
  • Cloudflare: Automatic geo-routing via Anycast.

Example (Route 53):

{
  "Name": "speech-api.example.com",
  "Type": "A",
  "GeoLocation": {
    "ContinentCode": "NA"
  },
  "ResourceRecords": [
    {"Value": "3.12.45.67"}  // US-West IP
  ]
}

Users in North America get routed to 3.12.45.67 (US-West). Users in Europe get routed to EU-West.

Pros:

  • Reduces latency by 80%+.
  • Automatic failover (if a region goes down, route to the next closest).

Cons:

  • DNS caching (TTL = 60s) can delay failover.

4. Edge Deployment for Ultra-Low Latency

For applications like voice calls or gaming, even 50ms is too much. Deploy models at the edge (CDN points of presence).

Edge Locations:

  • AWS has 400+ edge locations.
  • Cloudflare has 300+ PoPs.

Architecture:

User in Berlin → Cloudflare PoP (Berlin) → ASR Model (Frankfurt)
                       ↑
                  Model cached at edge

Implementation (AWS Lambda@Edge):

import json

def lambda_handler(event, context):
    # Run lightweight ASR model at edge
    audio_data = event['body']
    transcript = edge_asr_model.predict(audio_data)
    
    return {
        'statusCode': 200,
        'body': json.dumps({'transcript': transcript})
    }

Trade-offs:

  • Pros: < 10ms latency.
  • Cons: Edge environments have limited compute (no GPU). Must use quantized/lightweight models.

5. Deep Dive: Model Synchronization Across Regions

Challenge: You train a new ASR model in US-West. How do you deploy it to 10 regions?

Strategy 1: Centralized Model Registry

A single S3 bucket (replicated globally) stores all model versions.

Flow:

  1. Upload model to s3://global-models/asr-v100.pt.
  2. S3 replicates to all regions (automated cross-region replication).
  3. Each regional cluster pulls the latest model.

AWS S3 Cross-Region Replication:

{
  "Role": "arn:aws:iam::123456789:role/s3-replication",
  "Rules": [
    {
      "Status": "Enabled",
      "Priority": 1,
      "Filter": {"Prefix": "models/"},
      "Destination": {
        "Bucket": "arn:aws:s3:::models-eu-west",
        "ReplicationTime": {"Status": "Enabled", "Time": {"Minutes": 15}}
      }
    }
  ]
}

Models replicate within 15 minutes.

Strategy 2: Regional Model Stores

Each region has its own S3 bucket. A deployment pipeline copies models to all buckets.

Terraform Script:

resource "aws_s3_bucket" "model_store" {
  for_each = toset(["us-west-2", "eu-west-1", "ap-southeast-1"])
  bucket   = "speech-models-${each.value}"
  region   = each.value
}

resource "aws_s3_bucket_object" "model" {
  for_each = aws_s3_bucket.model_store
  bucket   = each.value.id
  key      = "asr-v100.pt"
  source   = "models/asr-v100.pt"
}

Pros: Independent regions (failure in one doesn’t affect others). Cons: Deployment latency (sequential uploads).

6. Deep Dive: Handling Regional Data Compliance (GDPR)

Problem: EU regulations (GDPR) require that user audio data stays in the EU.

Architecture:

EU User → EU Load Balancer → EU ASR Model → EU Storage
   ↓
Audio NEVER leaves EU

Implementation:

  • Network Policies: Block cross-region traffic from EU to US.
  • IAM Roles: EU instances can only access EU S3 buckets.
# In EU region only
AWS_REGION = "eu-west-1"
s3_client = boto3.client('s3', region_name=AWS_REGION)

# This will fail if model is in US bucket
model = s3_client.get_object(Bucket='models-us-west', Key='asr.pt')
# Error: Access Denied

Separate Training Pipelines:

  • EU Model: Trained only on EU user data.
  • US Model: Trained only on US user data.
  • Models may have different vocabularies (accents, slang).

7. Deep Dive: Canary Deployment in Multi-Region

Scenario: Deploy a new TTS model to 5 regions.

Strategy:

  1. Deploy to 1% of servers in US-West (canary).
  2. Monitor for 24 hours (latency, error rate, voice quality scores).
  3. If healthy, roll out to all servers in US-West.
  4. If still healthy, roll out to EU-West, then Asia-Pacific.

Kubernetes Deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tts-v2-canary
  namespace: us-west
spec:
  replicas: 1  # 1% of 100 total replicas
  selector:
    matchLabels:
      app: tts
      version: v2
  template:
    spec:
      containers:
      - name: tts
        image: tts:v2

Traffic Split (Istio):

apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: tts-service
spec:
  hosts:
  - tts.example.com
  http:
  - match:
    - headers:
        canary:
          exact: "true"
    route:
    - destination:
        host: tts
        subset: v2
  - route:
    - destination:
        host: tts
        subset: v1
      weight: 99
    - destination:
        host: tts
        subset: v2
      weight: 1

8. Deep Dive: Fallback and Disaster Recovery

Scenario: The EU-West data center goes offline (power outage).

Fallback Strategy 1: Route to Nearest Healthy Region

EU User → (EU-West DOWN) → GeoDNS → US-East

Cons: Increased latency (50ms → 150ms).

Fallback Strategy 2: Multi-Region Active-Active

Deploy to 2 regions per continent.

EU User → EU-West (Primary) + EU-Central (Backup)

If EU-West fails, EU-Central takes over instantly.

Cost: 2x infrastructure in each continent.

9. Deep Dive: Caching at Edge and Regional Layers

Speech models are compute-intensive. Caching can reduce load.

What to Cache:

  1. TTS Output: User says “What’s the weather?” every morning.
    • Cache the audio file for “It’s 72°F and sunny” for that user.
  2. Common Queries: “Set a timer for 10 minutes” is a frequent request.
    • Precompute ASR + NLU results.

Redis Caching:

import redis
import hashlib

redis_client = redis.Redis()

def tts_with_cache(text):
    cache_key = hashlib.md5(text.encode()).hexdigest()
    
    # Check cache
    cached_audio = redis_client.get(cache_key)
    if cached_audio:
        return cached_audio
    
    # Generate TTS
    audio = tts_model.synthesize(text)
    
    # Store in cache (TTL = 1 hour)
    redis_client.setex(cache_key, 3600, audio)
    
    return audio

Pros: Reduces TTS latency from 200ms to 5ms (cache hit). Cons: Stale data (if model updates, cache must be invalidated).

10. Deep Dive: Model Compression for Edge Deployment

Edge devices (smartphones, smart speakers) have limited compute. Deploy quantized models.

Quantization:

  • Convert FP32 weights to INT8.
  • Model size: 500 MB → 125 MB.
  • Inference speed: 2x faster.

PyTorch Quantization:

import torch

# Load original model
model = torch.load('asr_fp32.pt')

# Quantize to INT8
quantized_model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

# Save
torch.save(quantized_model, 'asr_int8.pt')

Accuracy Drop: Typically < 1% WER increase.

11. Deep Dive: Monitoring Multi-Region Deployments

Metrics to Track:

  1. Latency per Region: P50, P99 latency for ASR inference.
  2. Error Rate per Region: % of failed requests.
  3. Model Version: Which version is running in each region?
  4. Traffic Distribution: % of traffic per region.

Prometheus Metrics:

from prometheus_client import Counter, Histogram

asr_requests = Counter('asr_requests_total', 'Total ASR requests', ['region', 'model_version'])
asr_latency = Histogram('asr_latency_seconds', 'ASR latency', ['region'])

@app.post("/asr")
def transcribe(audio: bytes):
    region = get_region()
    model_version = get_model_version()
    
    asr_requests.labels(region=region, model_version=model_version).inc()
    
    with asr_latency.labels(region=region).time():
        transcript = asr_model.predict(audio)
    
    return {"transcript": transcript}

Grafana Dashboard:

  • Map View: Show latency heatmap by region.
  • Alerts: If EU-West p99 > 500ms, send alert.

12. Real-World Case Studies

Case Study 1: Google Assistant Multi-Region

Google deploys ASR models to 20+ regions.

Architecture:

  • Each region has a cluster of GPU servers.
  • Models stored in regional Google Cloud Storage buckets.
  • Canary deployments in US-Central1 first, then global rollout.

Result: < 100ms latency for 95% of users globally.

Case Study 2: Amazon Alexa Edge Deployment

Alexa uses a hybrid approach:

  • Wake Word Detection: Runs on-device (edge).
  • Full ASR: Runs in the cloud (regional clusters).

Why?

  • Wake word detection is lightweight (< 1 MB model).
  • Full ASR is heavy (> 500 MB model).

Case Study 3: Zoom’s Real-Time Transcription

Zoom deploys ASR models in 17 AWS regions.

Strategy:

  • Each meeting is routed to the closest region.
  • If the region is overloaded, fallback to the next closest.
  • Models updated weekly via blue-green deployment.

Implementation: Multi-Region Speech API

Step 1: Dockerize the ASR Model

FROM nvidia/cuda:11.8-runtime-ubuntu20.04
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY asr_model.pt .
COPY serve.py .
EXPOSE 8000
CMD ["python", "serve.py"]

Step 2: Deploy to Multi-Region Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: asr-us-west
  namespace: us-west-2
spec:
  replicas: 10
  template:
    spec:
      containers:
      - name: asr
        image: my-registry/asr:v1
        resources:
          limits:
            nvidia.com/gpu: 1
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: asr-eu-west
  namespace: eu-west-1
spec:
  replicas: 10
  template:
    spec:
      containers:
      - name: asr
        image: my-registry/asr:v1
        resources:
          limits:
            nvidia.com/gpu: 1

Step 3: Global Load Balancer (Cloudflare)

curl -X POST "https://api.cloudflare.com/client/v4/zones/{zone_id}/load_balancers" \
  -H "Authorization: Bearer {api_token}" \
  -d '{
    "name": "speech-api.example.com",
    "default_pools": ["us-west", "eu-west", "asia-pacific"],
    "region_pools": {
      "WNAM": ["us-west"],
      "EEUR": ["eu-west"],
      "SEAS": ["asia-pacific"]
    }
  }'

Top Interview Questions

Q1: How do you ensure low latency for global users? Answer:

  • GeoDNS: Route users to the nearest region.
  • Edge Deployment: Deploy lightweight models at CDN edge locations.
  • Caching: Cache frequent TTS outputs and ASR results.

Q2: What happens if a region goes down? Answer:

  • Failover: GeoDNS automatically routes to the next closest healthy region.
  • Active-Active: Deploy to multiple regions per continent for redundancy.
  • Monitoring: Real-time health checks detect failures within seconds.

Q3: How do you handle model updates across 10 regions? Answer:

  • Canary Deployment: Roll out to 1% in one region first.
  • Progressive Rollout: If healthy, deploy to all regions sequentially.
  • Automated Rollback: If error rate spikes, revert to the previous version.

Q4: How do you comply with GDPR for EU users? Answer:

  • Regional Isolation: EU audio data never leaves EU servers.
  • Separate Models: Train EU-specific models on EU data.
  • Network Policies: Block cross-region traffic from EU to other regions.

Q5: What’s the difference between edge and regional deployment? Answer:

  • Regional: Models run in data centers (full GPU, high compute).
  • Edge: Models run at CDN PoPs (limited compute, quantized models).
  • Use Case: Edge for ultra-low latency (< 10ms), Regional for high accuracy.

13. Deep Dive: Streaming ASR with WebRTC

For real-time applications (video calls, live captioning), audio streams chunk-by-chunk over WebRTC.

Challenge: Each audio chunk arrives every 20ms. ASR must process faster than real-time.

Architecture:

User Microphone
    ↓
WebRTC Stream (20ms chunks)
    ↓
Regional ASR Server (Streaming Model)
    ↓
Transcript (partial results every 100ms)

Streaming ASR Implementation:

import grpc
from google.cloud import speech_v1p1beta1 as speech

def stream_transcribe():
    client = speech.SpeechClient()
    
    config = speech.StreamingRecognitionConfig(
        config=speech.RecognitionConfig(
            encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
            sample_rate_hertz=16000,
            language_code='en-US',
        ),
        interim_results=True,  # Get partial results
    )
    
    # Generator for audio chunks
    def audio_generator():
        while True:
            chunk = get_audio_chunk()  # 20ms of audio
            yield speech.StreamingRecognizeRequest(audio_content=chunk)
    
    requests = audio_generator()
    responses = client.streaming_recognize(config, requests)
    
    for response in responses:
        for result in response.results:
            if result.is_final:
                print(f"Final: {result.alternatives[0].transcript}")
            else:
                print(f"Partial: {result.alternatives[0].transcript}")

Key Requirement: Model must process in < 20ms to keep up with audio stream.

14. Deep Dive: Voice Quality Monitoring

How do we measure if multi-region speech systems are working well?

Metrics:

1. Word Error Rate (WER): \[ \text{WER} = \frac{\text{Substitutions} + \text{Insertions} + \text{Deletions}}{\text{Total Words}} \]

2. Mean Opinion Score (MOS) for TTS:

  • Human raters score voice quality from 1 (terrible) to 5 (excellent).
  • Target: MOS > 4.0.

3. Real-Time Factor (RTF): \[ \text{RTF} = \frac{\text{Processing Time}}{\text{Audio Duration}} \]

  • RTF < 1.0 means faster than real-time.
  • Target for streaming: RTF < 0.5.

Automated Testing:

import time

def measure_rtf(asr_model, audio_file):
    audio_duration = get_audio_duration(audio_file)  # e.g., 10 seconds
    
    start = time.time()
    transcript = asr_model.transcribe(audio_file)
    processing_time = time.time() - start
    
    rtf = processing_time / audio_duration
    print(f"RTF: {rtf:.2f}")
    
    if rtf > 1.0:
        print("WARNING: Model is slower than real-time!")
    
    return rtf

15. Deep Dive: Bandwidth Management

Streaming audio consumes significant bandwidth. Optimization is critical for mobile users.

Audio Compression:

  • Uncompressed PCM: 16kHz, 16-bit = 256 kbps
  • Opus Codec: 16 kbps (16x compression)
  • Speex: 8 kbps (ultra-low bitrate)

Implementation:

import pyaudio
import opuslib

def stream_compressed_audio():
    p = pyaudio.PyAudio()
    encoder = opuslib.Encoder(16000, 1, opuslib.APPLICATION_VOIP)
    
    stream = p.open(format=pyaudio.paInt16,
                    channels=1,
                    rate=16000,
                    input=True,
                    frames_per_buffer=320)  # 20ms chunks
    
    while True:
        audio_chunk = stream.read(320)
        compressed = encoder.encode(audio_chunk, 320)
        
        # Send compressed chunk to server
        send_to_server(compressed)

Trade-off: Lower bitrate = worse audio quality = higher WER.

16. Deep Dive: Cost Optimization for Global Speech

Running GPU-based ASR globally is expensive. How do we reduce cost?

Strategy 1: CPU Inference with ONNX Runtime Convert model from PyTorch to ONNX for optimized CPU inference.

import torch
import onnx

# Export to ONNX
dummy_input = torch.randn(1, 80, 100)  # Mel spectrogram
torch.onnx.export(asr_model, dummy_input, "asr.onnx")

# Inference with ONNX Runtime (2-3x faster than PyTorch CPU)
import onnxruntime as ort

session = ort.InferenceSession("asr.onnx")
outputs = session.run(None, {"input": audio_features})

Result: CPU instances are 5x cheaper than GPU instances.

Strategy 2: Autoscaling Based on Region-Specific Traffic Don’t run 24/7 in all regions. Scale down at night.

Traffic Patterns:

  • US-West: Peak at 2pm PST (5pm EST).
  • EU-West: Peak at 2pm CET (8am EST).
  • Asia-Pacific: Peak at 2pm JST (1am EST).

Autoscaling Policy:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: asr-us-west
spec:
  scaleTargetRef:
    kind: Deployment
    name: asr-us-west
  minReplicas: 2   # Night
  maxReplicas: 50  # Day
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 60

Strategy 3: Spot Instances for Batch Transcription For non-real-time workloads (podcast transcription), use spot instances.

# Kubernetes Toleration for Spot Instances
spec:
  tolerations:
  - key: "spot"
    operator: "Equal"
    value: "true"
    effect: "NoSchedule"
  nodeSelector:
    instance-type: "spot"

17. Deep Dive: Multi-Language Support

Challenge: Support 100+ languages across all regions.

Option 1: Separate Models per Language

  • Deploy asr-en, asr-fr, asr-es, etc.
  • Pros: Best accuracy per language.
  • Cons: 100x storage and compute.

Option 2: Multilingual Model

  • Single model trained on 100 languages.
  • Pros: 1x storage, universal model.
  • Cons: Slightly lower accuracy per language.

Hybrid Approach: Deploy multilingual model globally. Deploy language-specific models in high-demand regions.

def get_model(language_code, region):
    if language_code == 'en' and region == 'us-west':
        return load_model('asr-en-specialized')
    else:
        return load_model('asr-multilingual')

18. Deep Dive: Failover Testing and Chaos Engineering

Problem: How do you verify that failover actually works?

Chaos Engineering Test:

  1. Simulate Region Failure: Terminate all pods in EU-West.
  2. Observe: Do EU users get routed to US-East?
  3. Measure: What’s the latency increase? Any errors?

Automated Failover Test:

import requests
import time

def test_failover():
    # Baseline: All regions healthy
    response = requests.get("https://speech-api.example.com/health")
    assert response.json()['all_healthy'] == True
    
    # Simulate EU-West failure
    kill_region('eu-west')
    
    time.sleep(10)  # Wait for DNS update
    
    # Test from EU client
    start = time.time()
    response = requests.post(
        "https://speech-api.example.com/asr",
        data=audio_data,
        headers={"X-Client-Location": "EU"}
    )
    latency = time.time() - start
    
    assert response.status_code == 200
    assert latency < 200  # Acceptable degraded latency
    
    print(f"Failover successful. Latency: {latency*1000:.0f}ms")

Run Monthly: Ensure team knows how to handle regional outages.

19. Deep Dive: Shadow Traffic for Model Validation

Before deploying a new ASR model to prod, validate it using shadow traffic.

Shadow Traffic Strategy:

  1. Deploy new model alongside old model.
  2. Send 100% of live traffic to both models.
  3. Serve old model’s response to users.
  4. Log new model’s response for comparison.
import asyncio

async def shadow_predict(audio):
    # Primary prediction
    primary_task = asyncio.create_task(model_v1.predict(audio))
    
    # Shadow prediction (async, non-blocking)
    shadow_task = asyncio.create_task(model_v2.predict(audio))
    
    # Wait for primary
    primary_result = await primary_task
    
    # Log shadow result (don't wait)
    asyncio.create_task(log_shadow_result(shadow_task))
    
    return primary_result

Comparison Metrics:

  • WER difference
  • Latency difference
  • Vocabulary coverage

20. Production War Stories

War Story 1: The DNS Caching Disaster A team deployed to a new region. Updated DNS. But users kept going to the old region for 2 hours. Root Cause: ISPs cached DNS records (TTL = 3600s). Lesson: Set TTL = 60s for DNS records that might change.

War Story 2: The Cross-Region Bandwidth Bill A team accidentally routed EU traffic to US servers for a week. Cost: $50,000 in cross-region data transfer fees. Lesson: Monitor traffic routing with dashboards.

War Story 3: The GDPR Violation A bug caused EU user audio to be logged in US servers. Result: GDPR fine, PR disaster. Lesson: Implement fail-safe network policies (block cross-region traffic at firewall level).

21. Deep Dive: Network Optimization for Speech

Speech data requires significant bandwidth. Optimize network usage.

Optimization 1: WebSocket Connection Pooling Reuse connections instead of creating new ones for each request.

import websockets
import asyncio

class ConnectionPool:
    def __init__(self, uri, pool_size=10):
        self.uri = uri
        self.pool = asyncio.Queue(maxsize=pool_size)
        asyncio.create_task(self._fill_pool(pool_size))
    
    async def _fill_pool(self, size):
        for _ in range(size):
            conn = await websockets.connect(self.uri)
            await self.pool.put(conn)
    
    async def get_connection(self):
        return await self.pool.get()
    
    async def return_connection(self, conn):
        await self.pool.put(conn)

# Usage
pool = ConnectionPool('wss://speech-api.example.com')

async def stream_audio(audio_data):
    conn = await pool.get_connection()
    try:
        await conn.send(audio_data)
        response = await conn.recv()
        return response
    finally:
        await pool.return_connection(conn)

Optimization 2: gRPC Streaming with Multiplexing gRPC multiplexes multiple streams over a single TCP connection.

import grpc
from concurrent import futures

class SpeechService:
    def StreamingRecognize(self, request_iterator, context):
        for request in request_iterator:
            audio_chunk = request.audio_content
            transcript = asr_model.process_chunk(audio_chunk)
            yield SpeechResponse(transcript=transcript)

# Server
server = grpc.server(futures.ThreadPoolExecutor(max_workers=100))
add_SpeechServiceServicer_to_server(SpeechService(), server)
server.add_insecure_port('[::]:50051')
server.start()

22. Deep Dive: Latency SLAs and Penalties

Production systems often have strict latency SLAs.

Example SLA:

  • P50 latency: < 50ms (99% of time)
  • P95 latency: < 150ms
  • P99 latency: < 300ms

Penalty: If P95 > 150ms for > 1 hour, pay $10,000.

How to Meet SLAs:

  1. Over-provision: Run 30% more replicas than needed.
  2. Circuit Breaker: If a region’s latency spikes, stop routing traffic there.
  3. Fallback: Route to next-closest region if primary is overloaded.

Circuit Breaker Implementation:

from enum import Enum
import time

class CircuitState(Enum):
    CLOSED = "closed"  # Normal operation
    OPEN = "open"      # Circuit tripped
    HALF_OPEN = "half_open"  # Testing recovery

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60):
        self.state = CircuitState.CLOSED
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.last_failure_time = 0
    
    def call(self, func, *args, **kwargs):
        if self.state == CircuitState.OPEN:
            if time.time() - self.last_failure_time > self.timeout:
                self.state = CircuitState.HALF_OPEN
            else:
                raise Exception("Circuit breaker is OPEN")
        
        try:
            result = func(*args, **kwargs)
            if self.state == CircuitState.HALF_OPEN:
                self.state = CircuitState.CLOSED
                self.failure_count = 0
            return result
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = time.time()
            
            if self.failure_count >= self.failure_threshold:
                self.state = CircuitState.OPEN
            raise e

# Usage
breaker = CircuitBreaker()

def call_asr_service(audio):
    return breaker.call(asr_model.transcribe, audio)

23. Production Deployment Checklist

Before deploying speech models to production, verify:

Pre-Launch Checklist:

  • Load Testing: Test with 10x expected peak traffic.
  • Failover Drill: Kill one region, verify traffic shifts.
  • Latency Benchmarks: P99 < 300ms in all regions.
  • GDPR Compliance: EU data stays in EU.
  • Monitoring Dashboards: Grafana dashboards for each region.
  • Alerting Rules: PagerDuty alerts for p99 > 500ms.
  • Runbooks: Documented procedures for common incidents.
  • Rollback Plan: Can revert to previous model in < 5 minutes.

Load Testing Script:

import asyncio
import aiohttp
import time

async def stress_test(url, audio_file, num_requests=10000):
    async with aiohttp.ClientSession() as session:
        tasks = []
        start = time.time()
        
        for i in range(num_requests):
            task = session.post(url, data=open(audio_file, 'rb'))
            tasks.append(task)
        
        responses = await asyncio.gather(*tasks)
        
        duration = time.time() - start
        rps = num_requests / duration
        
        latencies = [r.headers.get('X-Latency-Ms') for r in responses]
        p99 = sorted([float(l) for l in latencies if l])[int(len(latencies) * 0.99)]
        
        print(f"RPS: {rps:.0f}")
        print(f"P99 Latency: {p99:.0f}ms")

# Run
asyncio.run(stress_test(
    'https://speech-api.example.com/asr',
    'test_audio.wav',
    num_requests=10000
))

Trend: Instead of running servers 24/7, use serverless (AWS Lambda, Google Cloud Run).

Pros:

  • Cost: Pay only for actual usage (not idle time).
  • Auto-Scaling: Scales to zero when not in use.

Cons:

  • Cold Start: First request is slow (5-10 seconds to load model).
  • Memory Limits: Lambda has 10GB max memory.

Workaround: Provisioned Concurrency Keep N instances warm at all times.

# AWS Lambda with Provisioned Concurrency
Resources:
  SpeechFunction:
    Type: AWS::Lambda::Function
    Properties:
      Runtime: python3.9
      Handler: app.handler
      MemorySize: 10240  # 10 GB
      Timeout: 60
  
  ProvisionedConcurrency:
    Type: AWS::Lambda::Alias
    Properties:
      FunctionName: !Ref SpeechFunction
      ProvisionedConcurrencyConfig:
        ProvisionedConcurrentExecutions: 10  # Keep 10 warm

Key Takeaways

  1. Multi-Region is Essential: Reduces latency and ensures high availability.
  2. GeoDNS: Routes users to the nearest region automatically.
  3. Edge Deployment: For ultra-low latency, deploy quantized models at the edge.
  4. Canary Deployments: Reduce risk when rolling out new models.
  5. GDPR Compliance: Regional isolation is critical for EU users.

Summary

Aspect Insight
Goal Low latency, high availability, global reach
Architecture Multi-region clusters + GeoDNS + Edge caching
Challenges Model sync, data compliance, disaster recovery
Key Metrics Latency (p99), error rate, traffic distribution

Originally published at: arunbaby.com/speech-tech/0033-multi-region-speech-deployment

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