amazon-polly-generative

name	amazon-polly-generative
description	Amazon Polly Generative Voices. Reference skill (loaded via skill:// from the ios agent).

Amazon Polly Generative Voices

Overview

Amazon Polly Generative engine produces the most human-like speech synthesis available on AWS. Use it for podcast-style content where naturalness, expressiveness, and conversational flow are critical.

Available Generative Voices

Voice ID	Gender	Language	Best For
Matthew	Male	en-US	Conversational, energetic host
Ruth	Female	en-US	Warm, articulate host
Stephen	Male	en-US	Authoritative, documentary
Gregory	Male	en-US	Calm, educational
Danielle	Female	en-US	Professional, clear

API Usage (boto3)

import boto3

polly = boto3.client('polly', region_name='us-east-1')

response = polly.synthesize_speech(
    Engine='generative',
    OutputFormat='mp3',
    SampleRate='24000',
    Text='<speak>Hello from the road!</speak>',
    TextType='ssml',
    VoiceId='Matthew'
)

# Stream audio to S3
audio_stream = response['AudioStream'].read()

SSML for Natural Speech

Basic Structure

<speak>
  <prosody rate="medium" pitch="medium">
    Welcome back to the show!
  </prosody>
  <break time="500ms"/>
  <prosody rate="slow" pitch="low">
    Did you know that this highway was built in 1952?
  </prosody>
</speak>

Emphasis and Emotion

<speak>
  <emphasis level="strong">Wow</emphasis>, that's incredible!
  <break time="300ms"/>
  <prosody rate="fast" pitch="high">
    I can't believe we're already passing through Banff!
  </prosody>
</speak>

Pauses and Pacing

<!-- Between speakers (natural conversation pause) -->
<break time="800ms"/>

<!-- Dramatic pause -->
<break time="1500ms"/>

<!-- Quick interjection pause -->
<break time="200ms"/>

<!-- Paragraph break -->
<break strength="x-strong"/>

Whispering / Soft Speech

<amazon:effect name="soft">
  This is a little-known secret about this town...
</amazon:effect>

Multi-Voice Podcast Script Format

For two-host podcast content, generate the script in this structured format:

{
  "segmentId": "seg-001",
  "title": "The Ghost Town of Bankhead",
  "duration_target_seconds": 90,
  "dialogue": [
    {
      "speaker": "host_a",
      "voiceId": "Matthew",
      "text": "Okay so get this — we're about to pass through what used to be a thriving coal mining town.",
      "ssml_hints": {
        "rate": "medium",
        "emphasis_words": ["thriving"]
      }
    },
    {
      "speaker": "host_b",
      "voiceId": "Ruth",
      "text": "Wait, a coal mining town? Here in the middle of the Rockies?",
      "ssml_hints": {
        "rate": "medium-fast",
        "pitch": "high",
        "emphasis_words": ["coal mining town"]
      }
    },
    {
      "speaker": "host_a",
      "voiceId": "Matthew",
      "text": "Yep! Bankhead. At its peak in 1911, over a thousand people lived here. They had a swimming pool, a tennis court, even a hockey rink.",
      "ssml_hints": {
        "rate": "medium",
        "emphasis_words": ["thousand", "hockey rink"]
      }
    }
  ]
}

Audio Stitching Pipeline

After generating individual voice clips per dialogue turn:

1. Generate each turn as a separate Polly call (allows per-turn SSML)
2. Add inter-turn silence (300-800ms depending on conversational flow)
3. Concatenate all clips in order using ffmpeg or pydub
4. Normalize audio levels across the full segment
5. Export as MP3 at 24kHz, 128kbps (good quality, reasonable file size)

Lambda Implementation (pydub)

from pydub import AudioSegment
import io

def stitch_dialogue(turns: list[dict], audio_clips: list[bytes]) -> bytes:
    """Stitch individual Polly clips into a single podcast segment."""
    combined = AudioSegment.empty()
    
    for i, (turn, clip) in enumerate(zip(turns, audio_clips)):
        segment = AudioSegment.from_mp3(io.BytesIO(clip))
        combined += segment
        
        # Add pause between speakers
        if i < len(turns) - 1:
            next_speaker = turns[i + 1]['speaker']
            current_speaker = turn['speaker']
            if next_speaker != current_speaker:
                combined += AudioSegment.silent(duration=600)  # Speaker change: 600ms
            else:
                combined += AudioSegment.silent(duration=300)  # Same speaker continuation: 300ms
    
    # Normalize to -16 dBFS (broadcast standard)
    combined = combined.normalize()
    
    output = io.BytesIO()
    combined.export(output, format='mp3', bitrate='128k')
    return output.getvalue()

CDK Configuration

from aws_cdk import aws_iam as iam

# Lambda role needs Polly access
polly_policy = iam.PolicyStatement(
    actions=[
        'polly:SynthesizeSpeech',
        'polly:DescribeVoices',
        'polly:GetSpeechSynthesisTask',
        'polly:StartSpeechSynthesisTask'
    ],
    resources=['*']
)

Cost Optimization

• Generative engine: $0.030 per 1,000 characters
• A 90-second segment is ~1,500 characters = ~$0.045 per segment
• 30 segments per trip = ~$1.35 per trip for audio generation
• Cache aggressively: once generated, audio is immutable — store in S3, serve via CloudFront
• Use StartSpeechSynthesisTask for segments > 3,000 characters (async, outputs to S3 directly)

Limits

• SynthesizeSpeech: max 3,000 characters per call
• StartSpeechSynthesisTask: max 200,000 characters (async, for long content)
• Maximum 100 concurrent synthesis tasks per region
• Generative engine is US regions only (us-east-1, us-west-2)

Best Practices

1. Always use SSML for Generative voices (unlocks natural pacing)
2. Keep individual turns < 500 characters for natural conversation feel
3. Vary prosody between speakers (one faster/higher, one slower/deeper)
4. Add <break> tags at natural pause points (commas, periods aren't enough)
5. Pre-generate all content server-side — never generate on-device
6. Store audio in S3 with Intelligent Tiering (access patterns are unpredictable)
7. Use CloudFront signed URLs for content delivery (time-limited, secure)