AI Voices for Batch Voiceover Pipelines
At volume the cost driver is duplication, not generation. Hashing text and skipping what already exists removes most of it, since catalog and template work repeats the same phrases constantly.
VOICES FOR THIS USE CASE
Caching is the main cost lever
In catalog and template work, repeated phrases are usually a large share of total volume. Hashing the text and skipping anything already generated removes that duplication entirely. This sits on your side, and in most bulk pipelines it saves more than any other single optimisation.
Keeping output consistent at volume
Across thousands of items, small variations become visible. Fixing the voice, speed and sample rate for an entire run is what keeps a product catalog sounding uniform. Pronunciation dictionaries handle recurring brand and product names so those do not need per item correction.
Sizing a batch job
Requests cap at 250 characters, so a job is a queue of small calls rather than a few large ones. Concurrency is set by your plan, so check that before sizing a run. Throughput is generally limited by concurrency rather than by generation speed, which runs at 3.3 times real time.
Formats and storage
Output is 44.1 kHz natively, available as PCM, MP3 or WAV. For large libraries MP3 keeps storage manageable, while WAV is worth retaining for anything that may be re-edited later. Generating both in one pass is cheaper than regenerating from scratch when a format need changes.
SPECIFICATION
Sample rate
44.1 kHz native, resampled to 8 kHz for telephony
Latency
~200 ms to first byte (p50, warm region)
Output formats
ulaw, alaw, PCM 16-bit, WAV, MP3
Streaming transports
WebSocket, HTTP chunked transfer
Speed range
0.5× – 2.0×, set per request
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AI Voices for Batch Voiceover Pipelines
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