Batch Text-to-Speech Deep Dive: From One Speech Call to a Produced Show
Blog post from Deepgram
Producing a long-form podcast from batch text-to-speech requires a pipeline that treats each spoken line as an independent, stateless render and manages pacing, timing, audio formatting, and metadata after generation. Using Deepgram Flux TTS and the HN Radio project as an example, the process recommends requesting headerless linear16 PCM audio so segments can be concatenated safely, calculating planned gaps and chapter offsets before joining because concatenation removes boundary information, and writing a single WAV header only after final audio length is known. It emphasizes measuring and normalizing the leading and trailing silence already included in generated segments, then applying distinct gap durations for conversational turns and structural transitions to avoid an unnatural monologue-like rhythm. Final output should be encoded as a 44.1 kHz, 128 kbps MP3 with ID3v2.3 metadata, since technically valid 24 kHz MPEG-2 MP3 files or newer tag versions may appear in podcast applications but fail to play. The approach also separates source-specific editorial work, such as selecting stories and supplying commentary, from the reusable audio pipeline, allowing it to be adapted for sources ranging from news and changelogs to sports or lecture material.
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