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Echo-TTS

dignome

GGUF files released for the audio.cpp echo_tts model. Includes the s1 codec weights. This work is not affiliated or endorsed by the original author, Jordan Darefsky (https://huggingface.co/jordand/echo-tts-base) Echo-TTS is a diffusion-based text-to-speech model. The weights in this repository are intended for **research and non-commercial use only**.

Speech synthesis

Input Text→ Output Audio

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kitten_tts2

dignome

For the latest models please see audio.cpp project main. This is no longer maintained. Self-contained multilingual GGUF exports of KittenML/kitten-tts-2 weights at revision `f2eee74f4467855ad58fe16f1fb7b7df473263fd`. Each file includes an S3 MeanFlow decoder, speaker encoders, tokenizer, and all 48 prepared voice entries. Use an audio.cpp build containing the updated Kitten TTS 2 BF16/Q8/Q4 support. The model family is `kitten_tts2`. BF16/Q8/Q4 model-manager installations share `models/kitten-tts2-v2/`, with a distinct filename for each precision. Leave `kitten_tts2.weight_type` unset, or use `native`, to preserve each file's stored precision. Inference needs no Python or LibTorch installation.

Speech synthesis

Input Text→ Output Audio

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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