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MOSS-TTS-v1.5-GGUF

christopherthompson81

Self-contained GGUF packages of OpenMOSS-Team/MOSS-TTS-v1.5, for audio.cpp. Each file carries the 8B delay-pattern backbone, the MOSS-Audio-Tokenizer v1 codec (**both** encoder and decoder, because cloning needs the encoder), the tokenizer and the model spec — nothing else to download. q4_k is usable and clones as well as the others — but it is **not lossless on the words**. Across four takes of the same sentence at different seeds, one substituted an article ("the" for "this"); q8_0 was exact on all four. Two independent ASR models agree on that substitution, so it is the audio and not a transcription artifact. Four takes of one sentence is a small sample and the true rate is not pinned down by it. Treat q4_k as the option for when 9.3 GB versus 13.2 GB decides whether the model runs at all, and prefer q8_0 when it fits.

Speech synthesis

Input Text→ Output Audio

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MOSS-TTSD-GGUF

christopherthompson81

Self-contained GGUF packages of OpenMOSS-Team/MOSS-TTSD-v1.0, for audio.cpp. Each file carries the 8B delay-pattern backbone, the MOSS-Audio-Tokenizer v1 codec (**both** encoder and decoder, because cloning needs the encoder), the tokenizer and the model spec — nothing else to download. MOSS-TTSD speaks a speaker-tagged dialogue in one take: `[S1]` and `[S2]` mark who is talking, and each tag can carry its own reference recording. `OpenMOSS-Team/MOSS-TTSD-v1.0` ships no codec weights — its processor resolves `OpenMOSS-Team/MOSS-Audio-Tokenizer` at load time — so both are listed as parent models. This checkpoint reads the first 16 of that codec's 32 RVQ layers.

Speech synthesis

Input Text→ Output Audio

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VibeVoice-ASR-Streaming-1.5B-GGUF

christopherthompson81

This repository contains audio.cpp-native GGUF builds of `microsoft/VibeVoice-ASR-Streaming-1.5B`. The 1.5B checkpoint runs through the same audio.cpp loader as the 7B with no code changes: the layer count, hidden size, and head counts are read from the checkpoint's own `config.json`, and the tensor names are identical. Each GGUF is self-contained and embeds the audio.cpp package spec and required sidecars.

Speech recognition

Input Audio→ Output Text

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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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Explore AI models 3

MOSS-TTS-v1.5-GGUF

MOSS-TTSD-GGUF

VibeVoice-ASR-Streaming-1.5B-GGUF

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