Available Models

Here are the models currently available. See Downloading Models for how to obtain them. This library uses the Onnx model format, converted to the memory-mappable OnnxRuntime (.ort) flatbuffer encoding. For safetensor versions, see the HuggingFace section.

Moonshine models are MIT by default, in every language and at every size. Streaming speech-to-text is what each language with a streaming model now selects by default. The older non-streaming models for languages other than English stay under the non-commercial Moonshine Community License; all other STT models are under the MIT License. Where a streaming replacement exists they are deprecated and remain reachable only by naming the architecture.

Current models
Deprecated models

English WER in the first table is the Open ASR Leaderboard average across eight datasets, measured on the floating-point reference. Quantized English scores are a little higher, especially at Tiny; see Accuracy for LibriSpeech-clean float-vs-quantized numbers. Non-English streaming scores are 400-clip macros on the panels named in Accuracy (WER, or no-space CER for Japanese and Mandarin). Korean and Ukrainian have no streaming model yet, so their figures are the older full-FLEURS scores from scripts/eval-model-accuracy.py and are not comparable to the streaming rows.

Current models

Language	Architecture	# Parameters	WER/CER	License
English	Medium Streaming	245 million	6.65%	MIT
English	Small Streaming	123 million	7.84%	MIT
English	Tiny Streaming	34 million	12.00%	MIT
Arabic	Tiny Streaming	34 million	15.5%	MIT
German	Small Streaming	123 million	7.5%	MIT
German	Tiny Streaming	34 million	12.0%	MIT
Japanese	Small Streaming	123 million	17.2%†	MIT
Japanese	Tiny Streaming	34 million	19.7%†	MIT
Mandarin	Tiny Streaming	34 million	16.1%†	MIT
Spanish	Small Streaming	123 million	4.9%	MIT
Spanish	Tiny Streaming	34 million	6.2%	MIT
Tagalog	Tiny Streaming	34 million	14.9%	MIT
Vietnamese	Tiny Streaming	34 million	9.4%	MIT
English	Base	58 million	10.07%	MIT
English	Tiny	26 million	12.66%	MIT
Korean	Tiny	26 million	6.46%	Community
Ukrainian	Base	58 million	14.55%	Community

† No-space character error rate, not word error rate.

The first architecture listed for each language is the default "ar", "de", "en" and so on will download. Korean and Ukrainian still default to their non-streaming Community models because no streaming checkpoint is published for them yet.

Deprecated models

These Community-licensed non-streaming models have a streaming replacement in the table above. They still load if you name the architecture, but new integrations should use the streaming model. Their FLEURS scores come from scripts/eval-model-accuracy.py (a character-weighted average on the full test set) and are not comparable to the streaming 400-clip figures.

Language	Architecture	# Parameters	WER/CER	License
Arabic	Base	58 million	5.63%	Community
Japanese	Base	58 million	13.62%	Community
Japanese	Tiny	26 million	—	Community
Mandarin	Base	58 million	25.76%	Community
Spanish	Base	58 million	4.33%	Community
Vietnamese	Base	58 million	8.82%	Community

Japanese Tiny has never been scored with scripts/eval-model-accuracy, so its cell is left empty rather than filled from another harness.

One common issue to watch out for if you're using models that don't use the Latin alphabet (so not English, Spanish, German, Vietnamese or Tagalog) is that you'll need to set the max_tokens_per_second option to 13.0 when you create the transcriber. This is because the most common pattern for hallucinations is endlessly repeating the last few words, and our heuristic to detect this is to check if there's an unusually high number of tokens for the duration of a segment. Unfortunately the base number of tokens per second for non-Latin languages is much higher than for English, thanks to how we're tokenizing, so you have to manually set the threshold higher to avoid cutting off valid outputs.