moonshine-base-gguf
handy-computer基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Ported from upstream commit 57b8436, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny and moonshine-streaming-small; deepest of the three (14 / 14 layers) and widest hidden dims (encoder 768 / decoder 640). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.
Ported from upstream commit 2c03650, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny; deeper encoder/decoder (10 / 10 layers) and wider hidden dims (encoder 620 / decoder 512). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Ported from upstream commit d7231bb, pinned 2026-07-12. Validated against the MOSS author repo (OpenMOSS/MOSS-Transcribe-Diarize) reference at transcribe.cpp commit 3f5e15c on 2026-07-12. Offline English/Chinese speech-to-text with speaker diarization. A 0.9B audio-LLM: a Whisper-Medium encoder (24 layers, d_model=1024) feeds a 4x temporal merge + VQAdaptor bridge into a Qwen3-0.6B decoder (28 layers) via audio-token injection. The model emits `[start][Sxx]text[end]`; the runtime parses those generated markers into clean text and segment rows. Speaker attribution is opt-in (`--diarize`) and returns structured speaker ids/turns. Not a streaming model. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
GGUF conversions of nvidia/multitalker-parakeet-streaming-0.6b-v1 for use with transcribe.cpp. Ported from upstream commit 8749fc7, pinned 2026-07-12. Validated against the NeMo reference at transcribe.cpp commit 3083021 on 2026-08-03. Offline and cache-aware streaming English speech-to-text with punctuation and capitalization. A cache-aware streaming FastConformer encoder with an RNN-T transducer decoder, fine-tuned from nvidia/nemotron-speech-streaming-en-0.6b. Plain GGUFs run the single_speaker_mode ASR path, while bundle GGUFs embed nvidia/diar_streaming_sortformer_4spk-v2.1 and, with `--diarize`, transcribe up to four overlapping speakers into a speaker-tagged transcript. The encoder preserves the upstream att_context_size=[70, 13] (1.12s) cache-aware attention mask; all four latency lookahead settings are selectable.
GGUF conversions of nvidia/nemotron-3.5-asr-streaming-0.6b for use with transcribe.cpp. Ported from upstream commit 24b151a, pinned 2026-06-08. Validated against the NeMo reference at transcribe.cpp commit 909e94e on 2026-06-08. Multilingual speech-to-text across 32 supported language-locales (the model's tokenizer recognizes 40, but 8 are adaptation-ready and need fine-tuning) with punctuation and capitalization. A cache-aware streaming FastConformer encoder with a prompt-conditioned RNN-T transducer decoder; the target language is selected per call (--language en-US, fr-FR, de-DE, ...) and an auto mode emits a tag. Ships both the offline path (att_context_size=[56, 13], 1.12s, headline accuracy) and runtime-selectable chunked streaming (--stream-chunk-ms 1120 --stream-att-right {0,3,6,13}).
GGUF conversions of nvidia/nemotron-speech-streaming-en-0.6b for use with transcribe.cpp. Ported from upstream commit ef3bf40, pinned 2026-05-11. Validated against the NeMo reference at transcribe.cpp commit 12f1076 on 2026-05-11. English speech-to-text with punctuation and capitalization. A cache-aware streaming FastConformer encoder with an RNN-T transducer decoder. Runs in both offline and cache-aware streaming modes. The encoder preserves the upstream att_context_size=[70, 13] (1.12s) cache-aware attention mask end-to-end.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
GGUF conversions of nvidia/parakeet-ctc-0.6b for use with transcribe.cpp. Ported from upstream commit ad09ba1, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10. Offline English speech-to-text with greedy CTC decoding. A FastConformer-Large encoder with a linear CTC head — the simplest and fastest decoder in the parakeet family. Output is lowercase, no punctuation. Not a streaming model and does not translate.
Ported from upstream commit a707e81, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10. Offline English speech-to-text with greedy CTC decoding. A FastConformer-XL encoder with a linear CTC head. Output is lowercase, no punctuation. Not a streaming model and does not translate. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
GGUF conversions of primeline/parakeet-primeline for use with transcribe.cpp. Ported from upstream commit 3f1a9bc, pinned 2026-08-16. Validated against the NeMo reference at transcribe.cpp commit 856d7c1 on 2026-08-16. primeLine's German fine-tune of NVIDIA's parakeet-tdt-0.6b-v3. A FastConformer encoder with a TDT/RNNT transducer decoder, taking 16 kHz mono WAV and producing a punctuated, cased transcript with optional token-level timestamps. Tuned for German, but the fine-tune did not collapse the base model's multilingual ability: it still transcribes the other 24 v3 languages with correct per-language casing and punctuation. Not a streaming model and does not translate.
Ported from upstream commit c0c1f09, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10. Offline English speech-to-text with greedy RNN-T decoding. A FastConformer-Large encoder with an RNN-T transducer decoder. Output is lowercase, no punctuation. Not a streaming model and does not translate. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
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.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.