GGUF conversions of ibm-granite/granite-speech-4.1-2b-nar for use with transcribe.cpp. Ported from upstream commit 99a4df9, pinned 2026-05-24. Validated against the Transformers reference at transcribe.cpp commit c53af2c on 2026-05-24. Offline multilingual speech-to-text in a single non-autoregressive editor pass. IBM Granite Speech 4.1-2b NAR shares the Conformer audio encoder with the AR Granite-Speech family but pairs it with a custom MLP-with- attention projector and the Granite-4.0-1b LLM used as a bidirectional editor (causal mask disabled). One forward pass produces logits over the full transcript; CTC decode yields the final text. No token-by-token loop. Takes a 16 kHz mono WAV and produces a transcript. English plus French, German, Spanish, and Portuguese; ASR only (no translation, no timestamps).
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Ported from upstream commit edd3bf5, pinned 2026-05-17. Validated against the Transformers reference at transcribe.cpp commit 275332d on 2026-05-17. Offline multilingual speech-to-text with word-level timestamps. IBM Granite Speech 4.1-2b-plus is the timestamp-and-diarization variant of the Granite-Speech family. Same architecture as the base 4.1-2b (Conformer encoder, BLIP-2 Q-Former projector, Granite-4.0-1b autoregressive LLM decoder) with two changes: the encoder concatenates mid-layer (idx 3) and final-layer hidden states (`cat_hidden_layers=[3]`, doubling the projector K/V input from 1024 to 2048), and the LM token embeddings are tied with the lm_head. Takes a 16 kHz mono WAV and produces a transcript, with `--timestamps word` returning structured per-word timestamps (parsed from the model's `[T:N]` centisecond markers), or `--diarize` returning structured speaker-attributed turns from its separate SAA prompt. Those two prompt tasks cannot be combined. Transcribes English, French, German, Spanish, and Portuguese (no Japanese on this variant). This variant is transcription-only: unlike the base granite-speech-4.1-2b, it does not perform speech translation. 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.
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Ported from upstream commit 0253049, pinned 2026-10-05. Validated against the SpeechBrain reference at transcribe.cpp commit aa4d0f47 on 2026-10-08. Spoken language identification over 107 languages: SpeechBrain's ECAPA-TDNN trained on VoxLingua107. NOT a transcription model: a run returns the model's language labels ranked by probability, optionally restricted to a caller-chosen set. Takes 16 kHz mono WAV; scores up to the first 30 s of a clip. Top-1 accuracy on FLEURS multilingual (3,000 utterances), scored on cpu. Measured at transcribe.cpp `cf62265a` on 2026-10-08.
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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.
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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.
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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.
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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.
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