| --- |
| license: other |
| license_name: nyra-health-non-commercial-research |
| license_link: LICENSE.md |
| base_model: nyralabs/CrisperWhisper2.0_large |
| pipeline_tag: automatic-speech-recognition |
| tags: |
| - audio.cpp |
| - gguf |
| - whisper |
| - speech-recognition |
| - word-timestamps |
| --- |
| |
| # CrisperWhisper 2.0 Large GGUF |
|
|
| Use these self-contained models with [audio.cpp](https://github.com/0xShug0/audio.cpp). |
|
|
| Converted from [nyralabs/CrisperWhisper2.0_large](https://huggingface.co/nyralabs/CrisperWhisper2.0_large) |
| at revision `f4334f6e8193f2691212d49b20fa12d370e13896`. |
| The tokenizer, configuration, and audio.cpp model spec are embedded. |
|
|
| > **License:** the upstream non-commercial research license covers the model |
| > weights and generated outputs. See [LICENSE.md](LICENSE.md). |
|
|
| | File | Storage | Size | |
| |---|---|---:| |
| | `crisperwhisper-2-large-bf16.gguf` | Original BF16 weights | 2.88 GiB | |
| | `crisperwhisper-2-large-q8_0.gguf` | Q8_0 quantized weights | 1.60 GiB | |
| | `crisperwhisper-2-large-q4_k.gguf` | Q4_K quantized weights | 0.91 GiB | |
| |
| ## CUDA validation |
| |
| These measurements use an RTX 5090, eight CPU threads, and the default |
| transcription settings. C++ uses a CUDA Debug build; Python uses the official |
| Transformers backend. The sample contains five FLEURS clips from each supported |
| dataset language tested here (`en`, `kk`, `ru`, and `uz`), for 20 clips and |
| 242.4 seconds of audio. One request warmed each session before measurement. |
| RTF sums inference time for the 20 measured requests and excludes model loading |
| and the warm-up request. Peak VRAM includes the loaded model and inference |
| graphs. |
| |
| | Runtime / storage | Peak VRAM | RTF | Sample WER | Exact transcripts vs Python | |
| |---|---:|---:|---:|---:| |
| | Official Python, Transformers | 4,140 MiB | 0.0586 | 34.50% | reference | |
| | BF16 | 4,130 MiB | 0.0283 | 34.50% | 19/20 | |
| | Q8_0 | 2,822 MiB | 0.0236 | 34.77% | 18/20 | |
| | Q4_K | 2,122 MiB | 0.0260 | 34.77% | 11/20 | |
| |
| The one Python-versus-C++ BF16 mismatch was `uz_000`. Python emitted |
| `fukaralari ... muljallangin`, while C++ emitted |
| `fukaraları ... mulgallangin`. The FLEURS reference is |
| `fuqarolari ... moʻljallangan`, so both outputs count as the same two word |
| substitutions and have identical WER. The `19/20` result measures exact |
| agreement between implementations, not agreement with the reference text. |
|
|
| Q8_0 is the balanced quantized option in this sample: it reduced peak VRAM by |
| 1,308 MiB and ran 16.6% faster than BF16, with one additional word error. |
| Q4_K reduced peak VRAM by another 700 MiB, but introduced noticeably more |
| transcript wording drift and was slower than Q8_0. Its aggregate WER happened |
| to match Q8_0 on this small sample; that does not make its transcripts equally |
| stable. C++ BF16 was 2.1x faster than the official Python Transformers path; |
| Q8_0 was 2.5x faster and used 1,318 MiB less peak VRAM. These 20 clips are a |
| quantization and implementation check, not a general accuracy benchmark. |
| |
| ## Usage |
| |
| ```bash |
| audiocpp_cli --task asr --family crisperwhisper \ |
| --model crisperwhisper-2-large-q8_0.gguf --backend cuda \ |
| --audio speech.wav --request-option language=en \ |
| --text-out transcript.txt --words-out words.json --log |
| ``` |
| |
| - `transcription_mode=verbatim` preserves disfluencies and vocal events. |
| - `transcription_mode=intended` produces cleaned text. |
| - `transcription_mode=verbatimize` uses a clean transcript supplied with `--text` |
| to restore disfluencies from audio up to 30 seconds long. |
| - `--task align --text "Known transcript"` aligns supplied words in offline mode. |
| - Long-form ASR uses overlapping continuation windows. `--mode streaming` emits |
| committed text per window after receiving the complete recording, not live PCM. |
|
|
| See the [model usage guide](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/crisperwhisper.md) |
| for request and session options. |
|
|
