crispasr

Python bindings for CrispASR — lightweight on-device speech recognition via ggml.

Supports the ASR backends compiled into the linked CrispASR library, including Whisper, Qwen3-ASR, FastConformer, Canary, Parakeet, Cohere, Granite-Speech, Voxtral, wav2vec2, GLM-ASR, Kyutai-STT, Moonshine, FireRed, OmniASR, and VibeVoice-ASR.

Install

pip install crispasr

Platform wheels bundle the native libcrispasr — nothing else to install — for Linux (x86_64, arm64), macOS (Apple Silicon, Metal-accelerated), and Windows (x86_64).

GPU wheels

CUDA and Vulkan builds are published to a separate index (llama-cpp-python style — pass --extra-index-url):

# NVIDIA CUDA (Linux + Windows)
pip install crispasr --extra-index-url https://crispstrobe.github.io/CrispASR/whl/cuda/
# Vulkan (Windows)
pip install crispasr --extra-index-url https://crispstrobe.github.io/CrispASR/whl/vulkan/

Other platforms / bring-your-own library

Where no prebuilt wheel matches, pip installs the pure-Python sdist, which loads a libcrispasr you supply. Build/install it from source and, if it lands in a non-standard location, point CRISPASR_LIB_PATH at the file:

git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR && cmake -B build && cmake --build build -j && sudo cmake --install build
export CRISPASR_LIB_PATH=/usr/local/lib/libcrispasr.so   # only if non-standard

Quick start

from crispasr import CrispASR

model = CrispASR("ggml-base.en.bin")
for seg in model.transcribe("audio.wav"):
    print(f"[{seg.start:.1f}s - {seg.end:.1f}s] {seg.text}")
model.close()

Or use the unified Session API for non-Whisper backends (Qwen3-ASR, FastConformer, Parakeet, …):

from crispasr import Session

s = Session("qwen3-asr-0.6b-q4_k.gguf")
for seg in s.transcribe_pcm(pcm_f32, sample_rate=16000):
    print(seg.text)

API

• CrispASR — Whisper-compatible high-level API
• Session — unified API across all backends compiled into libcrispasr
• align_words(...) — word-level CTC alignment
• diarize_segments(...) — speaker diarization (energy / xcorr / vad-turns / pyannote)
• SpeakerEmbedder(spec) — pluggable embedder ("auto"/"titanet", "indextts"/"ecapa", or a .gguf path)
• PyannoteCache(pcm, model) — pre-computed pyannote-seg posteriors for cross-slice consistency
• agglomerative_cluster(embeddings, ...) — single-linkage cosine clustering for globally stable speaker IDs
• TitaNet / SpeakerDB — standalone speaker verification + closed-roster profile matching (consent-gated; requires expected_names + consent=True, see docs/diarization-speakers.md)
• detect_language_pcm(...) — language ID
• registry_lookup(...) — auto-download known models from the model hub
• registry_default_bundle(...) — enumerate the exact primary, companion, and extra files used by -m auto, including licence-acceptance policy

See the main repo for full documentation, model registry, and CLI.

License

MIT — see LICENSE.