Griot Nano 1 Onnx
This is the unquantized FP32 ONNX Runtime distribution of Qlerqly/griot-nano-1. It contains the same 153M-parameter Conformer CTC acoustic model and is intended for local CPU transcription without PyTorch.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 604 MB
- Versão
- vRevision 714d389
- Atualizado
- 27 de set. de 2026
- Downloads
- 4
- Fonte
- Hugging Face
Descrição da fonte
Griot Nano 1 — ONNX CPU Edition
This is the unquantized FP32 ONNX Runtime distribution of Qlerqly/griot-nano-1. It contains the same 153M-parameter Conformer CTC acoustic model and is intended for local CPU transcription without PyTorch.
Run locally
uvx --from huggingface-hub hf download Qlerqly/griot-nano-1-onnx \
--local-dir griot-nano-1-onnx
cd griot-nano-1-onnx
uv run --with-requirements requirements.txt python inference.py \
--model-dir . --audio recording.wav
# Live microphone transcription
uv run --with-requirements requirements.txt python inference.py \
--model-dir . --live
Use --threads N to control ONNX Runtime CPU threads.
Recommended: KenLM decoding
For best contextual accuracy, we encourage using this runtime together with Qlerqly/griot-nano-1-kenlm. It adds n-gram beam-search guidance without another neural network, keeping the overhead low relative to neural language-model rescoring.
# From the directory containing griot-nano-1-onnx
uvx --from huggingface-hub hf download Qlerqly/griot-nano-1-kenlm \
--local-dir griot-nano-1-kenlm
cd griot-nano-1-onnx
uv run --with-requirements requirements-kenlm.txt python inference.py \
--model-dir . --audio recording.wav \
--kenlm ../griot-nano-1-kenlm/multilingual.arpa --beam-width 25
This runs ONNX Runtime and KenLM as one CPU transcription pipeline. The
suggested starting values are beam width 25, LM weight 0.5, and word
insertion bonus 1.0; override the latter two with --lm-alpha and --lm-beta.
Test results
These are the canonical model's raw greedy-CTC results. The exporter performs a
numerical PyTorch-versus-ONNX check recorded in onnx_verification.json.
| Language | Samples | CER | WER |
|---|---|---|---|
| ak | 21,435 | 19.27% | 41.67% |
| dag | 5,307 | 7.04% | 23.75% |
| ee | 6,820 | 9.66% | 30.76% |
| en | 56,364 | 12.91% | 26.05% |
| ga | 9,621 | 8.14% | 15.83% |
| Combined | 99,547 | 13.66% | 29.19% |
Format
- ONNX opset 18
- FP32 weights
- Dynamic batch and time dimensions
- ONNX Runtime
CPUExecutionProvider - No quantization
- NumPy/SciPy audio preprocessing
Training data and license
This runtime uses the same weights, training sources, and license as the canonical model. See ATTRIBUTIONS.md for the complete source and license record.
Fontes
1 fonte
Fontes
1 fonteVerificado 30 de ago.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- language: - ak - dag - ee - en - gaa license: cc-by-nc-sa-4.0 library_name: onnx pipeline_tag: automatic-speech-recognition tags: - onnx - onnxruntime - conformer - ctc - cpu --- # Griot Nano 1 — ONNX CPU Edition This is the unquantized FP32 ONNX Runtime distribution of [Qlerqly/griot-nano-1](https://huggingface.co/Qlerqly/griot-nano-1). It contains the same 153M-parameter Conformer CTC acoustic model and is intended for local CPU transcription without PyTorch. ## Run locally ```bash uvx --from huggingface-hub hf download Qlerqly/griot-nano-1-onnx \ --local-dir griot-nano-1-onnx cd griot-nano-1-onnx uv run --with-requirements requirements.txt python inference.py \ --model-dir . --audio recording.wav # Live microphone transcription uv run --with-requirements requirements.txt python inference.py \ --model-dir . --live ``` Use `--threads N` to control ONNX Runtime CPU threads. ## Recommended: KenLM decoding For best contextual accuracy, we encourage using this runtime together with [Qlerqly/griot-nano-1-kenlm](https://huggingface.co/Qlerqly/griot-nano-1-kenlm). It adds n-gram beam-search guidance without another neural network, keeping the overhead low relative to neural language-model rescoring. ```bash # From the directory containing griot-nano-1-onnx uvx --from huggingface-hub hf download Qlerqly/griot-nano-1-kenlm \ --local-dir griot-nano-1-kenlm cd griot-nano-1-onnx uv run --with-requirements requirements-kenlm.txt python inference.py \ --model-dir . --audio recording.wav \ --kenlm ../griot-nano-1-kenlm/multilingual.arpa --beam-width 25 ``` This runs ONNX Runtime and KenLM as one CPU transcription pipeline. The suggested starting values are beam width `25`, LM weight `0.5`, and word insertion bonus `1.0`; override the latter two with `--lm-alpha` and `--lm-beta`. ## Test results These are the canonical model's raw greedy-CTC results. The exporter performs a numerical PyTorch-versus-ONNX check recorded in `onnx_verification.json`. | Language | Samples | CER | WER | |---|---:|---:|---:| | ak | 21,435 | 19.27% | 41.67% | | dag | 5,307 | 7.04% | 23.75% | | ee | 6,820 | 9.66% | 30.76% | | en | 56,364 | 12.91% | 26.05% | | ga | 9,621 | 8.14% | 15.83% | | **Combined** | **99,547** | **13.66%** | **29.19%** | ## Format - ONNX opset 18 - FP32 weights - Dynamic batch and time dimensions - ONNX Runtime `CPUExecutionProvider` - No quantiz...
Source context: 4 downloads · 0 likes · Pipeline automatic-speech-recognition · Library onnx · Repo Qlerqly/griot-nano-1-onnx
