Instructions to use Reza2kn/canary-180m-persian-semiclean31-staged-smart-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use Reza2kn/canary-180m-persian-semiclean31-staged-smart-init with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("Reza2kn/canary-180m-persian-semiclean31-staged-smart-init") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Persian-heavy Canary 180M staged smart-init ASR
Experimental ASR-only adaptation of nvidia/canary-180m-flash for Persian-first bilingual ASR.
This checkpoint uses the newer staged adaptation path:
- Fresh Persian-heavy bilingual SentencePiece tokenizer.
- Smart vocabulary/embedding initialization from the original Canary tokenizer where possible.
- Stage 1: decoder/head adaptation with the encoder frozen.
- Stage 2: full-parameter continuation with a short initial encoder freeze.
Data mix:
- Persian:
Reza2kn/persian-asr-semi-clean-31h-awq-werselected/cleaned audio+text only. - English: small FLEURS retention slice.
- Train split: 46,006 rows, about 31.742 hours.
- Validation split: 938 rows, about 0.652 hours.
Validation on the internal portable held-out split:
- Rows: 938
- WER: 0.341208 (34.12%)
- CER: 0.195946 (19.59%)
Artifact:
canary_180m_persian_semiclean31_staged_smart_gpu1_bd700.nemo- SHA256:
77fe2c46c30a507440b7129bb2efbb8e9b0e18622346509c7c46e99af16adb49
This is still a research checkpoint, not yet an Android/CoreML/ONNX export. The earlier non-smart-init semiclean31 checkpoint was much worse, around 102% WER; this staged smart-init checkpoint is the first run where the adaptation is clearly learning.
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Model tree for Reza2kn/canary-180m-persian-semiclean31-staged-smart-init
Base model
nvidia/canary-180m-flash