Instructions to use Reza2kn/visualears-fastconformer-fa-full-ab-qat-w2-materialized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use Reza2kn/visualears-fastconformer-fa-full-ab-qat-w2-materialized with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("Reza2kn/visualears-fastconformer-fa-full-ab-qat-w2-materialized") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
File size: 1,311 Bytes
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license: apache-2.0
base_model: Reza2kn/visualears-fastconformer-fa-full-ab
language:
- fa
tags:
- automatic-speech-recognition
- nemo
- fastconformer
- qat
- persian
---
# VisualEars FastConformer FA Full AB - QAT W2 Materialized
This repo contains the current 2-bit QAT materialized NeMo checkpoint from the VisualEars FastConformer Persian ASR quantization experiments.
Important: this is a **materialized float NeMo checkpoint**, not a packed 2-bit deployment artifact yet. The selected QAT linear weights are projected to their 2-bit values and saved back into normal NeMo/torch tensors, so the file size is still close to the full precision `.nemo`.
## Source
Base model: `Reza2kn/visualears-fastconformer-fa-full-ab`
Training run: `onebit_cotraining_fast_no_urls_lower3_20260612_161154`
Quantized modules: feed-forward linear layers in encoder layers 0-2, decoder/CTC/attention/convolution left unquantized.
## VisualEars269 score
Default decoding:
| model | WER | CER |
|---|---:|---:|
| FP baseline | 32.64% | 13.30% |
| W2 materialized | 37.47% | 16.69% |
Forced CTC:
| model | WER | CER |
|---|---:|---:|
| FP baseline | 34.96% | 14.36% |
| W2 materialized | 40.65% | 17.78% |
This is useful as an experimental checkpoint, but it is not yet parity and not yet compressed/bit-packed.
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