Instructions to use Reza2kn/visualears-fastconformer-fa-full-ab-litert-w4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use Reza2kn/visualears-fastconformer-fa-full-ab-litert-w4 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
VisualEars FastConformer Persian ASR LiteRT W4
LiteRT/TFLite weight-only 4-bit export of Reza2kn/visualears-fastconformer-fa-full-ab.
Artifact
- Format: LiteRT/TFLite weight-only 4-bit fixed acoustic CTC-core export
- Quantization/conversion: AI Edge Quantizer weight-only 4-bit, selected
FULLY_CONNECTEDops, regexWrapper;[2-9], channelwise - Runtime validation: LiteRT/TFLite XNNPACK CPU
- Size: 357 MB, 81.7% of LiteRT FP source
Validation
This artifact passed the earlier frame-level argmax gate, but it does not pass transcript parity.
| Check | Result |
|---|---|
| Frame-level CTC argmax parity vs PyTorch | 98.23% |
| Greedy CTC transcript parity vs PyTorch on 16 calibration items | 37.5% / 6 of 16 |
| Greedy CTC transcript parity vs LiteRT FP on 16 calibration items | 37.5% / 6 of 16 |
The earlier 98.23% number is frame-level CTC argmax token agreement, not "98% same final transcripts." For transcript-parity-sensitive use, this W4 artifact should be treated as failed.
Usage Boundary
This export takes precomputed log-mel features as processed_signal; it is not a full raw-audio-to-text pipeline by itself.
Notes
Best compressed LiteRT 4-bit attempt so far, but not a transcript-parity artifact. All-FC W4 compressed to 28.0% but failed at 96.51% frame argmax parity; safe FC+conv failed at 97.51% frame argmax parity.
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Model tree for Reza2kn/visualears-fastconformer-fa-full-ab-litert-w4
Base model
nvidia/stt_fa_fastconformer_hybrid_large