Automatic Speech Recognition
LiteRT
LiteRT
Persian
ctc
persian
farsi
fastconformer
visualears
reference-export
Instructions to use Reza2kn/visualears-fastconformer-fa-full-ab-litert-fp 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-fp 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 FP
Reference LiteRT/TFLite fixed-length acoustic CTC-core export of Reza2kn/visualears-fastconformer-fa-full-ab.
Artifact
- Format: LiteRT/TFLite FP fixed-length acoustic CTC-core export
- Quantization/conversion: FP LiteRT/TFLite conversion reference; no 4-bit quantization
- Runtime validation: LiteRT/TFLite XNNPACK CPU
- Size: 437 MB
.tflite
Validation
Two different parity checks are available:
| Check | Result |
|---|---|
| Frame-level CTC argmax parity vs PyTorch | 100.00% |
| Greedy CTC transcript parity vs PyTorch on the same 16 calibration items | 100.00% / 16 of 16 |
Transcript parity is computed after greedy CTC collapse with the model tokenizer. This is still a fixed-core feature-to-logits check, not a full raw-audio pipeline evaluation.
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
The LiteRT export required folding fixed positional slice constants and removing all-valid pre-encoder mask multiplies.
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Model tree for Reza2kn/visualears-fastconformer-fa-full-ab-litert-fp
Base model
nvidia/stt_fa_fastconformer_hybrid_large