Shenava 1.0: Open Streaming Persian ASR and Captioning
Collection
Public Shenava 1.0 release: Persian ASR models, Phase A/B datasets, AL corrections, eval artifacts, and demos. • 60 items • Updated
How to use Reza2kn/visualears-fastconformer-fa-full-ab-litert-fp16 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
LiteRT/TFLite FP16 fixed-length acoustic CTC-core export of
Reza2kn/visualears-fastconformer-fa-full-ab.
fastconformer_ctc_fixed2005_float16_fc.tfliteai_edge_quantizer float_casting, bits=16, dtype=FLOATFULLY_CONNECTED250000256,460,656 bytes, 58.65% of the FP32 LiteRT baseline| Check | Result |
|---|---|
| 16-item frame-level CTC argmax parity vs FP32 LiteRT | 100.00% |
| 16-item exact transcript parity vs FP32 LiteRT | 16 / 16 |
| VisualEars269 browser-feature exact transcript parity vs FP32 LiteRT | 269 / 269 |
| VisualEars269 browser-feature normalized transcript parity vs FP32 LiteRT | 269 / 269 |
The VisualEars269 check uses browser-style 80-bin log-mel features matching the real-time browser demo feature path, then compares greedy CTC-collapsed transcripts from this FP16 LiteRT model against the FP32 LiteRT baseline.
This is a fixed-frame feature-to-logits CTC core. It takes precomputed log-mel
features shaped [1, 80, 2005] as processed_signal; it is not a full
raw-audio-to-text pipeline by itself.
fastconformer_ctc_fixed2005_float16_fc.tflite: FP16 LiteRT model.recipe.json: ai_edge_quantizer recipe.validation/litert_float16_summary.json: size and 16-item frame-argmax parity.validation/litert_fp16_vs_fp_transcript_parity.json: 16-item transcript parity.validation/litert_fp16_vs_fp_visualears269_browser_features_transcript_parity.json: 269-item transcript parity.validation/visualears_benchmark_269_browser_features.json: feature-generation metadata for the 269 check.Base model
nvidia/stt_fa_fastconformer_hybrid_large