Yemberzal — Kashmiri SraVaani ASR (Epoch 28)
Yemberzal is a Kashmiri automatic speech-recognition checkpoint, full fine-tuned from SraVaani. This model is the epoch-28 checkpoint used as the primary Kashmiri ASR model in the associated data-collection service.
Model lineage and details
- Base model: ARTPARK-IISc/SraVaani-1.0
- Adaptation: full fine-tune for Kashmiri ASR
- File:
best_epoch28.nemo - Framework: NVIDIA NeMo
EncDecHybridRNNTCTCBPEModel - Primary decoding path: TDT / RNN-T
- Recommended runtime: Python 3.11 and
nemo_toolkit[asr]==2.7.3
Training and validation
This is the epoch-28 checkpoint from a full fine-tune on 45.0 hours of Kashmiri IndicVoices-R data. The experiment recorded a validation WER of 37.83% at epoch 28. The available report does not state the validation split's number of utterances or duration, so those values are intentionally not inferred here.
Held-out test set
Evaluation uses the official IndicVoices-R Kashmiri test split, after the configured 0.5–30 second clip-duration filter and transcript deduplication:
| Test-set property | Value |
|---|---|
| Utterances / clips | 403 |
| Duration | 0.9043 hours (about 54.3 minutes) |
| Speakers | 16 |
| Reference words | 9,357 |
| Reference characters | 49,326 |
Test results
Metrics are corpus-level micro averages. Use the TDT / RNN-T decoder for this checkpoint.
| Decoder | WER | 95% bootstrap WER CI | CER | Exact utterances |
|---|---|---|---|---|
| TDT / RNN-T (primary) | 38.13% | 36.57–39.69% | 12.74% | 10 / 403 |
| CTC (diagnostic) | 42.60% | 40.93–44.25% | 14.95% | 8 / 403 |
Loading locally
from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel
model = EncDecHybridRNNTCTCBPEModel.restore_from("best_epoch28.nemo")
model.change_decoding_strategy(decoder_type="rnnt")
model.eval()
text = model.transcribe(["sample.wav"], batch_size=1, verbose=False)[0]
Use and limitations
This research/deployment checkpoint is for the associated data-collection workflow. The held-out test set is relatively small and does not substitute for native-speaker acceptance review. Validate transcripts—particularly proper nouns, code-switching, short utterances, and noisy speech—with a fluent Kashmiri reviewer before treating them as ground truth. Before redistribution or commercial use, review the base model and training-data terms that apply to your use case.