---
language:
- kk
- ru
pipeline_tag: automatic-speech-recognition
tags:
- asr
- stt
- kazakh
- russian
- nemo
- fastconformer
- rnnt
- tdt
- speech-recognition
license: cc-by-nc-4.0
library_name: nemo
private: true
---

# KK/RU ASR TDT Accuracy

Accuracy-focused Kazakh and Russian ASR/STT model packaged as a NeMo `.nemo` checkpoint.

This model is intended for high-quality offline or server-side transcription where accuracy is prioritized over minimum latency.

## Files

- `kk_ru_asr_tdt_accuracy.nemo` - NeMo ASR checkpoint.
- `examples/offline_asr.py` - batch/file transcription example.
- `examples/serve_fastapi.py` - minimal HTTP service example.
- `metadata/eval.json` - evaluation summary.

## Model Characteristics

- Task: automatic speech recognition / speech-to-text.
- Languages: Kazakh (`kk`) and Russian (`ru`).
- Format: NVIDIA NeMo `.nemo`.
- Decoder: RNNT/TDT.
- Recommended decoding for best accuracy: RNNT beam search with beam size 4.
- Fast decoding option: RNNT greedy or beam size 2.
- Input audio: mono 16 kHz PCM WAV or another format supported by your NeMo audio stack.

This is not a TTS model, not a diarization model, and not a cache-aware streaming checkpoint.

## Evaluation

WER/CER on held-out local evaluation sets:

| Decode | Set | Overall WER | Overall CER | KK WER | RU WER | RTF |
|---|---:|---:|---:|---:|---:|---:|
| Greedy | clean | 3.321% | 0.680% | 2.351% | 5.719% | 0.0019 |
| Greedy | original/noisy | 10.549% | 6.481% | 13.221% | 5.719% | 0.0020 |
| Beam 2 | clean | 3.257% | 0.656% | 2.308% | 5.602% | 0.0047 |
| Beam 2 | original/noisy | 10.468% | 6.454% | 13.156% | 5.607% | 0.0048 |
| Beam 4 | clean | 3.197% | 0.644% | 2.229% | 5.588% | 0.0121 |
| Beam 4 | original/noisy | 10.430% | 6.443% | 13.107% | 5.588% | 0.0121 |

Recommended deployment default:

- Use `beam_size=4` when accuracy is the priority.
- Use `beam_size=2` when latency/cost matters.
- Use greedy only for maximum throughput.

## Install

Use an environment with NVIDIA NeMo ASR support and CUDA-enabled PyTorch.

```bash
pip install "nemo_toolkit[asr]" huggingface_hub soundfile fastapi uvicorn python-multipart
```

## Download

```python
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="nur-dev/kk-ru-asr-tdt-accuracy",
    filename="kk_ru_asr_tdt_accuracy.nemo",
    token=True,
)
print(model_path)
```

## Offline Inference

```bash
python examples/offline_asr.py \
  --model kk_ru_asr_tdt_accuracy.nemo \
  --audio /path/to/audio.wav \
  --strategy beam \
  --beam-size 4
```

Python:

```python
from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel
from omegaconf import OmegaConf

model = EncDecHybridRNNTCTCBPEModel.restore_from("kk_ru_asr_tdt_accuracy.nemo")
model.freeze()

decoding_cfg = OmegaConf.create(OmegaConf.to_container(model.cfg.decoding, resolve=True))
decoding_cfg.strategy = "beam"
decoding_cfg.beam.beam_size = 4
decoding_cfg.beam.return_best_hypothesis = True
model.change_decoding_strategy(decoding_cfg=decoding_cfg, decoder_type="rnnt")

text = model.transcribe(["/path/to/audio.wav"], batch_size=1, return_hypotheses=False)[0]
print(text)
```

## Serve With FastAPI

```bash
MODEL_PATH=kk_ru_asr_tdt_accuracy.nemo \
ASR_STRATEGY=beam \
ASR_BEAM_SIZE=4 \
uvicorn examples.serve_fastapi:app --host 0.0.0.0 --port 8000
```

Request:

```bash
curl -X POST "http://localhost:8000/transcribe" \
  -F "file=@/path/to/audio.wav"
```

Response:

```json
{"text": "..."}
```

## Best Practices

- Resample incoming audio to mono 16 kHz before inference.
- Use beam size 4 for final transcription.
- Use beam size 2 or greedy for interactive previews.
- Batch multiple files for throughput-oriented jobs.
- Do not use this checkpoint as a diarizer; combine it with a separate diarization model for speaker labels.
- For audio with many speakers or heavy overlap, use diarization and segment-level transcription before merging the final transcript.

<!-- measured-benchmark:start -->
## Evaluation (independently measured)

Held-out public test sets, measured directly — **not** self-reported (seed 42,
uniform multilingual-Whisper normalization). FLEURS test = 500 utterances/language;
ISSAI KSC2 test = 1000 utterances (in-domain Kazakh, spanning
crowd/parliament/podcasts/radio/talkshow).

| Test set | Lang | WER (%) | CER (%) |
|---|:--|--:|--:|
| FLEURS `kk_kz` | kk | 11.48 | 6.04 |
| FLEURS `ru_ru` | ru | 12.75 | 6.99 |
| FLEURS `en_us` | en | 100.47 | 90.47 |
| ISSAI KSC2 | kk | 9.75 | 3.50 |

Macro WER (kk/ru/en): **41.57%** (unweighted mean; penalises models that do not
cover all three languages).

> **Note.** The card reports author clean-set KK 2.35 / RU 5.72. On held-out data: FLEURS kk 11.48 and KSC2 9.75 — still the best Kazakh in this account and robust across broadcast domains. It has no English: English audio is transcribed as Cyrillic phonetics, so EN WER exceeds 100%.
<!-- measured-benchmark:end -->

<!-- license-note:start -->
## License & commercial use

**Non-commercial use only** (CC BY-NC 4.0). For commercial licensing or other
inquiries, please reach out to the author, **Nurgali Kadyrbek**, on LinkedIn:
https://www.linkedin.com/in/nurgali-kadyrbek-504260231/
<!-- license-note:end -->