---
license: cc-by-nc-4.0
language:
- kk
- ru
- en
- uz
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- whisper
- speech-recognition
- stt
- kazakh
- russian
- english
- uzbek
- multilingual
base_model: openai/whisper-large-v3
---

# Tynda STT 4L

**Tynda** (Тыңда — "Listen" in Kazakh) is a multilingual speech-to-text model supporting 4 languages of Central Asia and beyond.

## Supported Languages

| Language | Code |
|----------|------|
| Kazakh | `kk` |
| Russian | `ru` |
| English | `en` |
| Uzbek | `uz` |

## Model Details

- **Architecture**: Whisper Large V3 (1.55B parameters)
- **Task**: Automatic Speech Recognition / Speech-to-Text
- **Audio Input**: 16kHz mono WAV
- **Max Duration**: 30 seconds per segment

## Usage

```python
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_id = "nur-dev/tynda-stt-4L"
device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = WhisperForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.float16
).to(device)

# Choose language: "kazakh", "russian", "english", or "uzbek"
processor = WhisperProcessor.from_pretrained(
    "openai/whisper-large-v3", language="kazakh", task="transcribe"
)

# Load your audio (16kHz mono)
import soundfile as sf
audio, sr = sf.read("audio.wav", dtype="float32")

inputs = processor.feature_extractor(audio, sampling_rate=16000, return_tensors="pt")
features = inputs.input_features.to(device, dtype=torch.float16)

forced_ids = processor.get_decoder_prompt_ids(language="kazakh", task="transcribe")

with torch.no_grad():
    predicted_ids = model.generate(
        features,
        forced_decoder_ids=forced_ids,
        max_new_tokens=200,
    )

text = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(text)
```

### Using with `pipeline`

```python
from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="nur-dev/tynda-stt-4L",
    torch_dtype="float16",
    device="cuda:0",
)

result = pipe(
    "audio.wav",
    generate_kwargs={"language": "kazakh", "task": "transcribe"},
)
print(result["text"])
```

## License

This model is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). It is free for non-commercial use. For commercial licensing, please contact the authors.

<!-- 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 | 24.80 | 10.81 |
| FLEURS `ru_ru` | ru | 11.49 | 6.46 |
| FLEURS `en_us` | en | 6.33 | 3.41 |
| ISSAI KSC2 | kk | 30.60 | 12.54 |

Macro WER (kk/ru/en): **14.21%** (unweighted mean; penalises models that do not
cover all three languages).

> **Note.** The card reports no numbers. Best Russian and English in this account; Kazakh is the weak spot (FLEURS 24.8, and KSC2 30.6 on in-domain broadcast speech).
<!-- 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 -->