---
license: mit
language:
- ru
- en
base_model:
- ai-sage/GigaAM-v3
pipeline_tag: automatic-speech-recognition
tags:
- loom
- automatic-speech-recognition
library_name: loom-py-rt
---
# GigaAM-v3 RNNT

Sber's GigaAM-v3 Conformer RNNT Russian/English ASR model, exported for loom.cpp.

This is a [loom.cpp](https://github.com/loom-ai-org/loom.cpp) export: a single self-describing GGUF
that carries its own graph topologies, tokenizer (if any) and driver script, produced by
[loom-exporter](https://github.com/loom-ai-org/loom-exporter).

## Original model

Exported from [`ai-sage/GigaAM-v3`](https://huggingface.co/ai-sage/GigaAM-v3). Weights are unmodified; this repo packages the same parameters into
loom.cpp's GGUF format.

## License

`mit`, inherited from the base model above.

## Language(s)

`ru`, `en`

## Usage

Run it with [loom-py](https://github.com/loom-ai-org/loom-py) -- `loom-py-rt` on PyPI:

```sh
pip install -U "loom-py-rt[hub]"
```

```python
import loom

model = loom.Model.from_pretrained("loom-ai-org/gigaam-v3-rnnt-loom")

# Audio is a mono float list at 16 kHz. This model decodes in the one language it was trained for and
# takes no `language=` argument -- passing one warns and is ignored, because nothing in its decode
# could act on it.
result = model.speech2text.infer(audio, timestamps=True)
print(result.text)

# It emits no timestamp tokens, so `segments` is one span covering the whole clip and
# `result.timestamped` is False. Check that before treating a start/end as a boundary the model chose.
for segment in result.segments:
    print(segment.start, segment.end, segment.text)
```

### The layer underneath

The call above is the high-level door: one per task, named for the modality pair it maps between, with
the windowing, sampling and assembly this model needs already applied. Under it, `model.infer(...)`
passes your arguments straight to the driver this GGUF embeds -- which is where you go for a knob the
door does not name.

`model.driver_source` prints that driver, including a header comment documenting every argument it
accepts for this model, and is the authority on it. See [loom-py](https://github.com/loom-ai-org/loom-py) for the API and
[loom.cpp](https://github.com/loom-ai-org/loom.cpp) for what the engine does between the two.

## Files

- `gigaam-v3-rnnt.gguf` -- the model, exported with loom-exporter.