---
license: mit
language:
- ru
pipeline_tag: automatic-speech-recognition
library_name: transformers
tags:
- asr
- gigaam
- stt
- ru
- ctc
- ngram
- audio
- speech
---
[![Use In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/waveletdeboshir/07e39ae96f27331aa3e1e053c2c2f9e8/gigaam-ctc-hf-with-lm.ipynb)

# GigaAM-v2-CTC with ngram LM and beamsearch 🤗 Hugging Face transformers
This is an **unofficial Transformers wrapper** for the original GigaAM model released by SberDevices.

* original git https://github.com/salute-developers/GigaAM
* ngram LM from [`bond005/wav2vec2-large-ru-golos-with-lm`](https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm)

Russian ASR model GigaAM-v2-CTC with external ngram LM and beamsearch decoding.

## Model info
This is GigaAM-v2-CTC with `transformers` library interface, beamsearch decoding and hypothesis rescoring with external ngram LM.
In addition it can be use to extract word-level timestamps.

File [`gigaam_transformers.py`](https://huggingface.co/waveletdeboshir/gigaam-ctc-with-lm/blob/main/gigaam_transformers.py) contains model, feature extractor and tokenizer classes with usual transformers methods. Model can be initialized with transformers auto classes (see an example below).

## Installation

my lib versions:
* `torch` 2.7.1
* `torchaudio` 2.7.1
* `transformers` 4.49.0

You need to install `kenlm` and `pyctcdecode`:
```bash
pip install kenlm
pip install pyctcdecode
```

## Usage
Usage is same as other `transformers` ASR models.

```python
from transformers import AutoModel, AutoProcessor
import torch
import torchaudio

# load audio
wav, sr = torchaudio.load("audio.wav")
# resample if necessary
wav = torchaudio.functional.resample(wav, sr, 16000)

# load model and processor
processor = AutoProcessor.from_pretrained("waveletdeboshir/gigaam-ctc-with-lm", trust_remote_code=True)
model = AutoModel.from_pretrained("waveletdeboshir/gigaam-ctc-with-lm", trust_remote_code=True)
model.eval()

input_features = processor(wav[0], sampling_rate=16000, return_tensors="pt")

# predict
with torch.no_grad():
    logits = model(**input_features).logits

# decoding with beamseach and LM (tune alpha, beta, beam_width for your data)
transcription = processor.batch_decode(
    logits=logits.numpy(),
    beam_width=64,
    alpha=0.5,
    beta=0.5,
).text[0]

```

### Decoding with timestamps
We can use decoder to extract word-level timestamps. For this we need to know model stride and set parameter `output_word_offsets=True`.

In our case (Conformer) `MODEL_STRIDE = 40` ms per timestamp.

```python
MODEL_STRIDE = 40
outputs = processor.batch_decode(
    logits=logits.numpy(),
    beam_width=64,
    alpha=0.5,
    beta=0.5,
    output_word_offsets=True
)
word_ts = [
    {
        "word": d["word"],
        "start": round(d["start_offset"] * MODEL_STRIDE / 1000, 2),
        "end": round(d["end_offset"] * MODEL_STRIDE / 1000, 2),
    }
    for d in outputs.word_offsets[0]
]
```