README.md

metadata

license: mit
language:
  - ru
pipeline_tag: automatic-speech-recognition
library_name: transformers
tags:
  - asr
  - gigaam
  - stt
  - ru
  - ctc
  - audio
  - speech

GigaAM-v2-CTC 🤗 Hugging Face transformers

This is an unofficial Transformers wrapper for the original GigaAM-v2-CTC model released by SberDevices.

• original git https://github.com/salute-developers/GigaAM

Model info

This is GigaAM-v2-CTC with transformers library interface.

File 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
• accelerate 1.5.2

Usage

Usage is same as other transformers ASR models.

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", trust_remote_code=True)
model = AutoModel.from_pretrained("waveletdeboshir/gigaam-ctc", 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
# greedy decoding
greedy_ids = logits.argmax(dim=-1)
# decode token ids to text
transcription = processor.batch_decode(greedy_ids)[0]

Fine-tune

Fine-tuning Jupyter