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.

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", 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

Finetune In Colab

Fine-tuning Jupyter