andrewbawitlung/xlsr-1b-mizonal3-E5-lus-v2026.06

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Disclaimer / Notice: Details for these are in Peer Review and publications of the paper will be made available soon for more details.

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xlsr-1b-mizonal3-E5-lus-v2026.06

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MiZonal v3.0 dataset. Note: ~1 hour of conversational speech was added to this dataset version.

It achieves the following results on the evaluation set:

• —Wer: 26.3669
• —Cer: 5.7509
• —Real Time Factor: 0.0038

Quick Inference

import torch
import librosa
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = Wav2Vec2Processor.from_pretrained("andrewbawitlung/xlsr-1b-mizonal3-E5-lus-v2026.06")
model = Wav2Vec2ForCTC.from_pretrained("andrewbawitlung/xlsr-1b-mizonal3-E5-lus-v2026.06").to(device)

audio, sr = librosa.load("your_audio.wav", sr=16000)
input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_values.to(device)

with torch.no_grad():
    logits = model(input_values).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)

Model description

Experiment Configurations

This repository is part of a series of experiments. The different configurations are:

• —E1 (Baseline): Standard training configuration.
• —E2 (Noise): Training with background noise augmentation.
• —E3 (Speed): Training with speed perturbation augmentation.
• —E4 (SpecAug): Training with SpecAugment (time and frequency masking).
• —E5 (Combined): Training with a combination of all augmentations.

All Models in this Family

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0003
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: OptimizerNames.ADAMWTORCHFUSED
• —lrschedulertype: SchedulerType.LINEAR
• —num_epochs: 8

Training results