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XLS-R-300M - Maltese

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MT dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1895
  • Wer: 0.1984

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7.5e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 60.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.4219 3.6 400 3.3127 1.0
3.0399 7.21 800 3.0330 1.0
1.5756 10.81 1200 0.6108 0.5724
1.0995 14.41 1600 0.3091 0.3154
0.9639 18.02 2000 0.2596 0.2841
0.9032 21.62 2400 0.2270 0.2514
0.8145 25.23 2800 0.2172 0.2483
0.7845 28.83 3200 0.2084 0.2333
0.7694 32.43 3600 0.1974 0.2234
0.7333 36.04 4000 0.2020 0.2185
0.693 39.64 4400 0.1947 0.2148
0.6802 43.24 4800 0.1960 0.2102
0.667 46.85 5200 0.1904 0.2072
0.6486 50.45 5600 0.1881 0.2009
0.6339 54.05 6000 0.1877 0.1989
0.6254 57.66 6400 0.1893 0.2003

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with split test
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config mt --split test

Inference With LM

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
    logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "għadu jilagħbu ċirku tant bilfondi"

Eval results on Common Voice 8 "test" (WER):

Without LM With LM (run ./eval.py)
19.853 15.967
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Dataset used to train anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm

Evaluation results