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You can test this model online with the Space for Romanian Speech Recognition

The model ranked TOP-1 on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge :

Romanian Wav2Vec2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8.0 - Romanian subset dataset, with extra training data from Romanian Speech Synthesis dataset.

Without the 5-gram Language Model optimization, it achieves the following results on the evaluation set (Common Voice 8.0, Romanian subset, test split):

  • Loss: 0.1553
  • Wer: 0.1174
  • Cer: 0.0294

Model description

The architecture is based on facebook/wav2vec2-xls-r-300m with a speech recognition CTC head and an added 5-gram language model (using pyctcdecode and kenlm) trained on the Romanian Corpora Parliament dataset. Those libraries are needed in order for the language model-boosted decoder to work.

Intended uses & limitations

The model is made for speech recognition in Romanian from audio clips sampled at 16kHz. The predicted text is lowercased and does not contain any punctuation.

How to use

Make sure you have installed the correct dependencies for the language model-boosted version to work. You can just run this command to install the kenlm and pyctcdecode libraries :

pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode

With the framework transformers you can load the model with the following code :

from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained("gigant/romanian-wav2vec2")

model = AutoModelForCTC.from_pretrained("gigant/romanian-wav2vec2")

Or, if you want to test the model, you can load the automatic speech recognition pipeline from transformers with :

from transformers import pipeline

asr = pipeline("automatic-speech-recognition", model="gigant/romanian-wav2vec2")

Example use with the datasets library

First, you need to load your data

We will use the Romanian Speech Synthesis dataset in this example.

from datasets import load_dataset

dataset = load_dataset("gigant/romanian_speech_synthesis_0_8_1")

You can listen to the samples with the IPython.display library :

from IPython.display import Audio

i = 0
sample = dataset["train"][i]
Audio(sample["audio"]["array"], rate = sample["audio"]["sampling_rate"])

The model is trained to work with audio sampled at 16kHz, so if the sampling rate of the audio in the dataset is different, we will have to resample it.

In the example, the audio is sampled at 48kHz. We can see this by checking dataset["train"][0]["audio"]["sampling_rate"]

The following code resample the audio using the torchaudio library :

import torchaudio
import torch

i = 0
audio = sample["audio"]["array"]
rate = sample["audio"]["sampling_rate"]
resampler = torchaudio.transforms.Resample(rate, 16_000)
audio_16 = resampler(torch.Tensor(audio)).numpy()

To listen to the resampled sample :

Audio(audio_16, rate=16000)

Know you can get the model prediction by running

predicted_text = asr(audio_16)
ground_truth = dataset["train"][i]["sentence"]

print(f"Predicted text : {predicted_text}")
print(f"Ground truth : {ground_truth}")

Training and evaluation data

Training data :

Evaluation data :

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 48
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
2.9272 0.78 500 0.7603 0.7734 0.2355
0.6157 1.55 1000 0.4003 0.4866 0.1247
0.4452 2.33 1500 0.2960 0.3689 0.0910
0.3631 3.11 2000 0.2580 0.3205 0.0796
0.3153 3.88 2500 0.2465 0.2977 0.0747
0.2795 4.66 3000 0.2274 0.2789 0.0694
0.2615 5.43 3500 0.2277 0.2685 0.0675
0.2389 6.21 4000 0.2135 0.2518 0.0627
0.2229 6.99 4500 0.2054 0.2449 0.0614
0.2067 7.76 5000 0.2096 0.2378 0.0597
0.1977 8.54 5500 0.2042 0.2387 0.0600
0.1896 9.32 6000 0.2110 0.2383 0.0595
0.1801 10.09 6500 0.1909 0.2165 0.0548
0.174 10.87 7000 0.1883 0.2206 0.0559
0.1685 11.65 7500 0.1848 0.2097 0.0528
0.1591 12.42 8000 0.1851 0.2039 0.0514
0.1537 13.2 8500 0.1881 0.2065 0.0518
0.1504 13.97 9000 0.1840 0.1972 0.0499
0.145 14.75 9500 0.1845 0.2029 0.0517
0.1417 15.53 10000 0.1884 0.2003 0.0507
0.1364 16.3 10500 0.2010 0.2037 0.0517
0.1331 17.08 11000 0.1838 0.1923 0.0483
0.129 17.86 11500 0.1818 0.1922 0.0489
0.1198 18.63 12000 0.1760 0.1861 0.0465
0.1203 19.41 12500 0.1686 0.1839 0.0465
0.1225 20.19 13000 0.1828 0.1920 0.0479
0.1145 20.96 13500 0.1673 0.1784 0.0446
0.1053 21.74 14000 0.1802 0.1810 0.0456
0.1071 22.51 14500 0.1769 0.1775 0.0444
0.1053 23.29 15000 0.1920 0.1783 0.0457
0.1024 24.07 15500 0.1904 0.1775 0.0446
0.0987 24.84 16000 0.1793 0.1762 0.0446
0.0949 25.62 16500 0.1801 0.1766 0.0443
0.0942 26.4 17000 0.1731 0.1659 0.0423
0.0906 27.17 17500 0.1776 0.1698 0.0424
0.0861 27.95 18000 0.1716 0.1600 0.0406
0.0851 28.73 18500 0.1662 0.1630 0.0410
0.0844 29.5 19000 0.1671 0.1572 0.0393
0.0792 30.28 19500 0.1768 0.1599 0.0407
0.0798 31.06 20000 0.1732 0.1558 0.0394
0.0779 31.83 20500 0.1694 0.1544 0.0388
0.0718 32.61 21000 0.1709 0.1578 0.0399
0.0732 33.38 21500 0.1697 0.1523 0.0391
0.0708 34.16 22000 0.1616 0.1474 0.0375
0.0678 34.94 22500 0.1698 0.1474 0.0375
0.0642 35.71 23000 0.1681 0.1459 0.0369
0.0661 36.49 23500 0.1612 0.1411 0.0357
0.0629 37.27 24000 0.1662 0.1414 0.0355
0.0587 38.04 24500 0.1659 0.1408 0.0351
0.0581 38.82 25000 0.1612 0.1382 0.0352
0.0556 39.6 25500 0.1647 0.1376 0.0345
0.0543 40.37 26000 0.1658 0.1335 0.0337
0.052 41.15 26500 0.1716 0.1369 0.0343
0.0513 41.92 27000 0.1600 0.1317 0.0330
0.0491 42.7 27500 0.1671 0.1311 0.0328
0.0463 43.48 28000 0.1613 0.1289 0.0324
0.0468 44.25 28500 0.1599 0.1260 0.0315
0.0435 45.03 29000 0.1556 0.1232 0.0308
0.043 45.81 29500 0.1588 0.1240 0.0309
0.0421 46.58 30000 0.1567 0.1217 0.0308
0.04 47.36 30500 0.1533 0.1198 0.0302
0.0389 48.14 31000 0.1582 0.1185 0.0297
0.0387 48.91 31500 0.1576 0.1187 0.0297
0.0376 49.69 32000 0.1560 0.1182 0.0295

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Tokenizers 0.11.0
  • pyctcdecode 0.3.0
  • kenlm
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Datasets used to train gigant/romanian-wav2vec2

Space using gigant/romanian-wav2vec2

Evaluation results