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---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
- gigant/romanian_speech_synthesis_0_8_1
model-index:
- name: wav2vec2-ro-300m_01
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Dev WER (without LM)
type: wer
value: 46.99
- name: Dev CER (without LM)
type: cer
value: 16.04
- name: Dev WER (with LM)
type: wer
value: 38.63
- name: Dev CER (with LM)
type: cer
value: 14.52
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: mozilla-foundation/common_voice_8_0
args: ro
metrics:
- name: Test WER (without LM)
type: wer
value: 11.73
- name: Test CER (without LM)
type: cer
value: 2.93
- name: Test WER (with LM)
type: wer
value: 7.31
- name: Test CER (with LM)
type: cer
value: 2.17
---
You can test this model online with the [Space for Romanian Speech Recognition](https://huggingface.co/spaces/gigant/romanian-speech-recognition)
The model ranked TOP-1 on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge : [**Leaderboard**](https://huggingface.co/spaces/speech-recognition-community-v2/FinalLeaderboard)
# Romanian Wav2Vec2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset, with extra training data from [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) 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](https://huggingface.co/facebook/wav2vec2-xls-r-300m) with a speech recognition CTC head and an added 5-gram language model (using [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) and [kenlm](https://github.com/kpu/kenlm)) trained on the [Romanian Corpora Parliament](gigant/ro_corpora_parliament_processed) 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](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) 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 :
- [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : train + validation + other splits
- [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) : train + test splits
Evaluation data :
- [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : test split
## 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
|