README.md

---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
- gigant/romanian_speech_synthesis_0_8_1
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: wav2vec2-ro-300m_01
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Robust Speech Event
      type: speech-recognition-community-v2/dev_data
      args: ro
    metrics:
    - type: wer
      value: 46.99
      name: Dev WER (without LM)
    - type: cer
      value: 16.04
      name: Dev CER (without LM)
    - type: wer
      value: 38.63
      name: Dev WER (with LM)
    - type: cer
      value: 14.52
      name: Dev CER (with LM)
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice
      type: mozilla-foundation/common_voice_8_0
      args: ro
    metrics:
    - type: wer
      value: 11.73
      name: Test WER (without LM)
    - type: cer
      value: 2.93
      name: Test CER (without LM)
    - type: wer
      value: 7.31
      name: Test WER (with LM)
    - type: cer
      value: 2.17
      name: Test CER (with LM)
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Robust Speech Event - Test Data
      type: speech-recognition-community-v2/eval_data
      args: ro
    metrics:
    - type: wer
      value: 43.23
      name: Test WER
---

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 :

* [**The 🤗 Speech Bench**](https://huggingface.co/spaces/huggingface/hf-speech-bench)

* [**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