
# Fine-tuned XLSR-53 large model for speech recognition in Dutch

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10).
When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

## Usage

The model can be used directly (without a language model) as follows...

Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:

```python
from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-dutch")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

transcriptions = model.transcribe(audio_paths)
```

Writing your own inference script:

```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "nl"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-dutch"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)
```

| Reference  | Prediction |
| ------------- | ------------- |
| DE ABORIGINALS ZIJN DE OORSPRONKELIJKE BEWONERS VAN AUSTRALIË. | DE ABBORIGENALS ZIJN DE OORSPRONKELIJKE BEWONERS VAN AUSTRALIË |
| MIJN TOETSENBORD ZIT VOL STOF. | MIJN TOETSENBORD ZIT VOL STOF |
| ZE HAD DE BANK BESCHADIGD MET HAAR SKATEBOARD. | ZE HAD DE BANK BESCHADIGD MET HAAR SCHEETBOORD |
| WAAR LAAT JIJ JE ONDERHOUD DOEN? | WAAR LAAT JIJ HET ONDERHOUD DOEN |
| NA HET LEZEN VAN VELE BEOORDELINGEN HAD ZE EINDELIJK HAAR OOG LATEN VALLEN OP EEN LAPTOP MET EEN QWERTY TOETSENBORD. | NA HET LEZEN VAN VELE BEOORDELINGEN HAD ZE EINDELIJK HAAR OOG LATEN VALLEN OP EEN LAPTOP MET EEN QUERTITOETSEMBORD |
| DE TAMPONS ZIJN OP. | DE TAPONT ZIJN OP |
| MARIJKE KENT OLIVIER NU AL MEER DAN TWEE JAAR. | MAARRIJKEN KENT OLIEVIER NU AL MEER DAN TWEE JAAR |
| HET VOEREN VAN BROOD AAN EENDEN IS EIGENLIJK ONGEZOND VOOR DE BEESTEN. | HET VOEREN VAN BEUROT AAN EINDEN IS EIGENLIJK ONGEZOND VOOR DE BEESTEN |
| PARKET MOET JE STOFZUIGEN, TEGELS MOET JE DWEILEN. | PARKET MOET JE STOF ZUIGEN MAAR TEGELS MOET JE DWEILEN |
| IN ONZE BUURT KENT IEDEREEN ELKAAR. | IN ONZE BUURT KENT IEDEREEN ELKAAR |

## Evaluation

1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`

```bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-dutch --dataset mozilla-foundation/common_voice_6_0 --config nl --split test
```

2. To evaluate on `speech-recognition-community-v2/dev_data`

```bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-dutch --dataset speech-recognition-community-v2/dev_data --config nl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```

## Citation
If you want to cite this model you can use this:

```bibtex
@misc{grosman2021xlsr53-large-dutch,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {D}utch},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-dutch}},
  year={2021}
}
```
{"language": "nl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "mozilla-foundation/common_voice_6_0", "nl", "robust-speech-event", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "mozilla-foundation/common_voice_6_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLSR Wav2Vec2 Dutch by Jonatas Grosman", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice nl", "type": "common_voice", "args": "nl"}, "metrics": [{"type": "wer", "value": 15.72, "name": "Test WER"}, {"type": "cer", "value": 5.35, "name": "Test CER"}, {"type": "wer", "value": 12.84, "name": "Test WER (+LM)"}, {"type": "cer", "value": 4.64, "name": "Test CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 35.79, "name": "Dev WER"}, {"type": "cer", "value": 17.67, "name": "Dev CER"}, {"type": "wer", "value": 31.54, "name": "Dev WER (+LM)"}, {"type": "cer", "value": 16.37, "name": "Dev CER (+LM)"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio hf-asr-leaderboard mozilla-foundation/common_voice_6_0 nl robust-speech-event speech xlsr-fine-tuning-week dataset:common_voice dataset:mozilla-foundation/common_voice_6_0 doi:10.57967/hf/0203 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #mozilla-foundation/common_voice_6_0 #nl #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #dataset-mozilla-foundation/common_voice_6_0 #doi-10.57967/hf/0203 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Fine-tuned XLSR-53 large model for speech recognition in Dutch
==============================================================


Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the train and validation splits of Common Voice 6.1 and CSS10.
When using this model, make sure that your speech input is sampled at 16kHz.


This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)


The script used for training can be found here: URL


Usage
-----


The model can be used directly (without a language model) as follows...


Using the HuggingSound library:


Writing your own inference script:



Evaluation
----------


1. To evaluate on 'mozilla-foundation/common\_voice\_6\_0' with split 'test'
2. To evaluate on 'speech-recognition-community-v2/dev\_data'


If you want to cite this model you can use this:



TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #mozilla-foundation/common_voice_6_0 #nl #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #dataset-mozilla-foundation/common_voice_6_0 #doi-10.57967/hf/0203 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #mozilla-foundation/common_voice_6_0 #nl #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #dataset-mozilla-foundation/common_voice_6_0 #doi-10.57967/hf/0203 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
