
# Wav2Vec2-Large-XLSR-53-Dutch

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice)

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

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

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

test_dataset = load_dataset("common_voice", "nl", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("simonsr/wav2vec2-large-xlsr-dutch")
model = Wav2Vec2ForCTC.from_pretrained("simonsr/wav2vec2-large-xlsr-dutch")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], 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)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation

The model can be evaluated as follows on the Dutch test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import unidecode
import re

test_dataset = load_dataset("common_voice", "nl", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\(\)\=\´\–\&\…\—\’]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = unidecode.unidecode(batch["sentence"])
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

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

    pred_ids = torch.argmax(logits, dim=-1)
   
    batch["pred_strings"] = processor.batch_decode(pred_ids)
     return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 38.74 %

## Training

The Common Voice `train`, `validation`, and ... datasets were used for training.

The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
{"language": "nl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "simonsr wav2vec2-large-xlsr-dutch", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice nl", "type": "common_voice", "args": "nl"}, "metrics": [{"type": "wer", "value": 38.74, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week nl dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-Dutch

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

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




## Evaluation

The model can be evaluated as follows on the Dutch test data of Common Voice.



Test Result: 38.74 %

## Training

The Common Voice 'train', 'validation', and ... datasets were used for training.

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
# Wav2Vec2-Large-XLSR-53-Dutch

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice

When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

The model can be used directly (without a language model) as follows: ## Evaluation

The model can be evaluated as follows on the Dutch test data of Common Voice.



Test Result: 38.74 % ## Training

The Common Voice 'train', 'validation', and ... datasets were used for training.

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-Dutch

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice

When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

The model can be used directly (without a language model) as follows: ## Evaluation

The model can be evaluated as follows on the Dutch test data of Common Voice.



Test Result: 38.74 % ## Training

The Common Voice 'train', 'validation', and ... datasets were used for training.

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-Dutch

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice

When using this model, make sure that your speech input is sampled at 16kHz.## Usage

The model can be used directly (without a language model) as follows:## Evaluation

The model can be evaluated as follows on the Dutch test data of Common Voice.



Test Result: 38.74 %## Training

The Common Voice 'train', 'validation', and ... datasets were used for training.

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.