
# wav2vec2-large-xlsr-53-Dutch

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in 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("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Dutch")

model = Wav2Vec2ForCTC.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Dutch")

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):

  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 re

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

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Dutch")

model = Wav2Vec2ForCTC.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Dutch")

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"] = 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**:  26.494162 %

## Training

The Common Voice `train`, `validation` datasets were used for training.
{"language": "nl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Dutch by Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice nl", "type": "common_voice", "args": "nl"}, "metrics": [{"type": "wer", "value": 26.494162, "name": "Test WER"}]}]}]}
transformers pytorch jax 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 #jax #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 in 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:  26.494162 %

## Training

The Common Voice 'train', 'validation' datasets were used for training.
# wav2vec2-large-xlsr-53-Dutch

Fine-tuned facebook/wav2vec2-large-xlsr-53 in 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:  26.494162 % ## Training

The Common Voice 'train', 'validation' datasets were used for training.
TAGS
#transformers #pytorch #jax #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 in 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:  26.494162 % ## Training

The Common Voice 'train', 'validation' datasets were used for training.
passage: TAGS
#transformers #pytorch #jax #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 in 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:  26.494162 %## Training

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