
# Wav2Vec2-Large-XLSR-Indonesian

This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned 
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
It was trained using the default hyperparamer and for 2x30 epochs.
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", "id", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian-baseline")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian-baseline")

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[:2]["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)

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


## Evaluation

The model can be evaluated as follows on the Indonesian 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", "id", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian-baseline")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian-baseline") 
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 audio 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**: 25.55 %

## Training

The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ...  # TODO

The script used for training can be found [here](https://github.com/indonesian-nlp/indonesian-speech-recognition) 
(will be available soon)

{"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian Baseline by indonesian-nlp", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": 25.55, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week id 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 #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-Indonesian

This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned 
facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset.
It was trained using the default hyperparamer and for 2x30 epochs.
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 Indonesian test data of Common Voice.



Test Result: 25.55 %

## Training

The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ...  # TODO

The script used for training can be found here 
(will be available soon)

# Wav2Vec2-Large-XLSR-Indonesian

This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned 
facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset.
It was trained using the default hyperparamer and for 2x30 epochs.
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 Indonesian test data of Common Voice.



Test Result: 25.55 % ## Training

The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ...  # TODO

The script used for training can be found here 
(will be available soon)
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-Indonesian

This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned 
facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset.
It was trained using the default hyperparamer and for 2x30 epochs.
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 Indonesian test data of Common Voice.



Test Result: 25.55 % ## Training

The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ...  # TODO

The script used for training can be found here 
(will be available soon)
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-Indonesian

This is the baseline for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned 
facebook/wav2vec2-large-xlsr-53
model on the Indonesian Common Voice dataset.
It was trained using the default hyperparamer and for 2x30 epochs.
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 Indonesian test data of Common Voice.



Test Result: 25.55 %## Training

The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ...  # TODO

The script used for training can be found here 
(will be available soon)