
# Wav2Vec2-Large-XLSR-Indonesian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice) and synthetic voices
generated using [Artificial Common Voicer](https://github.com/cahya-wirawan/artificial-commonvoice), which
again based on Google Text To Speech.
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("cahya/wav2vec2-large-xlsr-indonesian-mix")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")


# 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"])
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
    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("cahya/wav2vec2-large-xlsr-indonesian-mix")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix") 
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'


# 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"])
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
    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**: 19.36 %

## 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/cahya-wirawan/indonesian-speech-recognition) 

{"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 Mix by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": 19.36, "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

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset and synthetic voices
generated using Artificial Common Voicer, which
again based on Google Text To Speech.
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: 19.36 %

## 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 

# Wav2Vec2-Large-XLSR-Indonesian

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset and synthetic voices
generated using Artificial Common Voicer, which
again based on Google Text To Speech.
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: 19.36 % ## 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
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

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset and synthetic voices
generated using Artificial Common Voicer, which
again based on Google Text To Speech.
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: 19.36 % ## 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
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

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset and synthetic voices
generated using Artificial Common Voicer, which
again based on Google Text To Speech.
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: 19.36 %## 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