
# Wav2Vec2-Large-XLSR-53-Lithuanian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Lithuanian 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", "lt", split="test[:2%]") 

processor = Wav2Vec2Processor.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")

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 Lithuanian 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", "lt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
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**: 35.87 %  


## Training

The Common Voice datasets `except the test` set were used for training.

The script used for training can be found [here](https://github.com/ebdundar/)
{"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Lithuanian by Enes Burak Dundar", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice lt", "type": "common_voice", "args": "lt"}, "metrics": [{"type": "wer", "value": 35.87, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week lt 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 #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-Lithuanian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian 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 Lithuanian test data of Common Voice.




Test Result: 35.87 %  


## Training

The Common Voice datasets 'except the test' set were used for training.

The script used for training can be found here
# Wav2Vec2-Large-XLSR-53-Lithuanian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian 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 Lithuanian test data of Common Voice.




Test Result: 35.87 % ## Training

The Common Voice datasets 'except the test' set were used for training.

The script used for training can be found here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-Lithuanian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian 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 Lithuanian test data of Common Voice.




Test Result: 35.87 % ## Training

The Common Voice datasets 'except the test' set were used for training.

The script used for training can be found here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-Lithuanian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian 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 Lithuanian test data of Common Voice.




Test Result: 35.87 %## Training

The Common Voice datasets 'except the test' set were used for training.

The script used for training can be found here