# Wav2Vec2-Large-XLSR-53-Greek

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, e.g. French and eventually add more datasets that were used and eventually remove common voice if model was not trained on 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:

import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1")
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")

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

# Preprocessing the datasets.
def speech_file_to_array_fn(batch):
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)

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 Greek test data of Common Voice.

import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1")
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")
model.to("cuda")

chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.

def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.

def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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: 56.253154 %

## Training

The Common Voice train, validation, and ... datasets were used for training as well as ... and ... # TODO: adapt to state all the datasets that 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.