Wav2vec2 Large Xlsr Greek 2

🚀 Wav2Vec2-Large-XLSR-53-Greek

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 on Greek data from Common Voice, aiming to provide high - quality automatic speech recognition for the Greek language.

⚠️ Important Note

When using this model, make sure that your speech input is sampled at 16kHz.

🚀 Quick Start

This model is fine - tuned from facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice. The Greek CV data has a majority of male voices. To balance it, synthesised female voices were created using the approach discussed here. The text from the common - voice dataset was used to synthesize voices of female speakers using [Google's TTS Standard Voice model](https://cloud.google.com/text - to - speech).

The fine - tuning results are as follows:

• Fine - tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice for 5 epochs >> 56.25% WER
• Resuming from checkpoints and trained for another 15 epochs >> 34.00% WER
• Added synthesised female voices and trained for 12 epochs >> 34.00% WER (no change)

💻 Usage Examples

Basic Usage

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "el", split="test[:2%]")

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.
# 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])

📚 Documentation

Evaluation

The model can be evaluated as follows on the Greek test data of Common Voice.

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "el", split="test") 
wer = load_metric("wer")

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.
# 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: 45.048955 %

Training

The Common Voice train and validation datasets 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.