In this article, we’ll take you step-by-step through the process of finetuning the XLSR Wav2Vec2 model for Lithuanian speech recognition. By the end of this guide, you’ll have a clear understanding of how to implement this in your projects using the Common Voice dataset.

Understanding the Setup

Think of the XLSR Wav2Vec2 model as a sponge, designed to soak up the nuances of spoken words in various languages. When we fine-tune this sponge using our specific dataset — in this case, Lithuanian audio samples from the Common Voice — we’re essentially training it to recognize and reproduce those spoken words more accurately. This process requires careful preparation, much like preparing a specific flavor of soup where each ingredient needs to be carefully selected.

Prerequisites

• Python installed on your machine
• Pytorch and torchaudio libraries
• Access to the datasets: Common Voice
• A good GPU for model training

Step-by-Step Implementation

Follow these steps to fine-tune the model:

1. Load Required Libraries

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

2. Load Your Dataset

You’ll first want to load your test dataset from Common Voice:

test_dataset = load_dataset("common_voice", "lt", split="test[:2%]")

3. Initialize Your Processor and Model

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

4. Data Resampling

Set up a resampling process to ensure that the audio is at 16kHz:

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

5. Preprocess the Audio Files

Create a function to transform audio files into usable data:

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)

6. Making Predictions

Now it’s time to run the model on the data:

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])

Model Evaluation

Evaluate your model with the common voice test data:

from datasets import load_metric

wer = load_metric("wer")
result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER:", 100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))

Troubleshooting

While loading or processing your model, you might encounter errors. Here are some troubleshooting ideas:

• Ensure that you have all required libraries installed and are using compatible versions.
• Check that your input audio files are sampled correctly at 16kHz; otherwise, you might receive audio processing errors.
• If your model is not performing as expected, experiment with different portions of the dataset for training and evaluation.
• In some cases, running the model on CPU instead of GPU can be slower; ensure you have the right hardware setup.

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Conclusion

By following the above steps, you should have successfully fine-tuned the XLSR Wav2Vec2 model for speech recognition in Lithuanian. With practice, you’ll become more adept at handling various models and refining their performance.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.