Wav2vec2 Large Xlsr Frisian

🚀 Wav2Vec2-Large-XLSR-53-Frisian

This is a fine - tuned facebook/wav2vec2-large-xlsr-53 model for Frisian, leveraging the Common Voice dataset. When using this model, ensure that the speech input is sampled at 16kHz.

🚀 Quick Start

This fine - tuned model is based on facebook/wav2vec2-large-xlsr-53 and trained on Frisian data from Common Voice. Remember to sample your speech input at 16kHz when using this model.

✨ Features

• Language: Specifically fine - tuned for Frisian.
• Data Source: Utilizes the Common Voice dataset for training.
• Task: Designed for automatic speech recognition.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

The model can be used directly (without a language model) as follows:

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

test_dataset = load_dataset("common_voice", "fy-NL", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")

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


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])

Advanced Usage

The model can be evaluated as follows on the Frisian 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", "fy-NL", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\–\\—\\¬\\⅛]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

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)

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: 21.72 %

📚 Documentation

Training

The Common Voice train, validation datasets were used for training. The script used for training can be found here A notebook of the evaluation script can be found here

📄 License

This model is licensed under the apache - 2.0 license.

📊 Model Information