wav2vec2-large-xlsr-53-icelandic-ep30-967h EU AI Act Compliance Profile

wav2vec2-large-xlsr-53-icelandic-ep30-967h

The "wav2vec2-large-xlsr-53-icelandic-ep30-967h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model facebook/wav2vec2-large-xlsr-53 for 30 epochs with 967 hours of Icelandic data collected by the Language and Voice Laboratory through the platform Samrómur.

The specific data that was used to fine-tune the model is the corpus Samrómur Milljón, which is the result of the automatic verification of 1 million of recordings comming from the corpus "Samromur Unverified 22.07". It has to be pointed out that this model was trained with different data than our previous model wav2vec2-large-xlsr-53-icelandic-ep10-1000h .

The fine-tuning process was performed during July (2023) in the servers of the Language and Voice Laboratory (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.

Evaluation

import torch
from transformers import Wav2Vec2Processor
from transformers import Wav2Vec2ForCTC

#Load the processor and model.
MODEL_NAME="language-and-voice-lab/wav2vec2-large-xlsr-53-icelandic-ep30-967h"
processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)

#Load the dataset
from datasets import load_dataset, load_metric, Audio
ds=load_dataset("language-and-voice-lab/samromur_children", split="test")

#Downsample to 16kHz
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))

#Process the dataset
def prepare_dataset(batch):
    audio = batch["audio"]
    #Batched output is "un-b

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