This model for Croatian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 72 hours of recordings and transcripts from the Croatian parliament. This training dataset is an early result of the second iteration of the ParlaMint project inside which the dataset will be extended and published under the name of ParlaSpeech-HR and an open licence.
The efforts resulting in this model were coordinated by Nikola Ljubešić, the rough manual data alignment was performed by Ivo-Pavao Jazbec, the method for fine automatic data alignment from Plüss et al. was applied by Vuk Batanović and Lenka Bajčetić, the transcripts were normalised by Danijel Korzinek, while the final modelling was performed by Peter Rupnik.
Initial evaluation on partially noisy data showed the model to achieve a word error rate of 13.68% and a character error rate of 4.56%.
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import Audio import soundfile as sf import torch import os # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained( "classla/wav2vec2-xls-r-parlaspeech-hr") model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr") # download the example wav files: os.system("curl https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav") # read the wav file as datasets.Audio object audio = Audio(sampling_rate=16000).decode_example("00020570a.flac.wav") # remove the raw wav file os.system("rm 00020570a.flac.wav") # tokenize input_values = processor( audio["array"], return_tensors="pt", padding=True, sampling_rate=16000).input_values # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) # transcription: ['veliki broj poslovnih subjekata posluje sa minusom velik dio']
In fine-tuning, the following arguments were used:
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