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--- |
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language: hr |
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datasets: |
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- parlaspeech |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- parlaspeech |
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widget: |
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- example_title: example 1 |
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src: https://huggingface.co/classla/wav2vec2-xls-r-sabor-hr/raw/main/00020578b.flac.wav |
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- example_title: example 2 |
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src: https://huggingface.co/classla/wav2vec2-xls-r-sabor-hr/raw/main/00020570a.flac.wav |
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--- |
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# wav2vec2-xls-r-parlaspeech-hr |
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This model is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned over 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](https://www.clarin.eu/content/parlamint-towards-comparable-parliamentary-corpora) inside which the dataset will be extended and published under a permissive license. |
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The efforts resulting with 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.](https://arxiv.org/abs/2010.02810) was applied by Vuk Batanović and Lenka Bajčetić, while the final modelling was performed by Peter Rupnik. |
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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%. |
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## Usage in `transformers` |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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from datasets import Audio |
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import soundfile as sf |
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import torch |
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import os |
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# load model and tokenizer |
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processor = Wav2Vec2Processor.from_pretrained( |
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"classla/wav2vec2-xls-r-sabor-hr") |
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model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-sabor-hr") |
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# download the example wav files: |
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os.system("curl https://huggingface.co/classla/wav2vec2-xls-r-sabor-hr/raw/main/00020570a.flac.wav") |
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# read the wav file as datasets.Audio object |
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audio = Audio(sampling_rate=16000).decode_example("00020570a.flac.wav") |
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# remove the raw wav file |
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os.system("rm 00020570a.flac.wav") |
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# tokenize |
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input_values = processor( |
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audio["array"], return_tensors="pt", padding=True, |
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sampling_rate=16000).input_values |
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# retrieve logits |
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logits = model(input_values).logits |
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# take argmax and decode |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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# transcription: ['veliki broj poslovnih subjekata posluje sa minusom velik dio'] |
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``` |
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