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--- |
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language: hr |
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datasets: |
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- parlaspeech-hr |
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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-parlaspeech-hr/raw/main/1800.m4a |
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- example_title: example 2 |
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src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav |
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--- |
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# wav2vec2-xls-r-parlaspeech-hr |
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This model for Croatian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494). |
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If you use this model, please cite the following paper: |
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Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/pdf/2022.parlaclariniii-1.16.pdf |
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## Metrics |
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Evaluation is performed on the dev and test portions of the [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494) dataset. |
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|split|CER|WER| |
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|---|---|---| |
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|dev|0.0335|0.1046| |
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|test|0.0234|0.0761| |
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There are multiple models available, and in terms of CER and WER, the best-performing model is [wav2vec2-large-slavic-parlaspeech-hr-lm](https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr-lm). |
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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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import soundfile as sf |
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import torch |
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import os |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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# load model and tokenizer |
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processor = Wav2Vec2Processor.from_pretrained( |
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"classla/wav2vec2-xls-r-parlaspeech-hr") |
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model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr") |
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# download the example wav files: |
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os.system("wget https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav") |
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# read the wav file |
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speech, sample_rate = sf.read("00020570a.flac.wav") |
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input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.to(device) |
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# remove the raw wav file |
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os.system("rm 00020570a.flac.wav") |
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# retrieve logits |
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logits = model.to(device)(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.decode(predicted_ids[0]).lower() |
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# transcription: 'veliki broj poslovnih subjekata posluje sa minusom velik dio' |
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``` |
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## Training hyperparameters |
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In fine-tuning, the following arguments were used: |
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| arg | value | |
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|-------------------------------|-------| |
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| `per_device_train_batch_size` | 16 | |
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| `gradient_accumulation_steps` | 4 | |
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| `num_train_epochs` | 8 | |
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| `learning_rate` | 3e-4 | |
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| `warmup_steps` | 500 | |
