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--- |
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language: sr |
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datasets: |
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- juznevesti-sr |
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tags: |
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- audio |
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- automatic-speech-recognition |
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widget: |
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- example_title: Croatian 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: Croatian 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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- example_title: Croatian example 3 |
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src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav |
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--- |
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# wav2vec2-xls-r-juznevesti |
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This model for Serbian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 58 hours of audio and transcripts from [Južne vesti](https://www.juznevesti.com/), programme '15 minuta'. |
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For more info on the dataset creation see [this repo](https://github.com/clarinsi/parlaspeech/tree/main/juzne_vesti). |
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## Metrics |
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Evaluation is performed on the dev and test portions of the JuzneVesti dataset |
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| | dev | test | |
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|:----|---------:|---------:| |
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| WER | 0.295206 | 0.290094 | |
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| CER | 0.140766 | 0.137642 | |
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## Usage in `transformers` |
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Tested with `transformers==4.18.0`, `torch==1.11.0`, and `SoundFile==0.10.3.post1`. |
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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-juznevesti-sr") |
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model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-juznevesti-sr") |
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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]) |
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transcription # 'velik broj poslovnih subjekata posluje sa minosom velik deo' |
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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` | 20 | |
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| `learning_rate` | 3e-4 | |
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| `warmup_steps` | 500 | |
