Create README.md

README.md

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+---
+language: hr
+datasets:
+- parlaspeech-hr
+tags:
+- audio
+- automatic-speech-recognition
+- parlaspeech
+widget:
+- example_title: example 1
+  src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a
+- example_title: example 2
+  src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav
+
+---
+
+# wav2vec2-xls-r-parlaspeech-hr
+
+This model for Croatian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](facebook/wav2vec2-large-slavic-voxpopuli-v2) 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).
+
+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.](https://arxiv.org/abs/2010.02810) was applied by Vuk Batanović and Lenka Bajčetić, the transcripts were normalised by Danijel Korzinek, while the final modelling was performed by Peter Rupnik.
+
+If you use this model, please cite the following paper:
+
+Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. Submitted to ParlaCLARIN@LREC.
+
+## Metrics
+
+|split|CER|WER|
+|---|---|---|
+|dev|0.0311|0.0921|
+|test|0.0222|0.0679|
+
+## Usage in `transformers`
+
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import soundfile as sf
+import torch
+import os
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+# load model and tokenizer
+processor = Wav2Vec2Processor.from_pretrained(
+    "classla/wav2vec2-large-slavic-parlaspeech-hr")
+model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-large-slavic-parlaspeech-hr")
+# download the example wav files:
+os.system("wget https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr/raw/main/00020570a.flac.wav")
+# read the wav file 
+speech, sample_rate = sf.read("00020570a.flac.wav")
+input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.to(device)
+# remove the raw wav file
+os.system("rm 00020570a.flac.wav")
+# retrieve logits
+logits = model.to(device)(input_values).logits
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.decode(predicted_ids[0]).lower()
+# transcription: 'veliki broj poslovnih subjekata posluje sa minusom velik dio'
+```
+
+
+
+## Training hyperparameters
+
+In fine-tuning, the following arguments were used:
+
+| arg                           | value |
+|-------------------------------|-------|
+| `per_device_train_batch_size` | 16    |
+| `gradient_accumulation_steps` | 4     |
+| `num_train_epochs`            | 8     |
+| `learning_rate`               | 3e-4  |
+| `warmup_steps`                | 500   |