|
--- |
|
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 |
|
- example_title: example 3 |
|
src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav |
|
--- |
|
|
|
# wav2vec2-large-slavic-parlaspeech-hr-lm |
|
|
|
This model for Croatian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](https://huggingface.co/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) and enhanced with a 5-gram language model based on the [ParlaMint dataset](http://hdl.handle.net/11356/1432). |
|
|
|
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. http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/pdf/2022.parlaclariniii-1.16.pdf |
|
|
|
## Metrics |
|
|
|
Evaluation is performed on the dev and test portions of the [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494) dataset. |
|
|
|
|split|CER|WER| |
|
|---|---|---| |
|
|dev|0.0253|0.0556| |
|
|test|0.0188|0.0430| |
|
|
|
|
|
## Usage in `transformers` |
|
|
|
Tested with `transformers==4.18.0`, `torch==1.11.0`, and `SoundFile==0.10.3.post1`. |
|
|
|
```python |
|
from transformers import Wav2Vec2ProcessorWithLM, 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 = Wav2Vec2ProcessorWithLM.from_pretrained( |
|
"classla/wav2vec2-large-slavic-parlaspeech-hr-lm") |
|
model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-large-slavic-parlaspeech-hr-lm") |
|
# download the example wav files: |
|
os.system("wget https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr-lm/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.cuda() |
|
inputs = processor(speech, sampling_rate=sample_rate, return_tensors="pt") |
|
with torch.no_grad(): |
|
logits = model(**inputs).logits |
|
transcription = processor.batch_decode(logits.numpy()).text[0] |
|
|
|
# remove the raw wav file |
|
os.system("rm 00020570a.flac.wav") |
|
|
|
transcription # 'velik broj poslovnih subjekata poslao je 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 | |
