metadata

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 and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0 and enhanced with a language model.

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. 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	0.0253	0.0556
test	0.0188	0.0430

Usage in `transformers`

So far untested use from before:

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(
    "5roop/wav2vec2-large-slavic-parlaspeech-hr-lm")
model = Wav2Vec2ForCTC.from_pretrained("5roop/wav2vec2-large-slavic-parlaspeech-hr-lm")
# 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.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

wav2vec2-large-slavic-parlaspeech-hr-lm

Metrics

Usage in transformers

Training hyperparameters

Usage in `transformers`