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wav2vec2-xls-r-juznevesti-sr

Automatic Speech Recognition
Transformers
PyTorch
Serbian
wav2vec2
audio
Inference Endpoints
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wav2vec2-xls-r-juznevesti-sr / README.md
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Added info on dataset creation.
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metadata
language: sr
datasets:
  - juznevesti-sr
tags:
  - audio
  - automatic-speech-recognition
widget:
  - example_title: Croatian example 1
    src: >-
      https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a
  - example_title: Croatian example 2
    src: >-
      https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav
  - example_title: Croatian example 3
    src: >-
      https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav

wav2vec2-large-juznevesti

This model for Serbian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 58 hours of audio and transcripts from Južne vesti, programme '15 minuta'.

For more info on the dataset creation see this repo.

Metrics

Evaluation is performed on the dev and test portions of the JuzneVesti dataset

dev test
WER 0.295206 0.290094
CER 0.140766 0.137642

Usage in transformers

Tested with transformers==4.18.0, torch==1.11.0, and SoundFile==0.10.3.post1.

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(
    "5roop/wav2vec2-xls-r-juznevesti-sr")
model = Wav2Vec2ForCTC.from_pretrained("5roop/wav2vec2-xls-r-juznevesti-sr")


# download the example wav files:
os.system("wget https://huggingface.co/classla/wav2vec2-xls-r-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])

transcription # 'velik broj poslovnih subjekata posluje sa minosom velik deo'

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