language: ca
datasets:
- common_voice
- parlament_parla
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- speech-to-text
license: apache-2.0
model-index:
- name: Catalan VoxPopuli Wav2Vec2 Large
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
datasets:
- name: Common Voice ca
type: common_voice
args: ca
- name: ParlamentParla
url: https://www.openslr.org/59/
metrics:
- name: Test WER
type: wer
value: 5.98
- name: Google Crowsourced Corpus WER
type: wer
value: 12.14
- name: Audiobook “La llegenda de Sant Jordi” WER
type: wer
value: 12.02
Wav2Vec2-Large-100k-VoxPopuli-Català
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli on Catalan language using the Common Voice and ParlamentParla datasets.
Attention: The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found here. Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model. WER was calculated using this test.csv which was not seen by the model during training/evaluation.
You can find training and evaluation scripts in the github repository ccoreilly/wav2vec2-catala
When using this model, make sure that your speech input is sampled at 16kHz.
Results
Word error rate was evaluated on the following datasets unseen by the model:
| Dataset | WER |
|---|---|
| Test split CV+ParlamentParla | 5.98% |
| Google Crowsourced Corpus | 12.14% |
| Audiobook “La llegenda de Sant Jordi” | 12.02% |
Usage
The model can be used directly (without a language model) as follows:
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ca", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])