Wav2Vec2 Catal

Models de reconeixement automtic de la parla Wav2Vec2 pel Catal.

S'ha fet fine-tuning a partir de dos models base, el facebook/wav2vec2-large-xlsr-53 i el facebook/wav2vec2-large-100k-voxpopuli. Els podeu trobar a:

• ccoreilly/wav2vec2-large-xlsr-catala.
• ccoreilly/wav2vec2-large-100k-voxpopuli-catala.

Fine-tuned Wav2Vec2 models for the Catalan language based on facebook/wav2vec2-large-xlsr-53 and facebook/wav2vec2-large-100k-voxpopuli

You can find the models in the huggingface repository:

• ccoreilly/wav2vec2-large-xlsr-catala.
• ccoreilly/wav2vec2-large-100k-voxpopuli-catala.

Datasets

• Common Voice
• ParlamentParla

WER

Avaluada en els segents datasets no vistos durant l'entrenament:

Word error rate was evaluated on the following datasets unseen by the model:

Com que les dades de CommonVoice contenen metadades sobre l'edat, el gnere i la variant dialectal del parlant, podem avaluar el model segons aquests parmetres. Desafortunadament, per alguna de les categories no hi ha prou dades com per considerar la mostra significativa, s per aix que s'acompanya la taxa d'error amb la mida de la mostra.

Com fer-lo servir / Usage

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])