Model Description
The Wav2vec2 base model facebook/wav2vec2-base-960h fine tuned on phoneme recognition task for the dutch language.
Usage
To transcribe in phonemes audio files the model can be used as a standalone acoustic model as follows:
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch")
model = Wav2Vec2ForCTC.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch")
# load dummy dataset and read soundfiles
ds = load_dataset("common_voice", "nl", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
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Dataset used to train Clementapa/wav2vec2-base-960h-phoneme-reco-dutch
Space using Clementapa/wav2vec2-base-960h-phoneme-reco-dutch 1
Evaluation results
- Test PER on CommonVoice (clean)test set self-reported20.830
- Val PER on CommonVoice (clean)test set self-reported16.180