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Wav2vec2 Large L2 Arctic Supcon Repeated 8fold 0

Associated paper: Contrastive Regularization for Accent-Robust ASR.

TipoOtherVersiónRevision c2e3e81Publicador@ThaiVanPhat95Ccalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
1,18 GB
Versión
vRevision c2e3e81
Actualizado
28 sept 2026
Publicador
@ThaiVanPhat95
Descargas
3
Fuente
Hugging Face

Descripción de la fuente

Wav2Vec2 Large L2-ARCTIC SupCon Repeated: 8-Fold Split 0

Associated paper: Contrastive Regularization for Accent-Robust ASR.

English accented-speech recognition model trained with CTC and supervised contrastive learning. Repeated L2-ARCTIC prompts provide positive SupCon pairs. This checkpoint was trained using split 0 of the repository's 8-fold setup.

The included processor supports greedy CTC decoding.

Data and Code

Usage

import torch
import soundfile as sf
from transformers import AutoModelForCTC, AutoProcessor

model_id = "thaivanphat95/wav2vec2-large-l2-arctic-supcon-repeated-8fold-0"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCTC.from_pretrained(model_id).eval()

audio, sample_rate = sf.read("audio.wav")
inputs = processor(audio, sampling_rate=sample_rate, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

print(processor.batch_decode(torch.argmax(logits, dim=-1))[0])

Audio should be mono and sampled at 16 kHz.

Citation

@article{thai2026contrastive,
  title={Contrastive Regularization for Accent-Robust ASR},
  author={Thai, Van-Phat and Dhruv, Aradhya and Pham, Duc-Thinh and Alam, Sameer},
  journal={arXiv preprint arXiv:2605.03297},
  year={2026},
  doi={10.48550/arXiv.2605.03297}
}

Limitations and License

The model is specialized for L2-ARCTIC-style English read speech and should not be used without review in safety-critical settings. Model-weight use and redistribution may be affected by the licenses of the pretrained model and L2-ARCTIC. The training code's Apache-2.0 license does not cover these weights.

Fuentes

1 fuente
Hugging FacePrincipalVersión c2e3e81328c9928f5c064b4191a382a377fd47da
huggingface.co/ThaiVanPhat95/wav2vec2-large-l2-arctic-supcon-repeated-8fold-0/tree/c2e3e81328c9928f5c064b4191a382a377fd47da

Verificado 9 ago

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 1,18 GB · SHA-256 063e8b34fb79…efc7 · Hugging Face

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Extractos de fuentes

3 extractos

--- language: - en pipeline_tag: automatic-speech-recognition library_name: transformers tags: - arxiv:2605.03297 - wav2vec2 - ctc - supervised-contrastive-learning - accented-speech - l2-arctic license: other --- # Wav2Vec2 Large L2-ARCTIC SupCon Repeated: 8-Fold Split 0 Associated paper: [Contrastive Regularization for Accent-Robust ASR](https://arxiv.org/abs/2605.03297). English accented-speech recognition model trained with CTC and supervised contrastive learning. Repeated L2-ARCTIC prompts provide positive SupCon pairs. This checkpoint was trained using split 0 of the repository's 8-fold setup. The included processor supports greedy CTC decoding. ## Data and Code - L2-ARCTIC: https://psi.engr.tamu.edu/l2-arctic-corpus/ - Training code and split metadata: https://github.com/thaivanphat95/robust-atc-asr ## Usage ```python import torch import soundfile as sf from transformers import AutoModelForCTC, AutoProcessor model_id = "thaivanphat95/wav2vec2-large-l2-arctic-supcon-repeated-8fold-0" processor = AutoProcessor.from_pretrained(model_id) model = AutoModelForCTC.from_pretrained(model_id).eval() audio, sample_rate = sf.read("audio.wav") inputs = processor(audio, sampling_rate=sample_rate, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits print(processor.batch_decode(torch.argmax(logits, dim=-1))[0]) ``` Audio should be mono and sampled at 16 kHz. ## Citation ```bibtex @article{thai2026contrastive, title={Contrastive Regularization for Accent-Robust ASR}, author={Thai, Van-Phat and Dhruv, Aradhya and Pham, Duc-Thinh and Alam, Sameer}, journal={arXiv preprint arXiv:2605.03297}, year={2026}, doi={10.48550/arXiv.2605.03297} } ``` ## Limitations and License The model is specialized for L2-ARCTIC-style English read speech and should not be used without review in safety-critical settings. Model-weight use and redistribution may be affected by the licenses of the pretrained model and L2-ARCTIC. The training code's Apache-2.0 license does not cover these weights.

Source context: 3 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo ThaiVanPhat95/wav2vec2-large-l2-arctic-supcon-repeated-8fold-0

Source context: 4 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo ThaiVanPhat95/wav2vec2-large-l2-arctic-supcon-repeated-8fold-0