--- language: en datasets: - superb tags: - speech - audio - wav2vec2 license: apache-2.0 --- # Wav2Vec2-Base for Intent Classification ## Model description This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands). The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) ## Task and dataset description Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/) dataset, where each utterance is tagged with three intent labels: **action**, **object**, and **location**. For the original model's training and evaluation instructions refer to the [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#ic-intent-classification---fluent-speech-commands). ## Usage examples You can use the model directly like so: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example["speech"] = speech return example # load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "ic", split="test") dataset = dataset.map(map_to_array) model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ic") # compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(**inputs).logits action_ids = torch.argmax(logits[:, :6], dim=-1).tolist() action_labels = [model.config.id2label[_id] for _id in action_ids] object_ids = torch.argmax(logits[:, 6:20], dim=-1).tolist() object_labels = [model.config.id2label[_id + 6] for _id in object_ids] location_ids = torch.argmax(logits[:, 20:24], dim=-1).tolist() location_labels = [model.config.id2label[_id + 20] for _id in location_ids] ``` ## Eval results The evaluation metric is accuracy. | | **s3prl** | **transformers** | |--------|-----------|------------------| |**test**| `0.9235` | `N/A` | ### BibTeX entry and citation info ```bibtex @article{yang2021superb, title={SUPERB: Speech processing Universal PERformance Benchmark}, author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others}, journal={arXiv preprint arXiv:2105.01051}, year={2021} } ```