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--- |
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language: en |
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datasets: |
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- superb |
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tags: |
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- speech |
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- audio-classification |
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- hubert |
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license: apache-2.0 |
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--- |
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# Hubert-Base for Intent Classification |
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## Model description |
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This is a ported version of [S3PRL's Hubert for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands). |
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The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960), which is pretrained on 16kHz |
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sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. |
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For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) |
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## Task and dataset description |
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Intent Classification (IC) classifies utterances into predefined classes to determine the intent of |
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speakers. SUPERB uses the |
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[Fluent Speech Commands](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/) |
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dataset, where each utterance is tagged with three intent labels: **action**, **object**, and **location**. |
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For the original model's training and evaluation instructions refer to the |
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[S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#ic-intent-classification---fluent-speech-commands). |
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## Usage examples |
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You can use the model directly like so: |
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```python |
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import torch |
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import librosa |
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from datasets import load_dataset |
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from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor |
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def map_to_array(example): |
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speech, _ = librosa.load(example["file"], sr=16000, mono=True) |
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example["speech"] = speech |
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return example |
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# load a demo dataset and read audio files |
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dataset = load_dataset("anton-l/superb_demo", "ic", split="test") |
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dataset = dataset.map(map_to_array) |
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model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ic") |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic") |
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# compute attention masks and normalize the waveform if needed |
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inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") |
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logits = model(**inputs).logits |
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action_ids = torch.argmax(logits[:, :6], dim=-1).tolist() |
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action_labels = [model.config.id2label[_id] for _id in action_ids] |
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object_ids = torch.argmax(logits[:, 6:20], dim=-1).tolist() |
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object_labels = [model.config.id2label[_id + 6] for _id in object_ids] |
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location_ids = torch.argmax(logits[:, 20:24], dim=-1).tolist() |
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location_labels = [model.config.id2label[_id + 20] for _id in location_ids] |
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``` |
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## Eval results |
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The evaluation metric is accuracy. |
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| | **s3prl** | **transformers** | |
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|--------|-----------|------------------| |
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|**test**| `0.9834` | `N/A` | |
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### BibTeX entry and citation info |
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```bibtex |
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@article{yang2021superb, |
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title={SUPERB: Speech processing Universal PERformance Benchmark}, |
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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}, |
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journal={arXiv preprint arXiv:2105.01051}, |
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year={2021} |
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} |
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``` |
