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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 |
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- wav2vec2 |
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- audio-classification |
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license: apache-2.0 |
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widget: |
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- example_title: Speech Commands "down" |
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src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav |
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- example_title: Speech Commands "go" |
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src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_go.wav |
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--- |
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# Wav2Vec2-Large for Keyword Spotting |
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## Model description |
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This is a ported version of |
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[S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands). |
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The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), 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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Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of |
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words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and |
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inference time are all crucial. SUPERB uses the widely used |
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[Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. |
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The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the |
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false positive. |
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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#ks-keyword-spotting). |
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## Usage examples |
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You can use the model via the Audio Classification pipeline: |
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```python |
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from datasets import load_dataset |
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from transformers import pipeline |
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dataset = load_dataset("anton-l/superb_demo", "ks", split="test") |
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classifier = pipeline("audio-classification", model="superb/wav2vec2-large-superb-ks") |
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labels = classifier(dataset[0]["file"], top_k=5) |
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``` |
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Or use the model directly: |
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```python |
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import torch |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor |
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from torchaudio.sox_effects import apply_effects_file |
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effects = [["channels", "1"], ["rate", "16000"], ["gain", "-3.0"]] |
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def map_to_array(example): |
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speech, _ = apply_effects_file(example["file"], effects) |
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example["speech"] = speech.squeeze(0).numpy() |
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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", "ks", split="test") |
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dataset = dataset.map(map_to_array) |
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model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-large-superb-ks") |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-large-superb-ks") |
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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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predicted_ids = torch.argmax(logits, dim=-1) |
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labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] |
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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**| `0.9666` | `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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``` |
