101 lines
3.6 KiB
Markdown
101 lines
3.6 KiB
Markdown
---
|
|
language: en
|
|
datasets:
|
|
- superb
|
|
tags:
|
|
- speech
|
|
- audio
|
|
- wav2vec2
|
|
- audio-classification
|
|
widget:
|
|
- example_title: Speech Commands "down"
|
|
src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav
|
|
- example_title: Speech Commands "go"
|
|
src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_go.wav
|
|
license: apache-2.0
|
|
---
|
|
|
|
# Wav2Vec2-Base for Keyword Spotting
|
|
|
|
## Model description
|
|
|
|
This is a ported version of
|
|
[S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_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
|
|
|
|
Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of
|
|
words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
|
|
inference time are all crucial. SUPERB uses the widely used
|
|
[Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task.
|
|
The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
|
|
false positive.
|
|
|
|
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#ks-keyword-spotting).
|
|
|
|
|
|
## Usage examples
|
|
|
|
You can use the model via the Audio Classification pipeline:
|
|
```python
|
|
from datasets import load_dataset
|
|
from transformers import pipeline
|
|
|
|
dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
|
|
|
|
classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-ks")
|
|
labels = classifier(dataset[0]["file"], top_k=5)
|
|
```
|
|
|
|
Or use the model directly:
|
|
```python
|
|
import torch
|
|
from datasets import load_dataset
|
|
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
|
|
from torchaudio.sox_effects import apply_effects_file
|
|
|
|
effects = [["channels", "1"], ["rate", "16000"], ["gain", "-3.0"]]
|
|
def map_to_array(example):
|
|
speech, _ = apply_effects_file(example["file"], effects)
|
|
example["speech"] = speech.squeeze(0).numpy()
|
|
return example
|
|
|
|
# load a demo dataset and read audio files
|
|
dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
|
|
dataset = dataset.map(map_to_array)
|
|
|
|
model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")
|
|
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
|
|
|
|
# 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
|
|
predicted_ids = torch.argmax(logits, dim=-1)
|
|
labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
|
|
```
|
|
|
|
## Eval results
|
|
|
|
The evaluation metric is accuracy.
|
|
|
|
| | **s3prl** | **transformers** |
|
|
|--------|-----------|------------------|
|
|
|**test**| `0.9623` | `0.9643` |
|
|
|
|
### 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}
|
|
}
|
|
``` |