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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: IEMOCAP clip "happy" |
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src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav |
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- example_title: IEMOCAP clip "neutral" |
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src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav |
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--- |
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# Wav2Vec2-Base for Emotion Recognition |
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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 Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion). |
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The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), 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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Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset |
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[IEMOCAP](https://sail.usc.edu/iemocap/) is adopted, and we follow the conventional evaluation protocol: |
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we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and |
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cross-validate on five folds of the standard splits. |
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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#er-emotion-recognition). |
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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", "er", split="session1") |
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classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") |
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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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import librosa |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForSequenceClassification, 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", "er", split="session1") |
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dataset = dataset.map(map_to_array) |
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model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er") |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er") |
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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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|**session1**| `0.6343` | `0.6258` | |
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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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``` |
