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--- |
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language: ru |
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tags: |
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- audio-classification |
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- audio |
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- emotion |
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- emotion-recognition |
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- emotion-classification |
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- speech |
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license: mit |
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datasets: |
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- Aniemore/resd |
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model-index: |
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- name: XLS-R Wav2Vec2 For Russian Speech Emotion Classification by Nikita Davidchuk |
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results: |
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- task: |
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name: Audio Emotion Recognition |
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type: audio-emotion-recognition |
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dataset: |
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name: Russian Emotional Speech Dialogs |
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type: Aniemore/resd |
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args: ru |
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metrics: |
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- name: accuracy |
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type: accuracy |
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value: 72% |
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--- |
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# Prepare and importing |
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|
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```python |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torchaudio |
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from transformers import AutoConfig, AutoModel, Wav2Vec2FeatureExtractor |
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import librosa |
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import numpy as np |
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def speech_file_to_array_fn(path, sampling_rate): |
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speech_array, _sampling_rate = torchaudio.load(path) |
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resampler = torchaudio.transforms.Resample(_sampling_rate) |
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speech = resampler(speech_array).squeeze().numpy() |
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return speech |
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def predict(path, sampling_rate): |
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speech = speech_file_to_array_fn(path, sampling_rate) |
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inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) |
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inputs = {key: inputs[key].to(device) for key in inputs} |
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with torch.no_grad(): |
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logits = model_(**inputs).logits |
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scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] |
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outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] |
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return outputs |
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``` |
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# Evoking: |
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```python |
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TRUST = True |
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config = AutoConfig.from_pretrained('Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition', trust_remote_code=TRUST) |
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model_ = AutoModel.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition", trust_remote_code=TRUST) |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_.to(device) |
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``` |
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# Use case |
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```python |
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result = predict("/path/to/russian_audio_speech.wav", 16000) |
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print(result) |
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``` |
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```python |
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# outputs |
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[{'Emotion': 'anger', 'Score': '0.0%'}, |
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{'Emotion': 'disgust', 'Score': '100.0%'}, |
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{'Emotion': 'enthusiasm', 'Score': '0.0%'}, |
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{'Emotion': 'fear', 'Score': '0.0%'}, |
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{'Emotion': 'happiness', 'Score': '0.0%'}, |
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{'Emotion': 'neutral', 'Score': '0.0%'}, |
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{'Emotion': 'sadness', 'Score': '0.0%'}] |
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``` |
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# Results |
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| | precision | recall | f1-score | support | |
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|--------------|-----------|--------|----------|---------| |
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| anger | 0.97 | 0.86 | 0.92 | 44 | |
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| disgust | 0.71 | 0.78 | 0.74 | 37 | |
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| enthusiasm | 0.51 | 0.80 | 0.62 | 40 | |
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| fear | 0.80 | 0.62 | 0.70 | 45 | |
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| happiness | 0.66 | 0.70 | 0.68 | 44 | |
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| neutral | 0.81 | 0.66 | 0.72 | 38 | |
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| sadness | 0.79 | 0.59 | 0.68 | 32 | |
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| accuracy | | | 0.72 | 280 | |
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| macro avg | 0.75 | 0.72 | 0.72 | 280 | |
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| weighted avg | 0.75 | 0.72 | 0.73 | 280 | |
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# Citations |
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``` |
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@misc{Aniemore, |
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author = {Артем Аментес, Илья Лубенец, Никита Давидчук}, |
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title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека}, |
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year = {2022}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Hub}, |
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howpublished = {\url{https://huggingface.com/aniemore/Aniemore}}, |
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email = {hello@socialcode.ru} |
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} |
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``` |
