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