Prepare and importing
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:
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
result = predict("/path/to/russian_audio_speech.wav", 16000)
print(result)
# 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 = {[email protected]}
}
- Downloads last month
- 1,880
Inference API (serverless) does not yet support model repos that contain custom code.
Dataset used to train Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition
Space using Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition 1
Evaluation results
- accuracy on Russian Emotional Speech Dialogsself-reported72%