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import numpy as np
import pandas as pd
from main import SpeechClassifierOutput, Wav2Vec2ForSpeechClassification
from datasets import load_dataset
from transformers import AutoConfig, Wav2Vec2Processor
import torchaudio
import torch
import torch.nn.functional as F
import seaborn as sns
import matplotlib.pyplot as plt
import streamlit as st
import os
sns.set_theme(style="darkgrid", palette="pastel")
def demo_speech_file_to_array_fn(path):
speech_array, _sampling_rate = torchaudio.load(path, normalize=True)
resampler = torchaudio.transforms.Resample(_sampling_rate, 16_000)
speech = resampler(speech_array).squeeze().numpy()
return speech
def demo_predict(df_row):
path, emotion = df_row["path"], df_row["emotion"]
speech = demo_speech_file_to_array_fn(path)
features = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": round(score * 100, 3)} for i, score in enumerate(scores)]
return outputs
def cache_model():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = 'm3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition'
config = AutoConfig.from_pretrained(model_name_or_path)
processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
return config, processor, model, device
@st.cache
def load_data():
return pd.read_csv('data/test.csv', delimiter = '\t')
def bar_plot(df):
fig = plt.figure(figsize=(8, 6))
plt.title("Prediction Scores")
plt.xticks(fontsize=12)
sns.barplot(x="Score", y="Emotion", data=df)
st.pyplot(fig)
if __name__ == '__main__':
os.system('python download_dataset.py')
test = load_data()
config, processor, model, device = cache_model()
print('Model loaded')
st.title("Emotion Classifier for Greek Speech Audio Demo")
if st.button("Classify Random Audio"):
# Load demo file
idx = np.random.randint(0, len(test))
sample = test.iloc[idx]
audio_file = open(sample['path'], 'rb')
audio_bytes = audio_file.read()
st.success(f'Label: {sample["emotion"]}')
st.audio(audio_bytes, format='audio/ogg')
outputs = demo_predict(sample)
r = pd.DataFrame(outputs)
# st.dataframe(r)
bar_plot(r) |