Update README.md

README.md

@@ -10,6 +10,70 @@ tags:
 license: gpl-3.0
 ---
 
+# Prepare and importing
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from transformers import Wav2Vec2Config, AutoModelForAudioClassification, 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 = Wav2Vec2Config.from_pretrained('Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition', trust_remote_code=TRUST)
+model_ = AutoModelForAudioClassification.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition", trust_remote_code=TRUST, config=config)
+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 |