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inference.py

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+import torch
+import torch.nn.functional as F
+import torchaudio
+from transformers import AutoConfig, Wav2Vec2Processor
+
+from Wav2Vec2ForSpeechClassification import Wav2Vec2ForSpeechClassification
+
+MY_MODEL = "myrun3"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+config = AutoConfig.from_pretrained(MY_MODEL)
+processor = Wav2Vec2Processor.from_pretrained(MY_MODEL)
+sampling_rate = processor.feature_extractor.sampling_rate
+model = Wav2Vec2ForSpeechClassification.from_pretrained(MY_MODEL).to(device)
+
+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)
+	features = processor(speech, sampling_rate=sampling_rate, 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": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
+	return outputs
+
+res = predict("test.wav", 16000)
+max = max(res, key=lambda x: x['Score'])
+print("Expected anger:", max)