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Upload inference.py

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  1. inference.py +39 -0
inference.py ADDED
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+ import torch
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+ import torch.nn.functional as F
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+ import torchaudio
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+ from transformers import AutoConfig, Wav2Vec2Processor
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+
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+ from Wav2Vec2ForSpeechClassification import Wav2Vec2ForSpeechClassification
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+
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+ MY_MODEL = "padmalcom/wav2vec2-large-nonverbalvocalization-classification"
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ config = AutoConfig.from_pretrained(MY_MODEL)
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+ processor = Wav2Vec2Processor.from_pretrained(MY_MODEL)
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+ sampling_rate = processor.feature_extractor.sampling_rate
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+ model = Wav2Vec2ForSpeechClassification.from_pretrained(MY_MODEL).to(device)
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+
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+ def speech_file_to_array_fn(path, sampling_rate):
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+ speech_array, _sampling_rate = torchaudio.load(path)
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+ resampler = torchaudio.transforms.Resample(_sampling_rate)
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+ speech = resampler(speech_array).squeeze().numpy()
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+ return speech
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+
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+
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+ def predict(path, sampling_rate):
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+ speech = speech_file_to_array_fn(path, sampling_rate)
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+ features = processor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
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+
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+ input_values = features.input_values.to(device)
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+ attention_mask = features.attention_mask.to(device)
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+
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+ with torch.no_grad():
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+ logits = model(input_values, attention_mask=attention_mask).logits
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+
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+ scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
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+ outputs = [{"Vocalization": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
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+ return outputs
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+
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+ res = predict("test.wav", 16000)
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+ max = max(res, key=lambda x: x['Score'])
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+ print("Expected lip popping:", max)