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| from speechbrain.pretrained import EncoderClassifier | |
| class CustomEncoderWav2vec2Classifier(EncoderClassifier): | |
| def compute_forward(self, batch, stage): | |
| wavs, wav_lens = batch.sig | |
| feats = self.mods.compute_features(wavs) | |
| if self.mods.normalize: | |
| feats = self.mods.normalize(feats, wav_lens) | |
| x = self.mods.encoder(feats) | |
| outputs = self.mods.classifier(x) | |
| return outputs | |
| def classify_file(self, path): | |
| signal = self.load_audio(path) | |
| batch = self.make_batch(signal) | |
| probs = self.forward(batch) | |
| score, index = probs.max(1) | |
| label = self.hparams.label_encoder.decode(index) | |
| return probs, score.item(), index.item(), label | |