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@@ -145,11 +145,9 @@ information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/)
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  import torchaudio
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  from speechbrain.pretrained import EncoderClassifier
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  language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
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- # Download Thai language sample from Omniglot
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- signal, fs = torchaudio.load("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
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- # Resample to 16000 and convert to mono by taking only the left channel
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- signal_resampled = torchaudio.transforms.Resample(fs, 16000)(signal)[0]
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- prediction = language_id.classify_batch(signal_resampled)
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  print(prediction)
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  (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
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  0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
@@ -210,7 +208,7 @@ Error rate: 7% on the development dataset
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  ```bibtex
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  @inproceedings{valk2021slt,
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  title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
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- author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
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  booktitle={Proc. IEEE SLT Workshop},
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  year={2021},
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  }
 
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  import torchaudio
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  from speechbrain.pretrained import EncoderClassifier
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  language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
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+ # Download Thai language sample from Omniglot and cvert to suitable form
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+ signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
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+ prediction = language_id.classify_batch(signal)
 
 
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  print(prediction)
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  (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
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  0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
 
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  ```bibtex
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  @inproceedings{valk2021slt,
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  title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
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+ author={J{\\"o}rgen Valk and Tanel Alum{\\"a}e},
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  booktitle={Proc. IEEE SLT Workshop},
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  year={2021},
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  }