Update README.md

README.md

@@ -0,0 +1,212 @@
+---
+language: multilingual
+tags:
+- LID
+- spoken language recognition
+license: Apache 2.0
+datasets:
+- VoxLingua107
+metrics:
+- ER
+inference: false
+---
+
+# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
+
+## Model description
+
+This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
+The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.
+
+The model can classify a speech utterance according to the language spoken.
+It knows about 107 different languages (
+Abkhazian, 
+Afrikaans, 
+Amharic, 
+Arabic, 
+Assamese, 
+Azerbaijani, 
+Bashkir, 
+Belarusian, 
+Bulgarian, 
+Bengali, 
+Tibetan, 
+Breton, 
+Bosnian, 
+Catalan, 
+Cebuano, 
+Czech, 
+Welsh, 
+Danish, 
+German, 
+Greek, 
+English, 
+Esperanto, 
+Spanish, 
+Estonian, 
+Basque, 
+Persian, 
+Finnish, 
+Faroese, 
+French, 
+Galician, 
+Guarani, 
+Gujarati, 
+Manx, 
+Hausa, 
+Hawaiian, 
+Hindi, 
+Croatian, 
+Haitian, 
+Hungarian, 
+Armenian, 
+Interlingua, 
+Indonesian, 
+Icelandic, 
+Italian, 
+Hebrew, 
+Japanese, 
+Javanese, 
+Georgian, 
+Kazakh, 
+Central Khmer, 
+Kannada, 
+Korean, 
+Latin, 
+Luxembourgish, 
+Lingala, 
+Lao, 
+Lithuanian, 
+Latvian, 
+Malagasy, 
+Maori, 
+Macedonian, 
+Malayalam, 
+Mongolian, 
+Marathi, 
+Malay, 
+Maltese, 
+Burmese, 
+Nepali, 
+Dutch, 
+Norwegian Nynorsk, 
+Norwegian, 
+Occitan, 
+Panjabi, 
+Polish, 
+Pushto, 
+Portuguese, 
+Romanian, 
+Russian, 
+Sanskrit, 
+Scots, 
+Sindhi, 
+Sinhala, 
+Slovak, 
+Slovenian, 
+Shona, 
+Somali, 
+Albanian, 
+Serbian, 
+Sundanese, 
+Swedish, 
+Swahili, 
+Tamil, 
+Telugu, 
+Tajik, 
+Thai, 
+Turkmen, 
+Tagalog, 
+Turkish, 
+Tatar, 
+Ukrainian, 
+Urdu, 
+Uzbek, 
+Vietnamese, 
+Waray, 
+Yiddish, 
+Yoruba, 
+Mandarin Chinese).
+
+## Intended uses & limitations
+
+The model has two uses:
+
+  - use 'as is' for spoken language recognition
+  - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data
+  
+The model is trained on the automatically collected YouTube data. For more 
+information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/).
+
+
+#### How to use
+
+```python
+import torchaudio
+from speechbrain.pretrained import EncoderClassifier
+EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
+# Download Thai language sample from Omniglot
+signal, fs = torchaudio.load("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
+# Resample to 16000 and convert to mono by taking only the left channel
+signal_resampled = torchaudio.transforms.Resample(fs, 16000)(signal)[0]
+prediction =  language_id.classify_batch(signal_resampled)
+print(prediction)
+  (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
+           0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
+           0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256,
+           0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944,
+           0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950,
+           0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777,
+           0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193,
+           0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364,
+           0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017,
+           0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464,
+           0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838,
+           0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th'])
+# The scores in the prediction[0] tensor can be interpreted as cosine scores between
+# the languages and the given utterance (i.e., the larger the better)
+# The identified language ISO code is given in prediction[3]
+print(prediction[3])
+  ['th']
+```
+
+#### Limitations and bias
+
+Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are:
+
+ - Probably it's accuracy on smaller languages  is quite limited
+ - Probably it works much worse on female speech than male speech (because of YouTube data includes much more male speech)
+ - Based on subjective experiments, it doesn't work well for speech with a foreign accent
+ - Probably it doesn't work well on children's speech
+
+
+## Training data
+
+The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/).
+
+VoxLingua107 is a speech dataset for training spoken language identification models. 
+The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
+
+VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. 
+The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.
+
+## Training procedure
+
+We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model.
+Training recipe will be published soon.
+
+## Evaluation results
+
+Error rate: 6% on the development dataset
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{valk2021slt,
+  title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
+  author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
+  booktitle={Proc. IEEE SLT Workshop},
+  year={2021},
+}
+```