"./tests/integration_tests/data/audio_files/audio_2.wav"], target_lang = "eng_Latn") predict([ "./tests/integration_tests/data/audio_files/audio_1.wav", load( "./tests/integration_tests/data/audio_files/audio_1.wav")Īssert sr = 16000, "Sample rate should be 16kHz" # passing loaded audio files s2t_model. speech import SpeechToTextModelPipeline s2t_model = SpeechToTextModelPipeline( encoder = "sonar_speech_encoder_eng", SONAR depends mainly on Fairseq2 and can be installed using (tested with python=3.8)įrom sonar. If you want to install SONAR manually, you can install it localy. Note that there is another sonar package on pip that IS NOT this project, make sure to use sonar-space in your dependencies. You can install SONAR with pip install sonar-space. The full list of supported languages (along with download links) can be found here below. SONAR stands for Sentence-level multim Odal and la Nguage- Agnostic Representations We also provide a single text decoder, which allows us to perform text-to-text and speech-to-text machine translation, including for zero-shot language and modality combinations. Speech segments can be embedded in the same SONAR embedding space using language-specific speech encoders trained in a teacher-student setting on speech transcription data. It substantially outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks. We introduce SONAR, a new multilingual and multimodal fixed-size sentence embedding space, with a full suite of speech and text encoders and decoders.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |