speechbrain.lobes.models.huggingface_transformers.nllb module

This lobe enables the integration of huggingface pretrained NLLB models. Reference: https://arxiv.org/abs/2207.04672

Transformer from HuggingFace needs to be installed: https://huggingface.co/transformers/installation.html

Authors

  • Ha Nguyen 2023

Summary

Classes:

NLLB

This lobe enables the integration of HuggingFace and SpeechBrain pretrained NLLB models.

Reference

class speechbrain.lobes.models.huggingface_transformers.nllb.NLLB(source, save_path, freeze=True, target_lang='fra_Latn', decoder_only=True, share_input_output_embed=True)[source]

Bases: mBART

This lobe enables the integration of HuggingFace and SpeechBrain pretrained NLLB models.

Source paper NLLB: https://arxiv.org/abs/2207.04672 Transformer from HuggingFace needs to be installed: https://huggingface.co/transformers/installation.html

The model is normally used as a text decoder of seq2seq models. It will download automatically the model from HuggingFace or use a local path.

For now, HuggingFace’s NLLB model can be loaded using the exact code for mBART model. For this reason, NLLB can be fine inheriting the mBART class.

Parameters:

  • source (str) – HuggingFace hub name: e.g “facebook/nllb-200-1.3B”

  • save_path (str) – Path (dir) of the downloaded model.

  • freeze (bool (default: True)) – If True, the model is frozen. If False, the model will be trained alongside with the rest of the pipeline.

  • target_lang (str (default: fra_Latn (a.k.a French)) – The target language code according to NLLB model.

  • decoder_only (bool (default: True)) – If True, only take the decoder part (and/or the lm_head) of the model. This is useful in case one wants to couple a pre-trained speech encoder (e.g. wav2vec) with a text-based pre-trained decoder (e.g. mBART, NLLB).

  • share_input_output_embed (bool (default: True)) – If True, use the embedded layer as the lm_head.

Example

>>> import torch
>>> src = torch.rand([10, 1, 1024])
>>> tgt = torch.LongTensor([[256057,    313,     25,    525,    773,  21525,   4004,      2]])
>>> model_hub = "facebook/nllb-200-distilled-600M"
>>> save_path = "savedir"
>>> model = NLLB(model_hub, save_path)
>>> outputs = model(src, tgt)
training: bool