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Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings

Facebook's Wav2Vec2 Conformer (TODO-add link)

Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.

Paper: fairseq S2T: Fast Speech-to-Text Modeling with fairseq

Authors: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino

The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the official paper. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.


To transcribe audio files the model can be used as a standalone acoustic model as follows:

 from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC
 from datasets import load_dataset
 import torch
 # load model and processor
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-100h-ft")
 model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-100h-ft")
 # load dummy dataset and read soundfiles
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 # tokenize
 input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
 # retrieve logits
 logits = model(input_values).logits
 # take argmax and decode
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)
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Dataset used to train facebook/wav2vec2-conformer-rel-pos-large-100h-ft