pytorch wav2vec2-conformer automatic-speech-recognition en dataset:librispeech_asr arxiv:2010.05171 transformers speech audio hf-asr-leaderboard license:apache-2.0
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
---

# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings

Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech 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](https://arxiv.org/abs/2010.05171)

**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](https://arxiv.org/abs/2010.05171).

The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

# Usage

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

```python
 from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC
 from datasets import load_dataset
 import torch
 
 # load model and processor
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft")
 model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-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)
 ```