---
language: es
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
---

# Wav2Vec2-Large-XLSR-53-Spanish-With-LM

This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co./jonatasgrosman/wav2vec2-large-xlsr-53-spanish)
that has language model support. 

This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) integration 
with Transformers led by [this PR](https://github.com/huggingface/transformers/pull/14339). The PR explains in-detail how the 
integration works. 

In a nutshell: This PR adds a new Wav2Vec2WithLMProcessor class as drop-in replacement for Wav2Vec2Processor.

The only change from the existing ASR pipeline will be:

```diff
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset

ds = load_dataset("common_voice", "es", split="test", streaming=True)

sample = next(iter(ds))

model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2Processor.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")

input_values = processor(sample["audio"]["array"], return_tensors="pt").input_values

logits = model(input_values).logits
prediction_ids = torch.argmax(logits, dim=-1)

transcription = processor.batch_decode(prediction_ids)

print(transcription)
```


| Model | WER | CER |
| ------------- | ------------- | ------------- |
| jonatasgrosman/wav2vec2-large-xlsr-53-spanish | **8.81%** | **2.70%** |
| pcuenq/wav2vec2-large-xlsr-53-es | 10.55% | 3.20% |
| facebook/wav2vec2-large-xlsr-53-spanish | 16.99% | 5.40% |
| mrm8488/wav2vec2-large-xlsr-53-spanish | 19.20% | 5.96% |