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 that has language model support.
This model card can be seen as a demo for the pyctcdecode integration with Transformers led by this PR. 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:
Changes
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm"
sample = next(iter(load_dataset("common_voice", "es", split="test", streaming=True)))
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
-prediction_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(prediction_ids)
+transcription = processor.batch_decode(logits.numpy()).text
# => 'bien y qué regalo vas a abrir primero'
Improvement
This model has been compared on 512 speech samples from the Spanish Common Voice Test set and gives a nice 20 % performance boost:
The results can be reproduced by running from this model repository:
| Model | WER | CER |
|---|---|---|
| patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm | 8.44% | 2.93% |
| jonatasgrosman/wav2vec2-large-xlsr-53-spanish | 10.20% | 3.24% |
bash run_ngram_wav2vec2.py 1 512
bash run_ngram_wav2vec2.py 0 512
with run_ngram_wav2vec2.py being
https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm/blob/main/run_ngram_wav2vec2.py