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

diff

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