This code snippet shows how to evaluate patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram on LibriSpeech's "clean" and "other" test data.

python

from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torch
from jiwer import wer

model_id = "patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram"

librispeech_eval = load_dataset("librispeech_asr", "other", split="test")

model = AutoModelForCTC.from_pretrained(model_id).to("cuda")
processor = AutoProcessor.from_pretrained(model_id)

def map_to_pred(batch):
    inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt")

    inputs = {k: v.to("cuda") for k,v in inputs.items()}

    with torch.no_grad():
        logits = model(**inputs).logits

    transcription = processor.batch_decode(logits.cpu().numpy()).text[0]
    batch["transcription"] = transcription
    return batch

result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])

print(wer(result["text"], result["transcription"]))

Result (WER):

"clean"	"other"
1.94	3.54