Edit model card

Evaluation

The model can be evaluated as follows on the German test data of Common Voice.

import torch
from transformers import AutoModelForCTC, AutoProcessor
from unidecode import unidecode
import re
from datasets import load_dataset, load_metric
import datasets

counter = 0
wer_counter = 0
cer_counter = 0
device = "cuda" if torch.cuda.is_available() else "cpu"


special_chars = [["Ä"," AE "], ["Ö"," OE "], ["Ü"," UE "], ["ä"," ae "], ["ö"," oe "], ["ü"," ue "]]
def clean_text(sentence):
    for special in special_chars:
        sentence = sentence.replace(special[0], special[1])

    sentence = unidecode(sentence)

    for special in special_chars:
        sentence = sentence.replace(special[1], special[0])

    sentence = re.sub("[^a-zA-Z0-9öäüÖÄÜ ,.!?]", " ", sentence)

    return sentence

def main(model_id):
    print("load model")
    model = AutoModelForCTC.from_pretrained(model_id).to(device)
    print("load processor")
    processor = AutoProcessor.from_pretrained(processor_id)

    print("load metrics")
    wer = load_metric("wer")
    cer = load_metric("cer")

    ds = load_dataset("mozilla-foundation/common_voice_8_0","de")
    ds = ds["test"]

    ds = ds.cast_column(
        "audio", datasets.features.Audio(sampling_rate=16_000)
    )

    def calculate_metrics(batch):
        global counter, wer_counter, cer_counter
        resampled_audio = batch["audio"]["array"]

        input_values = processor(resampled_audio, return_tensors="pt", sampling_rate=16_000).input_values

        with torch.no_grad():
            logits = model(input_values.to(device)).logits.cpu().numpy()[0]


        decoded = processor.decode(logits)
        pred = decoded.text.lower()

        ref = clean_text(batch["sentence"]).lower()

        wer_result = wer.compute(predictions=[pred], references=[ref])
        cer_result = cer.compute(predictions=[pred], references=[ref])

        counter += 1
        wer_counter += wer_result
        cer_counter += cer_result

        if counter % 100 == True:
            print(f"WER: {(wer_counter/counter)*100} | CER: {(cer_counter/counter)*100}")

        return batch


    ds.map(calculate_metrics, remove_columns=ds.column_names)
    print(f"WER: {(wer_counter/counter)*100} | CER: {(cer_counter/counter)*100}")

model_id = "flozi00/wav2vec2-xls-r-1b-5gram-german"
main(model_id)
Downloads last month
74
Hosted inference API
or or
This model can be loaded on the Inference API on-demand.

Dataset used to train aware-ai/wav2vec2-xls-r-1b-5gram-german

Evaluation results