Ilyes/wav2vec2-large-xlsr-53-french_punctuation

Evaluation on Common Voice FR Test

import re
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
    Wav2Vec2ForCTC,
    Wav2Vec2Processor,
)



model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation"


model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
processor = Wav2Vec2Processor.from_pretrained(model_name)


ds = load_dataset("common_voice", "fr", split="test")


chars_to_ignore_regex = '[\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\ǃ\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\;\:\*\—\–\─\―\_\/\:\ː\;\=\«\»\→]'
def normalize_text(text):
    text = text.lower().strip()
    text = re.sub('œ', 'oe', text)
    text = re.sub('æ', 'ae', text)
    text = re.sub("’|´|′|ʼ|‘|ʻ|`", "'", text)
    text = re.sub("'+ ", " ", text)
    text = re.sub(" '+", " ", text)
    text = re.sub("'$", " ", text)
    text = re.sub("' ", " ", text)
    text = re.sub("−|‐", "-", text)
    text = re.sub(" -", "", text)
    text = re.sub("- ", "", text)
    text = re.sub(chars_to_ignore_regex, '', text)
    return text



def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
    batch["sampling_rate"] = resampler.new_freq
    batch["sentence"] = normalize_text(batch["sentence"])
    return batch

ds = ds.map(map_to_array)

resampler = torchaudio.transforms.Resample(48_000, 16_000)
def map_to_pred(batch):
    features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
    input_values = features.input_values.to(device)
    attention_mask = features.attention_mask.to(device)
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["predicted"] = processor.batch_decode(pred_ids)
    batch["target"] = batch["sentence"]
    # remove duplicates
    batch["target"] = re.sub('\.+', '.', batch["target"])
    batch["target"] = re.sub('\?+', '?', batch["target"])
    batch["target"] = re.sub('!+', '!', batch["target"])
    batch["target"] = re.sub(',+', ',', batch["target"])
    return batch

result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))

Some results

Reference	Prediction
il vécut à new york et y enseigna une grande partie de sa vie.	il a vécu à new york et y enseigna une grande partie de sa vie.
au classement par nations, l'allemagne est la tenante du titre.	au classement der nation l'allemagne est la tenante du titre.
voici un petit calcul pour fixer les idées.	voici un petit calcul pour fixer les idées.
oh! tu dois être beau avec	oh! tu dois être beau avec.
babochet vous le voulez?	baboche, vous le voulez?
la commission est, par conséquent, défavorable à cet amendement.	la commission est, par conséquent, défavorable à cet amendement.

All the references and predictions of the test corpus are already available in this repository.

Results

text + punctuation

WER=21.47% CER=7.21%

text (without punctuation)

WER=19.71% CER=6.91%