2.39 kB
--- | |
language: fr | |
datasets: | |
- common_voice | |
tags: | |
- audio | |
- automatic-speech-recognition | |
- speech | |
- xlsr-fine-tuning-week | |
license: apache-2.0 | |
model-index: | |
- name: wav2vec2-large-xlsr-53-French by Ilyes Rebai | |
results: | |
- task: | |
name: Speech Recognition | |
type: automatic-speech-recognition | |
dataset: | |
name: Common Voice fr | |
type: common_voice | |
args: fr | |
metrics: | |
- name: Test WER | |
type: wer | |
value: 12.82 | |
--- | |
## Evaluation on Common Voice FR Test | |
The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2 | |
```python | |
import torch | |
import torchaudio | |
from datasets import load_dataset, load_metric | |
from transformers import ( | |
Wav2Vec2ForCTC, | |
Wav2Vec2Processor, | |
) | |
import re | |
model_name = "Ilyes/wav2vec2-large-xlsr-53-french" | |
device = "cpu" # "cuda" | |
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) | |
processor = Wav2Vec2Processor.from_pretrained(model_name) | |
ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr") | |
chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\.\,\;\:\*\—\–\─\―\_\/\:\ː\;\,\=\«\»\→]' | |
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"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") | |
return batch | |
resampler = torchaudio.transforms.Resample(48_000, 16_000) | |
ds = ds.map(map_to_array) | |
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"] | |
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"])) | |
``` | |
## Results | |
WER=12.82% | |
CER=4.40% | |