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Dataset: Common Voice zh-HK
CER: 17.810267
evaluation code
```python3
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import argparse
lang_id = "zh-HK"
model_id = "./wav2vec2-large-xlsr-cantonese"
parser = argparse.ArgumentParser(description='hanles checkpoint loading')
parser.add_argument('--checkpoint', type=str, default=None)
args = parser.parse_args()
model_path = model_id
if args.checkpoint is not None:
model_path += "/checkpoint-" + args.checkpoint
chars_to_ignore_regex = '[\,\?\.\!\-\;\:"\“\%\‘\”\�\．\⋯\！\－\：\–\。\》\,\）\,\？\；\～\~\…\︰\，\（\」\‧\《\﹔\、\—\／\,\「\﹖\·\']'
test_dataset = load_dataset("common_voice", f"{lang_id}", split="test")
cer = load_metric("./cer")
processor = Wav2Vec2Processor.from_pretrained(f"{model_id}")
model = Wav2Vec2ForCTC.from_pretrained(f"{model_path}")
model.to("cuda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=16)
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
Character Error Rate implementation
```python3
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class CER(datasets.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Value("string", id="sequence"),
}
),
codebase_urls=["https://github.com/jitsi/jiwer/"],
reference_urls=[
"https://en.wikipedia.org/wiki/Word_error_rate",
],
)
def _compute(self, predictions, references):
preds = [char for seq in predictions for char in list(seq)]
refs = [char for seq in references for char in list(seq)]
return wer(refs, preds)
```
will post the training code later.
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