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--- |
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language: nl |
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datasets: |
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- common_voice |
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tags: |
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- speech |
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- audio |
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- automatic-speech-recognition |
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license: apache-2.0 |
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--- |
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## Evaluation on Common Voice PL Test |
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```python |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import ( |
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Wav2Vec2ForCTC, |
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Wav2Vec2Processor, |
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) |
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import torch |
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import re |
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import sys |
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model_name = "facebook/wav2vec2-large-xlsr-53-polish" |
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device = "cuda" |
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]' # noqa: W605 |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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ds = load_dataset("common_voice", "pl", split="test", data_dir="./cv-corpus-6.1-2020-12-11") |
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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
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def map_to_array(batch): |
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speech, _ = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() |
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batch["sampling_rate"] = resampler.new_freq |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
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return batch |
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ds = ds.map(map_to_array) |
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def map_to_pred(batch): |
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features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = processor.batch_decode(pred_ids) |
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batch["target"] = batch["sentence"] |
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return batch |
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result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) |
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wer = load_metric("wer") |
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print(wer.compute(predictions=result["predicted"], references=result["target"])) |
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``` |
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**Result**: 24.6 % |
