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--- |
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language: fr |
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datasets: |
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- common_voice |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: wav2vec2-large-xlsr-53-French by Ilyes Rebai |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice |
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args: fr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 20.89% |
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--- |
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## Evaluation on Common Voice FR Test |
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The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2 |
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```python |
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import torch |
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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 re |
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model_name = "Ilyes/wav2vec2-large-xlsr-53-french" |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda') |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr") |
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chars_to_ignore_regex = '[\,\?\.\!\;\:\"\β\%\β\β\οΏ½\β\β\β\β\β\β¦\Β·\!\Η\?\Β«\βΉ\Β»\βΊβ\β\\ΚΏ\ΚΎ\β\β\\|\.\,\;\:\*\β\β\β\β\_\/\:\Λ\;\,\=\Β«\Β»\β]' |
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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("β", "'") |
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return batch |
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ds = ds.map(map_to_array) |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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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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## Results |
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# v0.1 |
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WER=18.29% |
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SER=71.44% |
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# v1.0 |
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WER=15.97% |
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CER=5.51% |
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# v2.0 |
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WER=14.71% |
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CER=5.06% |
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