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% | |