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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 |
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license: apache-2.0 |
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model-index: |
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- name: wav2vec2-large-xlsr-53-French_punctuation 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 and CER on text and puctuation prediction |
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types: [wer, cer] |
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values: [19.47%, 6.66%] |
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- name: Test WER and CER on text without punctuation |
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types: [wer, cer] |
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values: [17.88%, 6.37%] |
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--- |
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## Evaluation on Common Voice FR Test |
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```python |
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import re |
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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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model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation" |
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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") |
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chars_to_ignore_regex = '[\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\ǃ\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\;\:\*\—\–\─\―\_\/\:\ː\;\=\«\»\→]' |
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def normalize_text(text): |
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text = text.lower().strip() |
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text = re.sub('œ', 'oe', text) |
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text = re.sub('æ', 'ae', text) |
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text = re.sub("’|´|′|ʼ|‘|ʻ|`", "'", text) |
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text = re.sub("'+ ", " ", text) |
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text = re.sub(" '+", " ", text) |
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text = re.sub("'$", " ", text) |
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text = re.sub("' ", " ", text) |
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text = re.sub("−|‐", "-", text) |
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text = re.sub(" -", "", text) |
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text = re.sub("- ", "", text) |
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text = re.sub(chars_to_ignore_regex, '', text) |
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return text |
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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"] = normalize_text(batch["sentence"]) |
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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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# remove duplicates |
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batch["target"] = re.sub('\.+', '.', batch["target"]) |
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batch["target"] = re.sub('\?+', '?', batch["target"]) |
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batch["target"] = re.sub('!+', '!', batch["target"]) |
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batch["target"] = re.sub(',+', ',', batch["target"]) |
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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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## Some results |
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| Reference | Prediction | |
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| ------------- | ------------- | |
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| il vécut à new york et y enseigna une grande partie de sa vie. | il a vécu à new york et y enseigna une grande partie de sa vie. | |
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| au classement par nations, l'allemagne est la tenante du titre. | au classement der nation l'allemagne est la tenante du titre. | |
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| voici un petit calcul pour fixer les idées. | voici un petit calcul pour fixer les idées. | |
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| oh! tu dois être beau avec | oh! tu dois être beau avec. | |
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| babochet vous le voulez? | baboche, vous le voulez? | |
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| la commission est, par conséquent, défavorable à cet amendement. | la commission est, par conséquent, défavorable à cet amendement. | |
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All the references and predictions of the test corpus are already available in this repository. |
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## Results |
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text + punctuation |
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WER=21.47% CER=7.21% |
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text (without punctuation) |
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WER=19.71% CER=6.91% |
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