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README.md

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+---
+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
+      args: fr
+    metrics:
+       - name: Test WER
+         type: wer
+         value:  20.89%
+---
+## Evaluation on Common Voice FR Test
+```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"
+
+
+
+model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
+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
+    
+ds = ds.map(map_to_array)
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+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"]))
+```
+
+## Training
+
+6% of the Common Voice `train`, `validation` datasets were used for training.
+
+## Testing
+
+All the Common Voice `Test` dataset (15763 files) were used for testing.
+Results:
+WER=20.89%
+SER=77.56%
+