## 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%

{"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-French by Ilyes Rebai", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fr", "type": "common_voice", "args": "fr"}, "metrics": [{"type": "wer", "value": 12.82, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week fr dataset:common_voice license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: URL




## Results

WER=12.82%

CER=4.40%

## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: URL ## Results

WER=12.82%

CER=4.40%
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: URL ## Results

WER=12.82%

CER=4.40%
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: URL## Results

WER=12.82%

CER=4.40%