Update README.md

README.md

@@ -1,63 +1 @@
----
-language: nl
-datasets:
-- common_voice
-tags:
-- speech
-- audio
-- automatic-speech-recognition
-license: apache-2.0
----
-
-## Evaluation on Common Voice NL Test
-
-```python
-import torchaudio
-from datasets import load_dataset, load_metric
-from transformers import (
-    Wav2Vec2ForCTC,
-    Wav2Vec2Processor,
-)
-import torch
-import re
-import sys
-
-model_name = "facebook/wav2vec2-large-xlsr-53-dutch"
-device = "cuda"
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]'  # noqa: W605
-
-model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
-processor = Wav2Vec2Processor.from_pretrained(model_name)
-
-ds = load_dataset("common_voice", "nl", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
-
-resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
-
-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)
-
-
-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"]))
-```
-
-**Result**: 21.1 %
+Copy of "facebook/wav2vec2-large-xlsr-53-dutch"