Create README.md

README.md

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+## Test model
+
+To test this model run the following code:
+
+```python
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC
+import torchaudio
+import torch
+
+ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+
+model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2_tiny_random")
+
+def load_audio(batch):
+    batch["samples"], _ = torchaudio.load(batch["file"])
+    return batch
+    
+ds = ds.map(load_audio)
+
+input_values = torch.nn.utils.rnn.pad_sequence([torch.tensor(x[0]) for x in ds["samples"][:10]], batch_first=True)
+
+# forward
+logits = model(input_values).logits
+pred_ids = torch.argmax(logits, dim=-1)
+
+# dummy loss
+dummy_labels = pred_ids.clone()
+dummy_labels[dummy_labels == model.config.pad_token_id] = 1  # can't have CTC blank token in label
+dummy_labels = dummy_labels[:, -(dummy_labels.shape[1] // 4):] # make sure labels are shorter to avoid "inf" loss (can still happen though...)
+loss = model(input_values, labels=dummy_labels).loss
+```