| --- |
| language: en |
| datasets: |
| - librispeech_asr |
| tags: |
| - automatic-speech-recognition |
| license: apache-2.0 |
| --- |
| |
| ## 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_robust") |
| |
| 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 |
| ``` |
|
|
