Wav2vec2 Tiny Random Robust

🚀 測試模型

本模型用於自動語音識別，使用了LibriSpeech ASR數據集進行相關處理。以下將為你介紹如何測試該模型。

🚀 快速開始

要測試此模型，請運行以下代碼：

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

📄 許可證

本項目採用Apache-2.0許可證。