| from s3prl.downstream.runner import Runner | |
| from typing import Dict | |
| import torch | |
| import os | |
| class PreTrainedModel(Runner): | |
| def __init__(self, path=""): | |
| """ | |
| Initialize downstream model. | |
| """ | |
| ckp_file = os.path.join(path, "model.ckpt") | |
| ckp = torch.load(ckp_file, map_location='cpu') | |
| ckp["Args"].init_ckpt = ckp_file | |
| ckp["Args"].mode = "inference" | |
| ckp["Args"].device = "cpu" | |
| ckp["Config"]["downstream_expert"]["datarc"]["dict_path"] = os.path.join(path,'char.dict') | |
| Runner.__init__(self, ckp["Args"], ckp["Config"]) | |
| def __call__(self, inputs)-> Dict[str, str]: | |
| """ | |
| Args: | |
| inputs (:obj:`np.array`): | |
| The raw waveform of audio received. By default at 16KHz. | |
| Return: | |
| A :obj:`dict`:. The object return should be liked {"text": "XXX"} containing | |
| the detected text from the input audio. | |
| """ | |
| for entry in self.all_entries: | |
| entry.model.eval() | |
| inputs = [torch.FloatTensor(inputs)] | |
| with torch.no_grad(): | |
| features = self.upstream.model(inputs) | |
| features = self.featurizer.model(inputs, features) | |
| preds = self.downstream.model.inference(features, []) | |
| return {"text": preds[0]} | |