merge

游雁

2023-11-24 afae93b43dcd6f9061dec5934b6536981c7ef363

merge

 funasr/datasets/dataset_jsonl.py

@@ -22,14 +22,12 @@

def extract_features(data, date_type: str="sound", frontend=None):
    if date_type == "sound":
<<<<<<< HEAD

        if isinstance(data, np.ndarray):
            data = torch.from_numpy(data).to(torch.float32)
        data_len = torch.tensor([data.shape[0]]).to(torch.int32)
        feat, feats_lens = frontend(data[None, :], data_len)
=======
        feat, feats_lens = frontend(data, len(data))
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8

        feat = feat[0, :, :]
    else:
        feat, feats_lens = torch.from_numpy(data).to(torch.float32), torch.tensor([data.shape[0]]).to(torch.int32)
@@ -81,21 +79,16 @@


class AudioDataset(torch.utils.data.Dataset):
<<<<<<< HEAD
    def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=None):
=======
    def __init__(self, path, frontend=None, tokenizer=None):
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8

        super().__init__()
        self.indexed_dataset = IndexedDatasetJsonl(path)
        self.frontend = frontend.forward
        self.fs = 16000 if frontend is None else frontend.fs
        self.data_type = "sound"
        self.tokenizer = tokenizer
<<<<<<< HEAD
        self.token_id_converter = token_id_converter
=======
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8

        self.int_pad_value = -1
        self.float_pad_value = 0.0

@@ -107,24 +100,17 @@
    
    def __getitem__(self, index):
        item = self.indexed_dataset[index]
<<<<<<< HEAD
        # return item
=======
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8

        source = item["source"]
        data_src = load_audio(source, fs=self.fs)
        speech, speech_lengths = extract_features(data_src, self.data_type, self.frontend)
        target = item["target"]
<<<<<<< HEAD
        text = self.tokenizer.text2tokens(target)
        ids = self.token_id_converter.tokens2ids(text)
        ids_lengths = len(ids)
        text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
=======
        text = self.tokenizer.encode(target)
        text_lengths = len(text)
        text, text_lengths = torch.tensor(text, dtype=torch.int64), torch.tensor([text_lengths], dtype=torch.int32)
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8

        return {"speech": speech,
                "speech_lengths": speech_lengths,
                "text": text,
@@ -134,32 +120,21 @@
    
    def collator(self, samples: list=None):
        
<<<<<<< HEAD
        # return samples
        
=======
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
        outputs = {}
        for sample in samples:
            for key in sample.keys():
                if key not in outputs:
                    outputs[key] = []
                outputs[key].append(sample[key])
<<<<<<< HEAD

        for key, data_list in outputs.items():
            if data_list[0].dtype == torch.int64:
=======
		
        for key, data_list in outputs.items():
            if data_list[0].dtype.kind == "i":
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8

                pad_value = self.int_pad_value
            else:
                pad_value = self.float_pad_value
            outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
<<<<<<< HEAD
        return outputs
=======
        return samples
>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8