import json
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import torch
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import logging
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import torch.distributed as dist
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from funasr.register import tables
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@tables.register("index_ds_classes", "IndexDSJsonl")
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class IndexDSJsonl(torch.utils.data.Dataset):
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def __init__(self, path):
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super().__init__()
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contents = []
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with open(path, encoding='utf-8') as fin:
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for line in fin:
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data = json.loads(line.strip())
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if "text" in data: # for sft
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self.contents.append(data['text'])
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if "source" in data: # for speech lab pretrain
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prompt = data["prompt"]
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source = data["source"]
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target = data["target"]
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source_len = data["source_len"]
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target_len = data["target_len"]
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contents.append({"source": source,
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"prompt": prompt,
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"target": target,
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"source_len": source_len,
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"target_len": target_len,
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}
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)
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self.contents = []
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total_num = len(contents)
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try:
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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except:
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rank = 0
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world_size = 1
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logging.warning("distributed is not initialized, only single shard")
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num_per_rank = total_num // world_size
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# rank = 0
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# import ipdb; ipdb.set_trace()
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self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]
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logging.info("in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(rank, len(self.contents), len(contents)))
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def __len__(self):
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return len(self.contents)
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def __getitem__(self, index):
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try:
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data = self.contents[index]
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except:
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print(index)
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return data
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def get_source_len(self, data_dict):
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return data_dict["source_len"]
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def get_target_len(self, data_dict):
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return data_dict["target_len"] if "target_len" in data_dict else 0
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