import os
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import json
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import torch
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import logging
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import librosa
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import random
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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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@tables.register("index_ds_classes", "IndexDSJsonlRankFull")
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@tables.register("index_ds_classes", "IndexDSJsonlRankSplit")
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class IndexDSJsonlRankFull(torch.utils.data.Dataset):
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def __init__(self, path: str, **kwargs):
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super().__init__()
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self.max_source_length = kwargs.get("max_source_length", 2048)
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self.min_source_length = kwargs.get("min_source_length", 0)
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self.max_target_length = kwargs.get("max_target_length", 2048)
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self.min_target_length = kwargs.get("min_target_length", 0)
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self.max_token_length = kwargs.get("max_token_length", 2200)
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is_training = kwargs.get("is_training", True)
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if not (path.endswith(".jsonl") or path.endswith(".json")):
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# jsonl list file
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data_split_num = kwargs.get("data_split_num", 1)
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data_split_i = kwargs.get("data_split_i", 0)
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if not is_training:
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data_split_num = 1
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data_split_i = 0
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with open(path, encoding="utf-8") as fin:
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file_list_all = fin.readlines()
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num_per_slice = (len(file_list_all) - 1) // data_split_num + 1
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file_list = file_list_all[
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data_split_i * num_per_slice : (data_split_i + 1) * num_per_slice
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]
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logging.info(
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f"is_training: {is_training}, data_split_num: {data_split_num}, data_split_i: {data_split_i}, \nfile_list: {file_list}, \nfile_list_all: {file_list_all}"
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)
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else:
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file_list = [path]
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# total_num = len(file_list)
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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.info("distributed is not initialized, only single shard")
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#
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# if not kwargs.get("rank_split", False):
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# logging.info(f"Warning, rank_split disenabled, batch and shuffle data in global")
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# rank = 0
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# world_size = 1
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#
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# num_per_rank = total_num // world_size
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# if num_per_rank * world_size < total_num:
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# logging.info(f"Warning, jsonl file:{total_num} could not be divided by world_size: {world_size}, {path}")
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# total_num_needed = num_per_rank * world_size
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#
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# extra_num = total_num_needed - total_num
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# file_list_tmp = random.choices(file_list, k=extra_num)
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# file_list += file_list_tmp
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# logging.info(f"Warning, after random choices: {file_list}")
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#
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# file_list_rank = file_list[rank * num_per_rank:(rank + 1) * num_per_rank]
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#
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# logging.info(
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# f"is_training: {is_training}, file_list_rank: {file_list_rank}")
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# contents = []
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# for file_json in file_list_rank:
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contents = []
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for file_json in file_list:
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with open(file_json.strip(), 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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contents.append(data["text"])
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if "source" in data: # for speech lab pretrain
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prompt = data.get("prompt", "<ASR>")
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source = data["source"].replace(
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"/cpfs01", "/cpfs_speech/data"
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) # only use in alibaba gpu group: .replace("/cpfs01", "/cpfs_speech/data")
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target = data["target"]
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source_len = data.get("source_len", 1)
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target_len = data.get("target_len", 0)
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if "aishell" in source:
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target = target.replace(" ", "")
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if (
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source_len < self.min_source_length
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or source_len > self.max_source_length
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):
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continue
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if (
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target_len < self.min_target_length
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or target_len > self.max_target_length
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):
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continue
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if (source_len + target_len) > self.max_token_length:
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continue
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contents_i = {
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"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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text_language = data.get("text_language", None)
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if text_language is not None:
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contents_i["text_language"] = text_language
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# audio_language = data.get("audio_language", None)
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# if audio_language is not None:
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# contents_i["audio_language"] = audio_language
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contents.append(contents_i)
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self.contents = contents
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logging.info("total_num of samplers: {}, {}".format(len(self.contents), path))
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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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data = self.contents[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.get("source_len", 1)
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def get_target_len(self, data_dict):
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return data_dict.get("target_len", 0)
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