mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
224 lines
8.5 KiB
Python
224 lines
8.5 KiB
Python
import logging
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import os
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import random
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from functools import partial
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import torch
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import torch.distributed as dist
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import torchaudio
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from kaldiio import ReadHelper
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from torch.utils.data import IterableDataset
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from funasr.datasets.large_datasets.datapipes.batch import MaxTokenBucketizerIterDataPipe
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from funasr.datasets.large_datasets.datapipes.filter import FilterIterDataPipe
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from funasr.datasets.large_datasets.datapipes.map import MapperIterDataPipe
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from funasr.datasets.large_datasets.utils.clipping import clipping
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from funasr.datasets.large_datasets.utils.filter import filter
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from funasr.datasets.large_datasets.utils.padding import padding
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from funasr.datasets.large_datasets.utils.tokenize import tokenize
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def read_lists(list_file):
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lists = []
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with open(list_file, 'r', encoding='utf8') as fin:
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for line in fin:
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parts = line.strip()
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lists.append(parts)
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return lists
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class AudioDataset(IterableDataset):
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def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, speed_perturb=None,
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mode="train"):
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self.scp_lists = scp_lists
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self.data_names = data_names
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self.data_types = data_types
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self.frontend_conf = frontend_conf
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self.shuffle = shuffle
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self.mode = mode
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self.epoch = -1
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self.rank = 0
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self.world_size = 1
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self.worker_id = 0
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self.num_workers = 1
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self.speed_perturb = speed_perturb
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if self.speed_perturb is not None:
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logging.info("Using speed_perturb: {}".format(speed_perturb))
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def set_epoch(self, epoch):
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self.epoch = epoch
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def get_rank_data_list(self, data_index):
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assert dist.is_available()
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if dist.is_initialized():
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self.rank = dist.get_rank()
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self.world_size = dist.get_world_size()
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else:
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self.rank = 0
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self.world_size = 1
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if self.mode == "train":
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if self.shuffle:
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random.seed(self.epoch)
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random.shuffle(data_index)
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return data_index[self.rank::self.world_size]
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return data_index
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def get_worker_data_list(self, rank_data_index):
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worker_info = torch.utils.data.get_worker_info()
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if worker_info is None:
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self.worker_id = 0
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self.num_workers = 1
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else:
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self.worker_id = worker_info.id
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self.num_workers = worker_info.num_workers
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return rank_data_index[self.worker_id::self.num_workers]
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def close_reader(self, reader_list):
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for reader in reader_list:
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reader.close()
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def __iter__(self):
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data_index = list(range(len(self.scp_lists)))
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rank_data_index = self.get_rank_data_list(data_index)
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worker_data_index = self.get_worker_data_list(rank_data_index)
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for index in worker_data_index:
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data = dict(scp=self.scp_lists[index])
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assert 'scp' in data
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scp = data['scp']
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data_file_list = scp.strip().split()
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data_name_list = self.data_names.split(",")
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data_type_list = self.data_types.split(",")
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for file in data_file_list:
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assert os.path.exists(file), "{} not exists".format(file)
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assert len(data_file_list) == len(data_name_list) == len(data_type_list), \
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"The item number of data, data_names, data_types must be the same "
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reader_list = []
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for data_file, data_type in zip(data_file_list, data_type_list):
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if data_type == "kaldi_ark":
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ark_reader = ReadHelper('ark:{}'.format(data_file))
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reader_list.append(ark_reader)
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elif data_type == "text" or data_type == "sound":
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text_reader = open(data_file, "r")
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reader_list.append(text_reader)
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elif data_type == "none":
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continue
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else:
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raise TypeError("Data type {} is not supported".format(data_type))
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for items in zip(*reader_list):
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sample_dict = {}
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for item, (data_name, data_type) in zip(items, zip(data_name_list, data_type_list)):
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if data_type == "kaldi_ark":
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key, mat = item
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sample_dict[data_name] = mat
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if data_name == "speech":
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sample_dict["key"] = key
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elif data_type == "sound":
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key, path = item.strip().split()
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waveform, sampling_rate = torchaudio.load(path)
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if self.frontend_conf is not None:
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if sampling_rate != self.frontend_conf["fs"]:
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waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
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new_freq=self.frontend_conf["fs"])(waveform)
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sampling_rate = self.frontend_conf["fs"]
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waveform = waveform.numpy()
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mat = waveform[0]
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if self.speed_perturb is not None:
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speed = random.choice(self.speed_perturb)
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if speed != 1.0:
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mat, _ = torchaudio.sox_effects.apply_effects_tensor(
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mat, sampling_rate, [['speed', str(speed)], ['rate', str(sampling_rate)]])
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sample_dict[data_name] = mat
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sample_dict["sampling_rate"] = sampling_rate
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if data_name == "speech":
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sample_dict["key"] = key
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else:
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text = item
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segs = text.strip().split()
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sample_dict[data_name] = segs[1:]
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if "key" not in sample_dict:
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sample_dict["key"] = segs[0]
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yield sample_dict
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self.close_reader(reader_list)
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def len_fn_example(data):
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return 1
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def len_fn_token(data):
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assert "speech" in data
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if "sampling_rate" in data:
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return (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
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else:
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return data["speech"].shape[0]
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def Dataset(data_list_file,
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dict,
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seg_dict,
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punc_dict,
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bpe_tokenizer,
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conf,
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frontend_conf,
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speed_perturb=None,
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mode="train",
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batch_mode="padding"):
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scp_lists = read_lists(data_list_file)
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shuffle = conf.get('shuffle', True)
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data_names = conf.get("data_names", "speech,text")
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data_types = conf.get("data_types", "kaldi_ark,text")
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dataset = AudioDataset(scp_lists, data_names, data_types, frontend_conf=frontend_conf, shuffle=shuffle,
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speed_perturb=speed_perturb, mode=mode)
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filter_conf = conf.get('filter_conf', {})
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filter_fn = partial(filter, **filter_conf)
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dataset = FilterIterDataPipe(dataset, fn=filter_fn)
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if "text" in data_names:
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vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict, 'bpe_tokenizer': bpe_tokenizer}
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tokenize_fn = partial(tokenize, **vocab)
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dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)
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if shuffle:
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buffer_conf = conf.get('shuffle_conf', {})
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buffer_size = buffer_conf['shuffle_size']
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sort_size = buffer_conf['sort_size']
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else:
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buffer_size = 0
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sort_size = 1
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batch_conf = conf.get('batch_conf', {})
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batch_size = batch_conf['batch_size']
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batch_type = batch_conf['batch_type']
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assert batch_type in ["example", "token"]
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if batch_type == 'example':
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len_fn = len_fn_example
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else:
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len_fn = len_fn_token
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dataset = MaxTokenBucketizerIterDataPipe(dataset,
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batch_size=batch_size,
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len_fn=len_fn,
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buffer_size=buffer_size,
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sort_size=sort_size,
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batch_mode=batch_mode)
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int_pad_value = conf.get("int_pad_value", -1)
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float_pad_value = conf.get("float_pad_value", 0.0)
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padding_conf = {"int_pad_value": int_pad_value, "float_pad_value": float_pad_value}
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padding_fn = partial(padding, **padding_conf)
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dataset = MapperIterDataPipe(dataset, fn=padding_fn if batch_mode == "padding" else clipping)
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return dataset
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