update

speech_asr

2023-04-17 9a6de675dc0bf16a8c3440c7f5e42cfccd1433ac

update

 funasr/datasets/small_datasets/build_loader.py

@@ -1,15 +1,42 @@
import os

import torch
from funasr.datasets.small_datasets.dataset import ESPnetDataset
from funasr.datasets.small_datasets.preprocessor import build_preprocess
from funasr.samplers.build_batch_sampler import build_batch_sampler

def build_dataloader(args, train=False):
    preprocess_fn = build_preprocess(args, train=train)
def build_dataloader(args, mode="train"):
    preprocess_fn = build_preprocess(args, train=mode=="train")
    dest_sample_rate = args.frontend_conf["fs"] if (args.frontend_conf is not None and "fs" in args.frontend_conf) else 16000
    if mode == "train":
        data_path_and_name_and_type = args.train_data_path_and_name_and_type
        shape_files = args.train_shape_file
    elif mode == "valid":
        data_path_and_name_and_type = args.valid_data_path_and_name_and_type
        shape_files = args.valid_shape_file
    else:
        raise NotImplementedError(f"mode={mode}")
    dataset = ESPnetDataset(
        iter_options.data_path_and_name_and_type,
        data_path_and_name_and_type,
        float_dtype=args.train_dtype,
        preprocess=preprocess_fn,
        max_cache_size=args.max_cache_size,
        max_cache_fd=args.max_cache_fd,
        dest_sample_rate=dest_sample_rate,
    )
    if os.path.exists(os.path.join(data_path_and_name_and_type[0][0].parent, "utt2category")):
        utt2category_file = os.path.join(data_path_and_name_and_type[0][0].parent, "utt2category")
    else:
        utt2category_file = None
    batch_sampler = build_batch_sampler(
        type=args.batch_type,
        shape_files=iter_options.shape_files,
        fold_lengths=args.fold_length,
        batch_size=iter_options.batch_size,
        batch_bins=iter_options.batch_bins,
        sort_in_batch=args.sort_in_batch,
        sort_batch=args.sort_batch,
        drop_last=False,
        min_batch_size=torch.distributed.get_world_size() if args.distributed else 1,
        utt2category_file=utt2category_file,
    )

 funasr/datasets/small_datasets/dataset.py

@@ -12,7 +12,6 @@
from typing import Tuple
from typing import Union

import humanfriendly
import kaldiio
import numpy as np
import torch
@@ -22,7 +21,6 @@

from funasr.fileio.npy_scp import NpyScpReader
from funasr.fileio.sound_scp import SoundScpReader
from funasr.utils.sized_dict import SizedDict


class AdapterForSoundScpReader(collections.abc.Mapping):
@@ -111,8 +109,6 @@
            ] = None,
            float_dtype: str = "float32",
            int_dtype: str = "long",
            max_cache_size: Union[float, int, str] = 0.0,
            max_cache_fd: int = 0,
            dest_sample_rate: int = 16000,
    ):
        assert check_argument_types()
@@ -126,7 +122,6 @@

        self.float_dtype = float_dtype
        self.int_dtype = int_dtype
        self.max_cache_fd = max_cache_fd
        self.dest_sample_rate = dest_sample_rate

        self.loader_dict = {}
@@ -141,14 +136,6 @@
            if len(self.loader_dict[name]) == 0:
                raise RuntimeError(f"{path} has no samples")

        if isinstance(max_cache_size, str):
            max_cache_size = humanfriendly.parse_size(max_cache_size)
        self.max_cache_size = max_cache_size
        if max_cache_size > 0:
            self.cache = SizedDict(shared=True)
        else:
            self.cache = None

    def _build_loader(
            self, path: str, loader_type: str
    ) -> Mapping[str, Union[np.ndarray, torch.Tensor, str, numbers.Number]]:
@@ -162,7 +149,7 @@
            loader = SoundScpReader(path, self.dest_sample_rate, normalize=True, always_2d=False)
            return AdapterForSoundScpReader(loader, self.float_dtype)
        elif loader_type == "kaldi_ark":
            loader = kaldiio.load_scp(path, max_cache_fd=self.max_cache_fd)
            loader = kaldiio.load_scp(path)
            return AdapterForSoundScpReader(loader, self.float_dtype)
        elif loader_type == "npy":
            return NpyScpReader()
@@ -206,10 +193,6 @@
        if isinstance(uid, int):
            d = next(iter(self.loader_dict.values()))
            uid = list(d)[uid]

        if self.cache is not None and uid in self.cache:
            data = self.cache[uid]
            return uid, data

        data = {}
        # 1. Load data from each loaders
@@ -260,9 +243,6 @@
            else:
                raise NotImplementedError(f"Not supported dtype: {value.dtype}")
            data[name] = value

        if self.cache is not None and self.cache.size < self.max_cache_size:
            self.cache[uid] = data

        retval = uid, data
        assert check_return_type(retval)

 funasr/datasets/small_datasets/preprocessor.py

@@ -855,6 +855,19 @@
            text_name=text_names,
            non_linguistic_symbols=args.non_linguistic_symbols,
        )
    elif args.task_name == "lm":
        retval = LMPreprocessor(
            train=train,
            token_type=args.token_type,
            token_list=args.token_list,
            bpemodel=args.bpemodel,
            text_cleaner=args.cleaner,
            g2p_type=args.g2p,
            text_name="text",
            non_linguistic_symbols=args.non_linguistic_symbols,
            split_with_space=args.split_with_space,
            seg_dict_file=args.seg_dict_file
        )
    elif args.task_name == "vad":
        retval = None
    else: