Update readme.md

zhifu gao

2023-06-27 5e9ae0f1f913a36cab03eabb776b50557d81f50f

 funasr/datasets/iterable_dataset.py

@@ -8,11 +8,13 @@
from typing import Iterator
from typing import Tuple
from typing import Union
from typing import List

import kaldiio
import numpy as np
import torch
import torchaudio
import soundfile
from torch.utils.data.dataset import IterableDataset
from typeguard import check_argument_types
import os.path
@@ -65,8 +67,17 @@
        bytes = f.read()
    return load_bytes(bytes)

def load_wav(input):
    try:
        return torchaudio.load(input)[0].numpy()
    except:
        waveform, _ = soundfile.read(input, dtype='float32')
        if waveform.ndim == 2:
            waveform = waveform[:, 0]
        return np.expand_dims(waveform, axis=0)

DATA_TYPES = {
    "sound": lambda x: torchaudio.load(x)[0].numpy(),
    "sound": load_wav,
    "pcm": load_pcm,
    "kaldi_ark": load_kaldi,
    "bytes": load_bytes,
@@ -129,7 +140,7 @@
        non_iterable_list = []
        self.path_name_type_list = []

        if not isinstance(path_name_type_list[0], Tuple):
        if not isinstance(path_name_type_list[0], (Tuple, List)):
            path = path_name_type_list[0]
            name = path_name_type_list[1]
            _type = path_name_type_list[2]
@@ -227,13 +238,9 @@
                name = self.path_name_type_list[i][1]
                _type = self.path_name_type_list[i][2]
                if _type == "sound":
                    audio_type = os.path.basename(value).split(".")[-1].lower()
                    if audio_type not in SUPPORT_AUDIO_TYPE_SETS:
                        raise NotImplementedError(
                            f'Not supported audio type: {audio_type}')
                    if audio_type == "pcm":
                        _type = "pcm"

                   audio_type = os.path.basename(value).lower()
                   if audio_type.rfind(".pcm") >= 0:
                       _type = "pcm"
                func = DATA_TYPES[_type]
                array = func(value)
                if self.fs is not None and (name == "speech" or name == "ref_speech"):
@@ -244,10 +251,14 @@
                        array = torchaudio.transforms.Resample(orig_freq=audio_fs,
                                                               new_freq=model_fs)(array)
                        array = array.numpy()
                if self.mc:
                    data[name] = array.transpose(0, 1)
                        
                if _type == "sound":
                    if self.mc:
                        data[name] = array.transpose((1, 0))
                    else:
                        data[name] = array[0]
                else:
                    data[name] = array[0]
                    data[name] = array

                if self.preprocess is not None:
                    data = self.preprocess(uid, data)
@@ -331,11 +342,8 @@
                # 2.a. Load data streamingly
                for value, (path, name, _type) in zip(values, self.path_name_type_list):
                    if _type == "sound":
                        audio_type = os.path.basename(value).split(".")[-1].lower()
                        if audio_type not in SUPPORT_AUDIO_TYPE_SETS:
                            raise NotImplementedError(
                                f'Not supported audio type: {audio_type}')
                        if audio_type == "pcm":
                        audio_type = os.path.basename(value).lower()
                        if audio_type.rfind(".pcm") >= 0:
                            _type = "pcm"
                    func = DATA_TYPES[_type]
                    # Load entry
@@ -350,7 +358,7 @@
                            array = array.numpy()
                    if _type == "sound":
                        if self.mc:
                            data[name] = array.transpose(0, 1)
                            data[name] = array.transpose((1, 0))
                        else:
                            data[name] = array[0]
                    else:
@@ -387,3 +395,4 @@

        if count == 0:
            raise RuntimeError("No iteration")