Merge remote-tracking branch 'refs/remotes/origin/main' update contextual f...

shixian.shi

2023-11-23 adc88bd9e76644badbbe006913addfa7cbe5d89c

 funasr/datasets/iterable_dataset.py

@@ -14,8 +14,9 @@
import numpy as np
import torch
import torchaudio
# import librosa
import librosa
from torch.utils.data.dataset import IterableDataset
from typeguard import check_argument_types
import os.path

from funasr.datasets.dataset import ESPnetDataset
@@ -66,8 +67,18 @@
        bytes = f.read()
    return load_bytes(bytes)

def load_wav(input):
    try:
        return torchaudio.load(input)[0].numpy()
    except:
        # waveform, _ = librosa.load(input, dtype='float32')
        waveform, _ = librosa.load(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][0].numpy(),
    "sound": load_wav,
    "pcm": load_pcm,
    "kaldi_ark": load_kaldi,
    "bytes": load_bytes,
@@ -107,10 +118,10 @@
            ] = None,
            float_dtype: str = "float32",
            fs: dict = None,
            mc: bool = False,
            int_dtype: str = "long",
            key_file: str = None,
    ):
        assert check_argument_types()
        if len(path_name_type_list) == 0:
            raise ValueError(
                '1 or more elements are required for "path_name_type_list"'
@@ -123,6 +134,7 @@
        self.int_dtype = int_dtype
        self.key_file = key_file
        self.fs = fs
        self.mc = mc

        self.debug_info = {}
        non_iterable_list = []
@@ -175,90 +187,97 @@
    def __iter__(self) -> Iterator[Tuple[Union[str, int], Dict[str, np.ndarray]]]:
        count = 0
        if len(self.path_name_type_list) != 0 and (self.path_name_type_list[0][2] == "bytes" or self.path_name_type_list[0][2] == "waveform"):
            linenum = len(self.path_name_type_list)
            data = {}
            value = self.path_name_type_list[0][0]
            uid = 'utt_id'
            name = self.path_name_type_list[0][1]
            _type = self.path_name_type_list[0][2]
            func = DATA_TYPES[_type]
            array = func(value)
            if self.fs is not None and name == "speech":
                audio_fs = self.fs["audio_fs"]
                model_fs = self.fs["model_fs"]
                if audio_fs is not None and model_fs is not None:
                    array = torch.from_numpy(array)
                    array = array.unsqueeze(0)
                    array = torchaudio.transforms.Resample(orig_freq=audio_fs,
                                                   new_freq=model_fs)(array)
                    array = array.squeeze(0).numpy()
            data[name] = array
            for i in range(linenum):
                value = self.path_name_type_list[i][0]
                uid = 'utt_id'
                name = self.path_name_type_list[i][1]
                _type = self.path_name_type_list[i][2]
                func = DATA_TYPES[_type]
                array = func(value)
                if self.fs is not None and (name == "speech" or name == "ref_speech"):
                    audio_fs = self.fs["audio_fs"]
                    model_fs = self.fs["model_fs"]
                    if audio_fs is not None and model_fs is not None:
                        array = torch.from_numpy(array)
                        array = array.unsqueeze(0)
                        array = torchaudio.transforms.Resample(orig_freq=audio_fs,
                                                       new_freq=model_fs)(array)
                        array = array.squeeze(0).numpy()

            if self.preprocess is not None:
                data = self.preprocess(uid, data)
            for name in data:
                count += 1
                value = data[name]
                if not isinstance(value, np.ndarray):
                    raise RuntimeError(
                        f'All values must be converted to np.ndarray object '
                        f'by preprocessing, but "{name}" is still {type(value)}.')
                # Cast to desired type
                if value.dtype.kind == 'f':
                    value = value.astype(self.float_dtype)
                elif value.dtype.kind == 'i':
                    value = value.astype(self.int_dtype)
                else:
                    raise NotImplementedError(
                        f'Not supported dtype: {value.dtype}')
                data[name] = value
                data[name] = array

                if self.preprocess is not None:
                    data = self.preprocess(uid, data)
                for name in data:
                    count += 1
                    value = data[name]
                    if not isinstance(value, np.ndarray):
                        raise RuntimeError(
                            f'All values must be converted to np.ndarray object '
                            f'by preprocessing, but "{name}" is still {type(value)}.')
                    # Cast to desired type
                    if value.dtype.kind == 'f':
                        value = value.astype(self.float_dtype)
                    elif value.dtype.kind == 'i':
                        value = value.astype(self.int_dtype)
                    else:
                        raise NotImplementedError(
                            f'Not supported dtype: {value.dtype}')
                    data[name] = value

            yield uid, data

        elif len(self.path_name_type_list) != 0 and self.path_name_type_list[0][2] == "sound" and not self.path_name_type_list[0][0].lower().endswith(".scp"):
            linenum = len(self.path_name_type_list)
            data = {}
            value = self.path_name_type_list[0][0]
            uid = os.path.basename(self.path_name_type_list[0][0]).split(".")[0]
            name = self.path_name_type_list[0][1]
            _type = self.path_name_type_list[0][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"

            func = DATA_TYPES[_type]
            array = func(value)
            if self.fs is not None and name == "speech":
                audio_fs = self.fs["audio_fs"]
                model_fs = self.fs["model_fs"]
                if audio_fs is not None and model_fs is not None:
                    array = torch.from_numpy(array)
                    array = array.unsqueeze(0)
                    array = torchaudio.transforms.Resample(orig_freq=audio_fs,
                                                           new_freq=model_fs)(array)
                    array = array.squeeze(0).numpy()
            data[name] = array

            if self.preprocess is not None:
                data = self.preprocess(uid, data)
            for name in data:
                count += 1
                value = data[name]
                if not isinstance(value, np.ndarray):
                    raise RuntimeError(
                        f'All values must be converted to np.ndarray object '
                        f'by preprocessing, but "{name}" is still {type(value)}.')
                # Cast to desired type
                if value.dtype.kind == 'f':
                    value = value.astype(self.float_dtype)
                elif value.dtype.kind == 'i':
                    value = value.astype(self.int_dtype)
            for i in range(linenum):
                value = self.path_name_type_list[i][0]
                uid = os.path.basename(self.path_name_type_list[i][0]).split(".")[0]
                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).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"):
                    audio_fs = self.fs["audio_fs"]
                    model_fs = self.fs["model_fs"]
                    if audio_fs is not None and model_fs is not None:
                        array = torch.from_numpy(array)
                        array = torchaudio.transforms.Resample(orig_freq=audio_fs,
                                                               new_freq=model_fs)(array)
                        array = array.numpy()
                        
                if _type == "sound":
                    if self.mc:
                        data[name] = array.transpose((1, 0))
                    else:
                        data[name] = array[0]
                else:
                    raise NotImplementedError(
                        f'Not supported dtype: {value.dtype}')
                data[name] = value
                    data[name] = array

                if self.preprocess is not None:
                    data = self.preprocess(uid, data)
                for name in data:
                    count += 1
                    value = data[name]
                    if not isinstance(value, np.ndarray):
                        raise RuntimeError(
                            f'All values must be converted to np.ndarray object '
                            f'by preprocessing, but "{name}" is still {type(value)}.')
                    # Cast to desired type
                    if value.dtype.kind == 'f':
                        value = value.astype(self.float_dtype)
                    elif value.dtype.kind == 'i':
                        value = value.astype(self.int_dtype)
                    else:
                        raise NotImplementedError(
                            f'Not supported dtype: {value.dtype}')
                    data[name] = value

            yield uid, data

@@ -323,11 +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
@@ -337,11 +353,16 @@
                        model_fs = self.fs["model_fs"]
                        if audio_fs is not None and model_fs is not None:
                            array = torch.from_numpy(array)
                            array = array.unsqueeze(0)
                            array = torchaudio.transforms.Resample(orig_freq=audio_fs,
                                                                   new_freq=model_fs)(array)
                            array = array.squeeze(0).numpy()
                    data[name] = array
                            array = array.numpy()
                    if _type == "sound":
                        if self.mc:
                            data[name] = array.transpose((1, 0))
                        else:
                            data[name] = array[0]
                    else:
                        data[name] = array
                if self.non_iterable_dataset is not None:
                    # 2.b. Load data from non-iterable dataset
                    _, from_non_iterable = self.non_iterable_dataset[uid]
@@ -374,3 +395,4 @@

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