websocket bug

游雁

2023-07-03 4ee715e70e36cdba7b05fe044fecab9cf4fa16ff

 funasr/datasets/iterable_dataset.py

@@ -8,13 +8,14 @@
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

from funasr.datasets.dataset import ESPnetDataset
@@ -65,8 +66,17 @@
        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][0].numpy(),
    "sound": load_wav,
    "pcm": load_pcm,
    "kaldi_ark": load_kaldi,
    "bytes": load_bytes,
@@ -106,10 +116,10 @@
            ] = 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"'
@@ -122,12 +132,13 @@
        self.int_dtype = int_dtype
        self.key_file = key_file
        self.fs = fs
        self.mc = mc

        self.debug_info = {}
        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]
@@ -174,90 +185,97 @@
    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

@@ -322,11 +340,8 @@
                # 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
@@ -336,11 +351,16 @@
                        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]
@@ -373,3 +393,4 @@

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