speech_recognition/FunASR
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support audio uppersampling and downsampling

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hnluo 2023-02-05 12:12:03 +08:00 committed by GitHub
parent aa456c5e44
commit c14169f374
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1 changed files with 29 additions and 2 deletions
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31
funasr/datasets/iterable_dataset.py
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@ -11,7 +11,6 @@ from typing import Union
import kaldiio
import numpy as np
import soundfile
import torch
import torchaudio
from torch.utils.data.dataset import IterableDataset
@ -101,6 +100,7 @@ class IterableESPnetDataset(IterableDataset):
[str, Dict[str, np.ndarray]], Dict[str, np.ndarray]
] = None,
float_dtype: str = "float32",
fs: dict = None,
int_dtype: str = "long",
key_file: str = None,
):
@ -116,6 +116,7 @@ class IterableESPnetDataset(IterableDataset):
self.float_dtype = float_dtype
self.int_dtype = int_dtype
self.key_file = key_file
self.fs = fs
self.debug_info = {}
non_iterable_list = []
@ -175,6 +176,15 @@ class IterableESPnetDataset(IterableDataset):
_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
if self.preprocess is not None:
@ -211,6 +221,15 @@ class IterableESPnetDataset(IterableDataset):
f'Not supported audio type: {audio_type}')
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:
@ -302,6 +321,15 @@ class IterableESPnetDataset(IterableDataset):
func = DATA_TYPES[_type]
# Load entry
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.non_iterable_dataset is not None:
# 2.b. Load data from non-iterable dataset
@ -335,4 +363,3 @@ class IterableESPnetDataset(IterableDataset):
if count == 0:
raise RuntimeError("No iteration")