From c14169f374a05387f09087b006d1c046f2720d61 Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期日, 05 二月 2023 12:12:03 +0800
Subject: [PATCH] support audio uppersampling and downsampling
---
funasr/datasets/iterable_dataset.py | 31 +++++++++++++++++++++++++++++--
1 files changed, 29 insertions(+), 2 deletions(-)
diff --git a/funasr/datasets/iterable_dataset.py b/funasr/datasets/iterable_dataset.py
index 1fc9270..2ac37b2 100644
--- a/funasr/datasets/iterable_dataset.py
+++ b/funasr/datasets/iterable_dataset.py
@@ -11,7 +11,6 @@
import kaldiio
import numpy as np
-import soundfile
import torch
import torchaudio
from torch.utils.data.dataset import IterableDataset
@@ -101,6 +100,7 @@
[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 @@
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 @@
_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 @@
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 @@
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 @@
if count == 0:
raise RuntimeError("No iteration")
-
--
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