From fcc9c89eaba9a4e36c54958aeedeec7ab3756cd7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 13 二月 2023 17:43:31 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/datasets/iterable_dataset.py | 43 ++++++++++++++++++++++++++++++++++++++++---
1 files changed, 40 insertions(+), 3 deletions(-)
diff --git a/funasr/datasets/iterable_dataset.py b/funasr/datasets/iterable_dataset.py
index 1fc9270..fa0adeb 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
@@ -21,7 +20,7 @@
from funasr.datasets.dataset import ESPnetDataset
-SUPPORT_AUDIO_TYPE_SETS = ['flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma']
+SUPPORT_AUDIO_TYPE_SETS = ['flac', 'mp3', 'ogg', 'opus', 'wav', 'pcm']
def load_kaldi(input):
retval = kaldiio.load_mat(input)
@@ -61,9 +60,14 @@
array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
return array
+def load_pcm(input):
+ with open(input,"rb") as f:
+ bytes = f.read()
+ return load_bytes(bytes)
DATA_TYPES = {
"sound": lambda x: torchaudio.load(x)[0][0].numpy(),
+ "pcm": load_pcm,
"kaldi_ark": load_kaldi,
"bytes": load_bytes,
"waveform": lambda x: x,
@@ -101,6 +105,7 @@
[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 +121,7 @@
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 +181,15 @@
_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:
@@ -209,8 +224,20 @@
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:
@@ -299,9 +326,20 @@
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]
# 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 +373,3 @@
if count == 0:
raise RuntimeError("No iteration")
-
--
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