From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/utils/prepare_data.py | 43 +++++++++++++++++++++++++++----------------
1 files changed, 27 insertions(+), 16 deletions(-)
diff --git a/funasr/utils/prepare_data.py b/funasr/utils/prepare_data.py
index 0e773bb..36eebdc 100644
--- a/funasr/utils/prepare_data.py
+++ b/funasr/utils/prepare_data.py
@@ -5,9 +5,9 @@
import kaldiio
import numpy as np
+import librosa
import torch.distributed as dist
import torchaudio
-import soundfile
def filter_wav_text(data_dir, dataset):
@@ -46,7 +46,7 @@
try:
waveform, sampling_rate = torchaudio.load(wav_path)
except:
- waveform, sampling_rate = soundfile.read(wav_path)
+ waveform, sampling_rate = librosa.load(wav_path)
waveform = np.expand_dims(waveform, axis=0)
n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"])
feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"]
@@ -87,6 +87,7 @@
sample_name, feature_path = line.strip().split()
feature = kaldiio.load_mat(feature_path)
n_frames, feature_dim = feature.shape
+ write_flag = True
if n_frames > 0 and length_min > 0:
write_flag = n_frames >= length_min
if n_frames > 0 and length_max > 0:
@@ -195,23 +196,10 @@
def prepare_data(args, distributed_option):
- distributed = distributed_option.distributed
- if not distributed or distributed_option.dist_rank == 0:
- if hasattr(args, "filter_input") and args.filter_input:
- filter_wav_text(args.data_dir, args.train_set)
- filter_wav_text(args.data_dir, args.valid_set)
-
- if args.dataset_type == "small":
- calc_shape(args, args.train_set)
- calc_shape(args, args.valid_set)
-
- if args.dataset_type == "large":
- generate_data_list(args, args.data_dir, args.train_set)
- generate_data_list(args, args.data_dir, args.valid_set)
-
data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
file_names = args.data_file_names.split(",")
+ batch_type = args.dataset_conf["batch_conf"]["batch_type"]
print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names))
assert len(data_names) == len(data_types) == len(file_names)
if args.dataset_type == "small":
@@ -223,9 +211,32 @@
["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type])
args.valid_data_path_and_name_and_type.append(
["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type])
+ if os.path.exists(args.train_shape_file[0]):
+ assert os.path.exists(args.valid_shape_file[0])
+ print('shape file for small dataset already exists.')
+ return
else:
concat_data_name = "_".join(data_names)
args.train_data_file = os.path.join(args.data_dir, args.train_set, "{}_data.list".format(concat_data_name))
args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name))
+ if os.path.exists(args.train_data_file):
+ assert os.path.exists(args.valid_data_file)
+ print('data list for large dataset already exists.')
+ return
+
+ distributed = distributed_option.distributed
+ if not distributed or distributed_option.dist_rank == 0:
+ if hasattr(args, "filter_input") and args.filter_input:
+ filter_wav_text(args.data_dir, args.train_set)
+ filter_wav_text(args.data_dir, args.valid_set)
+
+ if args.dataset_type == "small" and batch_type != "unsorted":
+ calc_shape(args, args.train_set)
+ calc_shape(args, args.valid_set)
+
+ if args.dataset_type == "large":
+ generate_data_list(args, args.data_dir, args.train_set)
+ generate_data_list(args, args.data_dir, args.valid_set)
+
if distributed:
dist.barrier()
--
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