From 4ace5a95b052d338947fc88809a440ccd55cf6b4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 十一月 2023 16:39:52 +0800
Subject: [PATCH] funasr pages
---
funasr/datasets/large_datasets/dataset.py | 73 +++++++++++++++++++++++++-----------
1 files changed, 51 insertions(+), 22 deletions(-)
diff --git a/funasr/datasets/large_datasets/dataset.py b/funasr/datasets/large_datasets/dataset.py
index 53994cb..adfe4f6 100644
--- a/funasr/datasets/large_datasets/dataset.py
+++ b/funasr/datasets/large_datasets/dataset.py
@@ -1,20 +1,22 @@
+import logging
import os
import random
-import numpy
from functools import partial
import torch
-import torchaudio
import torch.distributed as dist
+import torchaudio
+import numpy as np
+import soundfile
from kaldiio import ReadHelper
from torch.utils.data import IterableDataset
from funasr.datasets.large_datasets.datapipes.batch import MaxTokenBucketizerIterDataPipe
from funasr.datasets.large_datasets.datapipes.filter import FilterIterDataPipe
from funasr.datasets.large_datasets.datapipes.map import MapperIterDataPipe
+from funasr.datasets.large_datasets.utils.clipping import clipping
from funasr.datasets.large_datasets.utils.filter import filter
from funasr.datasets.large_datasets.utils.padding import padding
-from funasr.datasets.large_datasets.utils.clipping import clipping
from funasr.datasets.large_datasets.utils.tokenize import tokenize
@@ -28,7 +30,8 @@
class AudioDataset(IterableDataset):
- def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, mode="train"):
+ def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, speed_perturb=None,
+ mode="train"):
self.scp_lists = scp_lists
self.data_names = data_names
self.data_types = data_types
@@ -40,6 +43,9 @@
self.world_size = 1
self.worker_id = 0
self.num_workers = 1
+ self.speed_perturb = speed_perturb
+ if self.speed_perturb is not None:
+ logging.info("Using speed_perturb: {}".format(speed_perturb))
def set_epoch(self, epoch):
self.epoch = epoch
@@ -101,8 +107,8 @@
if data_type == "kaldi_ark":
ark_reader = ReadHelper('ark:{}'.format(data_file))
reader_list.append(ark_reader)
- elif data_type == "text" or data_type == "sound":
- text_reader = open(data_file, "r")
+ elif data_type == "text" or data_type == "sound" or data_type == 'text_hotword':
+ text_reader = open(data_file, "r", encoding="utf-8")
reader_list.append(text_reader)
elif data_type == "none":
continue
@@ -119,14 +125,27 @@
sample_dict["key"] = key
elif data_type == "sound":
key, path = item.strip().split()
- waveform, sampling_rate = torchaudio.load(path)
+ try:
+ waveform, sampling_rate = torchaudio.load(path)
+ except:
+ waveform, sampling_rate = soundfile.read(path, dtype='float32')
+ if waveform.ndim == 2:
+ waveform = waveform[:, 0]
+ waveform = np.expand_dims(waveform, axis=0)
+ waveform = torch.tensor(waveform)
if self.frontend_conf is not None:
if sampling_rate != self.frontend_conf["fs"]:
waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
new_freq=self.frontend_conf["fs"])(waveform)
- sampling_rate = self.frontend_conf["fs"]
+ sampling_rate = self.frontend_conf["fs"]
waveform = waveform.numpy()
mat = waveform[0]
+ if self.speed_perturb is not None:
+ speed = random.choice(self.speed_perturb)
+ if speed != 1.0:
+ mat, _ = torchaudio.sox_effects.apply_effects_tensor(
+ torch.tensor(mat).view(1, -1), sampling_rate, [['speed', str(speed)], ['rate', str(sampling_rate)]])
+ mat = mat.view(-1).numpy()
sample_dict[data_name] = mat
sample_dict["sampling_rate"] = sampling_rate
if data_name == "speech":
@@ -138,6 +157,12 @@
if "key" not in sample_dict:
sample_dict["key"] = segs[0]
sample_dict['hw_tag'] = 1
+ elif data_type == "text_nospace":
+ text = item
+ segs = text.strip().split(maxsplit=1)
+ sample_dict[data_name] = [x for x in segs[1]]
+ if "key" not in sample_dict:
+ sample_dict["key"] = segs[0]
else:
text = item
segs = text.strip().split()
@@ -168,6 +193,7 @@
bpe_tokenizer,
conf,
frontend_conf,
+ speed_perturb=None,
mode="train",
batch_mode="padding"):
scp_lists = read_lists(data_list_file)
@@ -176,39 +202,42 @@
data_types = conf.get("data_types", "kaldi_ark,text")
pre_hwfile = conf.get("pre_hwlist", None)
- pre_prob = conf.get("pre_prob", 0) # unused yet
-
- hw_config = {"sample_rate": conf.get("sample_rate", 0.6),
- "double_rate": conf.get("double_rate", 0.1),
- "hotword_min_length": conf.get("hotword_min_length", 2),
- "hotword_max_length": conf.get("hotword_max_length", 8),
- "pre_prob": conf.get("pre_prob", 0.0)}
-
+ # pre_prob = conf.get("pre_prob", 0) # unused yet
if pre_hwfile is not None:
pre_hwlist = []
- with open(pre_hwfile, 'r') as fin:
+ with open(pre_hwfile, 'r', encoding="utf-8") as fin:
for line in fin.readlines():
pre_hwlist.append(line.strip())
else:
pre_hwlist = None
+ hw_config = {"sample_rate": conf.get("sample_rate", 0.6),
+ "double_rate": conf.get("double_rate", 0.1),
+ "hotword_min_length": conf.get("hotword_min_length", 2),
+ "hotword_max_length": conf.get("hotword_max_length", 8),
+ "pre_prob": conf.get("pre_prob", 0.0),
+ "pre_hwlist": pre_hwlist}
+
+
+
dataset = AudioDataset(scp_lists,
data_names,
data_types,
frontend_conf=frontend_conf,
- shuffle=shuffle,
+ shuffle=shuffle,
+ speed_perturb=speed_perturb,
mode=mode,
)
-
- filter_conf = conf.get('filter_conf', {})
- filter_fn = partial(filter, **filter_conf)
- dataset = FilterIterDataPipe(dataset, fn=filter_fn)
if "text" in data_names:
vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict, 'bpe_tokenizer': bpe_tokenizer, 'hw_config': hw_config}
tokenize_fn = partial(tokenize, **vocab)
dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)
+ filter_conf = conf.get('filter_conf', {})
+ filter_fn = partial(filter, **filter_conf)
+ dataset = FilterIterDataPipe(dataset, fn=filter_fn)
+
if shuffle:
buffer_conf = conf.get('shuffle_conf', {})
buffer_size = buffer_conf['shuffle_size']
--
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