From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords
---
funasr/datasets/large_datasets/dataset.py | 170 +++++++++++++++++++++++++++++++++++++++++---------------
1 files changed, 123 insertions(+), 47 deletions(-)
diff --git a/funasr/datasets/large_datasets/dataset.py b/funasr/datasets/large_datasets/dataset.py
index 33ed13a..1e5b6c1 100644
--- a/funasr/datasets/large_datasets/dataset.py
+++ b/funasr/datasets/large_datasets/dataset.py
@@ -6,6 +6,10 @@
import torch
import torch.distributed as dist
import torchaudio
+import numpy as np
+
+# import librosa
+import librosa
from kaldiio import ReadHelper
from torch.utils.data import IterableDataset
@@ -20,7 +24,7 @@
def read_lists(list_file):
lists = []
- with open(list_file, 'r', encoding='utf8') as fin:
+ with open(list_file, "r", encoding="utf8") as fin:
for line in fin:
parts = line.strip()
lists.append(parts)
@@ -28,8 +32,16 @@
class AudioDataset(IterableDataset):
- def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, speed_perturb=None,
- 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
@@ -61,7 +73,7 @@
if self.shuffle:
random.seed(self.epoch)
random.shuffle(data_index)
- return data_index[self.rank::self.world_size]
+ return data_index[self.rank :: self.world_size]
return data_index
@@ -74,7 +86,7 @@
self.worker_id = worker_info.id
self.num_workers = worker_info.num_workers
- return rank_data_index[self.worker_id::self.num_workers]
+ return rank_data_index[self.worker_id :: self.num_workers]
def close_reader(self, reader_list):
for reader in reader_list:
@@ -88,8 +100,8 @@
for index in worker_data_index:
data = dict(scp=self.scp_lists[index])
- assert 'scp' in data
- scp = data['scp']
+ assert "scp" in data
+ scp = data["scp"]
data_file_list = scp.strip().split()
data_name_list = self.data_names.split(",")
data_type_list = self.data_types.split(",")
@@ -97,16 +109,17 @@
for file in data_file_list:
assert os.path.exists(file), "{} not exists".format(file)
- assert len(data_file_list) == len(data_name_list) == len(data_type_list), \
- "The item number of data, data_names, data_types must be the same "
+ assert (
+ len(data_file_list) == len(data_name_list) == len(data_type_list)
+ ), "The item number of data, data_names, data_types must be the same "
reader_list = []
for data_file, data_type in zip(data_file_list, data_type_list):
if data_type == "kaldi_ark":
- ark_reader = ReadHelper('ark:{}'.format(data_file))
+ 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
@@ -123,11 +136,20 @@
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 = librosa.load(path, dtype='float32')
+ waveform, sampling_rate = librosa.load(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)
+ waveform = torchaudio.transforms.Resample(
+ orig_freq=sampling_rate, new_freq=self.frontend_conf["fs"]
+ )(waveform)
sampling_rate = self.frontend_conf["fs"]
waveform = waveform.numpy()
mat = waveform[0]
@@ -135,11 +157,28 @@
speed = random.choice(self.speed_perturb)
if speed != 1.0:
mat, _ = torchaudio.sox_effects.apply_effects_tensor(
- mat, sampling_rate, [['speed', str(speed)], ['rate', str(sampling_rate)]])
+ 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":
sample_dict["key"] = key
+ elif data_type == "text_hotword":
+ text = item
+ segs = text.strip().split()
+ sample_dict[data_name] = segs[1:]
+ 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()
@@ -158,61 +197,98 @@
def len_fn_token(data):
assert "speech" in data
if "sampling_rate" in data:
- return (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
+ return (data["speech"].shape[0] / data["sampling_rate"]) * 1000.0
else:
return data["speech"].shape[0]
-def Dataset(data_list_file,
- dict,
- seg_dict,
- punc_dict,
- bpe_tokenizer,
- conf,
- frontend_conf,
- speed_perturb=None,
- mode="train",
- batch_mode="padding"):
+def Dataset(
+ data_list_file,
+ dict,
+ seg_dict,
+ punc_dict,
+ bpe_tokenizer,
+ conf,
+ frontend_conf,
+ speed_perturb=None,
+ mode="train",
+ batch_mode="padding",
+):
scp_lists = read_lists(data_list_file)
- shuffle = conf.get('shuffle', True)
+ shuffle = conf.get("shuffle", True)
data_names = conf.get("data_names", "speech,text")
data_types = conf.get("data_types", "kaldi_ark,text")
- dataset = AudioDataset(scp_lists, data_names, data_types, frontend_conf=frontend_conf, 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)
+ pre_hwfile = conf.get("pre_hwlist", None)
+ # pre_prob = conf.get("pre_prob", 0) # unused yet
+ if pre_hwfile is not None:
+ pre_hwlist = []
+ 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,
+ speed_perturb=speed_perturb,
+ mode=mode,
+ )
if "text" in data_names:
- vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict, 'bpe_tokenizer': bpe_tokenizer}
+ 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']
- sort_size = buffer_conf['sort_size']
+ buffer_conf = conf.get("shuffle_conf", {})
+ buffer_size = buffer_conf["shuffle_size"]
+ sort_size = buffer_conf["sort_size"]
else:
buffer_size = 0
sort_size = 1
- batch_conf = conf.get('batch_conf', {})
- batch_size = batch_conf['batch_size']
- batch_type = batch_conf['batch_type']
+ batch_conf = conf.get("batch_conf", {})
+ batch_size = batch_conf["batch_size"]
+ batch_type = batch_conf["batch_type"]
assert batch_type in ["example", "token"]
- if batch_type == 'example':
+ if batch_type == "example":
len_fn = len_fn_example
else:
len_fn = len_fn_token
- dataset = MaxTokenBucketizerIterDataPipe(dataset,
- batch_size=batch_size,
- len_fn=len_fn,
- buffer_size=buffer_size,
- sort_size=sort_size,
- batch_mode=batch_mode)
+ dataset = MaxTokenBucketizerIterDataPipe(
+ dataset,
+ batch_size=batch_size,
+ len_fn=len_fn,
+ buffer_size=buffer_size,
+ sort_size=sort_size,
+ batch_mode=batch_mode,
+ )
int_pad_value = conf.get("int_pad_value", -1)
float_pad_value = conf.get("float_pad_value", 0.0)
--
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