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import random
from funasr.register import tables
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
@tables.register("dataset_classes", "AudioDataset")
class AudioDataset(torch.utils.data.Dataset):
"""
AudioDataset
"""
def __init__(
self,
path,
index_ds: str = None,
frontend=None,
tokenizer=None,
is_training: bool = True,
int_pad_value: int = -1,
float_pad_value: float = 0.0,
**kwargs,
):
super().__init__()
index_ds_class = tables.index_ds_classes.get(index_ds)
self.index_ds = index_ds_class(path, **kwargs)
self.preprocessor_speech = None
self.preprocessor_text = None
if is_training:
preprocessor_speech = kwargs.get("preprocessor_speech", None)
if preprocessor_speech:
preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
preprocessor_speech = preprocessor_speech_class(
**kwargs.get("preprocessor_speech_conf")
)
self.preprocessor_speech = preprocessor_speech
preprocessor_text = kwargs.get("preprocessor_text", None)
if preprocessor_text:
preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
self.preprocessor_text = preprocessor_text
self.frontend = frontend
self.fs = 16000 if frontend is None else frontend.fs
self.data_type = "sound"
self.tokenizer = tokenizer
self.int_pad_value = int_pad_value
self.float_pad_value = float_pad_value
def get_source_len(self, index):
item = self.index_ds[index]
return self.index_ds.get_source_len(item)
def get_target_len(self, index):
item = self.index_ds[index]
return self.index_ds.get_target_len(item)
def __len__(self):
return len(self.index_ds)
def __getitem__(self, index):
item = self.index_ds[index]
# import pdb;
# pdb.set_trace()
source = item["source"]
data_src = load_audio_text_image_video(source, fs=self.fs)
if self.preprocessor_speech:
data_src = self.preprocessor_speech(data_src, fs=self.fs)
speech, speech_lengths = extract_fbank(
data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
) # speech: [b, T, d]
target = item["target"]
if self.preprocessor_text:
target = self.preprocessor_text(target)
if self.tokenizer:
ids = self.tokenizer.encode(target)
text = torch.tensor(ids, dtype=torch.int64)
else:
ids = target
text = ids
ids_lengths = len(ids)
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
return {
"speech": speech[0, :, :],
"speech_lengths": speech_lengths,
"text": text,
"text_lengths": text_lengths,
}
def collator(self, samples: list = None):
outputs = {}
for sample in samples:
for key in sample.keys():
if key not in outputs:
outputs[key] = []
outputs[key].append(sample[key])
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
pad_value = self.int_pad_value
else:
pad_value = self.float_pad_value
outputs[key] = torch.nn.utils.rnn.pad_sequence(
data_list, batch_first=True, padding_value=pad_value
)
return outputs
@tables.register("dataset_classes", "AudioDatasetHotword")
class AudioDatasetHotword(AudioDataset):
# for finetuning contextual_paraformer and seaco_paraformer
def __init__(
self,
*args,
seaco_id: bool = 0,
**kwargs,
):
super().__init__(*args, **kwargs)
self.seaco_id = seaco_id
def __getitem__(self, index):
item = self.index_ds[index]
# import pdb;
# pdb.set_trace()
source = item["source"]
data_src = load_audio_text_image_video(source, fs=self.fs)
if self.preprocessor_speech:
data_src = self.preprocessor_speech(data_src, fs=self.fs)
speech, speech_lengths = extract_fbank(
data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
) # speech: [b, T, d]
target = item["target"]
if self.preprocessor_text:
target = self.preprocessor_text(target)
if self.tokenizer:
ids = self.tokenizer.encode(target)
text = torch.tensor(ids, dtype=torch.int64)
else:
ids = target
text = ids
ids_lengths = len(ids)
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
def generate_index(
length,
hotword_min_length=2,
hotword_max_length=8,
sample_rate=0.75,
double_rate=0.1,
pre_prob=0.0,
pre_index=None,
pre_hwlist=None,
):
if length < hotword_min_length:
return [-1]
if random.random() < sample_rate:
if pre_prob > 0 and random.random() < pre_prob and pre_index is not None:
return pre_index
if length == hotword_min_length:
return [0, length - 1]
elif (
random.random() < double_rate
and length > hotword_max_length + hotword_min_length + 2
):
# sample two hotwords in a sentence
_max_hw_length = min(hotword_max_length, length // 2)
# first hotword
start1 = random.randint(0, length // 3)
end1 = random.randint(
start1 + hotword_min_length - 1, start1 + _max_hw_length - 1
)
# second hotword
start2 = random.randint(end1 + 1, length - hotword_min_length)
end2 = random.randint(
min(length - 1, start2 + hotword_min_length - 1),
min(length - 1, start2 + hotword_max_length - 1),
)
return [start1, end1, start2, end2]
else: # single hotword
start = random.randint(0, length - hotword_min_length)
end = random.randint(
min(length - 1, start + hotword_min_length - 1),
min(length - 1, start + hotword_max_length - 1),
)
return [start, end]
else:
return [-1]
hotword_indx = generate_index(text_lengths[0])
return {
"speech": speech[0, :, :],
"speech_lengths": speech_lengths,
"text": text,
"text_lengths": text_lengths,
"hotword_indx": hotword_indx,
"seaco_id": self.seaco_id,
}
def collator(self, samples: list = None):
outputs = {}
hotword_indxs = []
seaco_id = samples[0]["seaco_id"]
for sample in samples:
for key in sample.keys():
if key == "seaco_id":
continue
elif key == "hotword_indx":
hotword_indxs.append(sample[key])
else:
if key not in outputs:
outputs[key] = []
outputs[key].append(sample[key])
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
pad_value = self.int_pad_value
else:
pad_value = self.float_pad_value
outputs[key] = torch.nn.utils.rnn.pad_sequence(
data_list, batch_first=True, padding_value=pad_value
)
hotword_list, hotword_lengths = [], []
text = outputs["text"]
seaco_label_pad = torch.ones_like(text) * -1 if seaco_id else None
for b, (hotword_indx, one_text, length) in enumerate(
zip(hotword_indxs, text, outputs["text_lengths"])
):
length = length[0]
if seaco_label_pad is not None:
seaco_label_pad[b][:length] = seaco_id
if hotword_indx[0] != -1:
start, end = int(hotword_indx[0]), int(hotword_indx[1])
hotword = one_text[start : end + 1]
hotword_list.append(hotword)
hotword_lengths.append(end - start + 1)
if seaco_label_pad is not None:
seaco_label_pad[b][start : end + 1] = one_text[start : end + 1]
if len(hotword_indx) == 4 and hotword_indx[2] != -1:
# the second hotword if exist
start, end = int(hotword_indx[2]), int(hotword_indx[3])
hotword_list.append(one_text[start : end + 1])
hotword_lengths.append(end - start + 1)
if seaco_label_pad is not None:
seaco_label_pad[b][start : end + 1] = one_text[start : end + 1]
hotword_list.append(torch.tensor([1]))
hotword_lengths.append(1)
hotword_pad = torch.nn.utils.rnn.pad_sequence(
hotword_list, batch_first=True, padding_value=0
)
outputs["hotword_pad"] = hotword_pad
outputs["hotword_lengths"] = torch.tensor(hotword_lengths, dtype=torch.int32)
if seaco_label_pad is not None:
outputs["seaco_label_pad"] = seaco_label_pad
return outputs
|