import torch
|
import random
|
|
from funasr.register import tables
|
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
|
|
|
@tables.register("dataset_classes", "SenseVoiceDataset")
|
class SenseVoiceDataset(torch.utils.data.Dataset):
|
"""
|
SenseVoiceDataset
|
"""
|
|
def __init__(
|
self,
|
path,
|
index_ds: str = None,
|
frontend=None,
|
tokenizer=None,
|
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)
|
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
|
self.sos = kwargs.get("sos", "<|startoftranscript|>")
|
self.eos = kwargs.get("eos", "<|endoftext|>")
|
|
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]
|
speech = speech.permute(0, 2, 1)
|
target = item["target"]
|
if self.preprocessor_text:
|
target = self.preprocessor_text(target)
|
|
task = item.get("prompt", "<|ASR|>")
|
text_language = item.get("text_language", "<|zh|>")
|
|
prompt = f"{self.sos}{task}{text_language}"
|
prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
|
prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
|
|
target_ids = self.tokenizer.encode(target, allowed_special="all")
|
target_ids_len = len(target_ids) + 1 # [lid, text]
|
|
eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
|
|
ids = prompt_ids + target_ids + eos
|
ids_lengths = len(ids)
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
|
|
target_mask = (
|
[0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
|
) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
|
target_mask = torch.tensor(target_mask, dtype=torch.float32)
|
|
return {
|
"speech": speech[0, :, :],
|
"speech_lengths": speech_lengths,
|
"text": text,
|
"text_lengths": text_lengths,
|
"target_mask": target_mask,
|
}
|
|
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
|
