437 lines
17 KiB
Python
437 lines
17 KiB
Python
|
import torch
|
||
|
|
import copy
|
||
|
|
|
||
|
|
from funasr.register import tables
|
||
|
|
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("dataset_classes", "AudioLLMNARDataset")
|
||
|
|
class AudioLLMNARDataset(torch.utils.data.Dataset):
|
||
|
|
"""
|
||
|
|
AudioLLMDataset
|
||
|
|
"""
|
||
|
|
|
||
|
|
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.float_pad_value = float_pad_value
|
||
|
|
self.prompt = kwargs.get("prompt", "Please copy the following text.")
|
||
|
|
self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
|
||
|
|
self.prompt
|
||
|
|
) # "USER: \nINSTRUCTION: {}\nINPUT: {}\nASSISTANT: "
|
||
|
|
self.prompt_af = ""
|
||
|
|
self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
|
||
|
|
self.int_pad_value = self.IGNORE_INDEX
|
||
|
|
|
||
|
|
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.squeeze(0)
|
||
|
|
|
||
|
|
target = item["target"]
|
||
|
|
if self.preprocessor_text:
|
||
|
|
target = self.preprocessor_text(target)
|
||
|
|
|
||
|
|
prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt]
|
||
|
|
prompt_ids_length = len(prompt_ids_pre)
|
||
|
|
|
||
|
|
# bos prompt audio bos target
|
||
|
|
# prompt_input = "{}{}".format(self.prompt_pre, target)
|
||
|
|
# prompt_input_ids = self.tokenizer.encode(prompt_input) #[bos, prompt, input]
|
||
|
|
# audio_length = len(prompt_input_ids) - prompt_ids_length
|
||
|
|
target_ids = self.tokenizer.encode(target)
|
||
|
|
if target_ids[0] == self.tokenizer.bos_token_id:
|
||
|
|
target_ids = target_ids[1:]
|
||
|
|
target_ids_length = len(target_ids)
|
||
|
|
audio_length = target_ids_length
|
||
|
|
input_ids = (
|
||
|
|
prompt_ids_pre + target_ids + [self.tokenizer.pad_token_id] + target_ids
|
||
|
|
) # [bos, prompt, input, pad, target]
|
||
|
|
input_ids = torch.tensor(
|
||
|
|
copy.deepcopy(input_ids), dtype=torch.int64
|
||
|
|
) # [bos, prompt, input, pad, target]
|
||
|
|
input_ids[prompt_ids_length : prompt_ids_length + audio_length] = (
|
||
|
|
-1
|
||
|
|
) # [bos, prompt,-1, pad, target] # it is no need, only for check
|
||
|
|
attention_mask = input_ids.ge(-1) # [true, true, true, true, true], length mask
|
||
|
|
|
||
|
|
# bos prompt audio target eos
|
||
|
|
# prompt_answer = "{}{}".format(self.prompt_pre, target)
|
||
|
|
# prompt_answer_ids = self.tokenizer.encode(prompt_answer) #[bos, prompt, input]
|
||
|
|
# answer_length = len(prompt_answer_ids) - prompt_ids_length
|
||
|
|
target_ids = self.tokenizer.encode(target)
|
||
|
|
if target_ids[0] == self.tokenizer.bos_token_id:
|
||
|
|
target_ids = target_ids[1:]
|
||
|
|
# target_ids_length = len(target_ids)
|
||
|
|
labels_ids = (
|
||
|
|
prompt_ids_pre + target_ids + target_ids + [self.tokenizer.eos_token_id]
|
||
|
|
) # [bos, prompt, input, target, eos]
|
||
|
|
labels_ids = torch.tensor(
|
||
|
|
copy.deepcopy(labels_ids), dtype=torch.int64
|
||
|
|
) # [bos, prompt, input, target, eos]
|
||
|
|
labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, target, eos]
|
||
|
|
label_mask = labels_ids.ge(0) # [false, false, true, true, true], length mask
|
||
|
|
labels_ids[~label_mask] = self.IGNORE_INDEX # [-1, -1, input, target, eos]
|
||
|
|
|
||
|
|
audio_mask = (
|
||
|
|
[0] * prompt_ids_length + [1] * audio_length + [0] * target_ids_length + [0]
|
||
|
|
) # [0, 0, 1, 0, 0]
|
||
|
|
audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
|
||
|
|
|
||
|
|
ids = target_ids # self.tokenizer.encode(target) # token ids is different from labels_ids
|
||
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
||
|
|
text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
|
||
|
|
|
||
|
|
prompt_bos_length = torch.tensor([len(prompt_ids_pre)], dtype=torch.int32)
|
||
|
|
|
||
|
|
return {
|
||
|
|
"speech": speech,
|
||
|
|
"speech_lengths": speech_lengths,
|
||
|
|
"text": text,
|
||
|
|
"text_lengths": text_lengths,
|
||
|
|
"input_ids": input_ids,
|
||
|
|
"attention_mask": attention_mask,
|
||
|
|
"labels_ids": labels_ids,
|
||
|
|
"label_mask": label_mask,
|
||
|
|
"audio_mask": audio_mask,
|
||
|
|
"prompt_bos_length": prompt_bos_length,
|
||
|
|
}
|
||
|
|
|
||
|
|
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", "AudioLLMDataset")
|
||
|
|
class AudioLLMDataset(torch.utils.data.Dataset):
|
||
|
|
"""
|
||
|
|
AudioLLMDataset
|
||
|
|
"""
|
||
|
|
|
||
|
|
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.float_pad_value = float_pad_value
|
||
|
|
self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
|
||
|
|
self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
|
||
|
|
self.prompt
|
||
|
|
) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
|
||
|
|
self.prompt_af = ""
|
||
|
|
self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
|
||
|
|
self.int_pad_value = self.IGNORE_INDEX
|
||
|
|
|
||
|
|
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.squeeze(0)
|
||
|
|
|
||
|
|
target = item["target"]
|
||
|
|
if self.preprocessor_text:
|
||
|
|
target = self.preprocessor_text(target)
|
||
|
|
|
||
|
|
prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt]
|
||
|
|
prompt_ids_length = len(prompt_ids_pre)
|
||
|
|
|
||
|
|
prompt_input = "{}{}".format(self.prompt_pre, target)
|
||
|
|
prompt_input_ids = self.tokenizer.encode(prompt_input)
|
||
|
|
audio_length = len(prompt_input_ids) - prompt_ids_length
|
||
|
|
input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
|
||
|
|
input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad]
|
||
|
|
input_ids[prompt_ids_length:] = -1 # [bos, prompt,-1,-1]
|
||
|
|
attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask
|
||
|
|
|
||
|
|
prompt_answer = "{}{}".format(self.prompt_pre, target)
|
||
|
|
prompt_answer_ids = self.tokenizer.encode(prompt_answer)
|
||
|
|
answer_length = len(prompt_answer_ids) - prompt_ids_length
|
||
|
|
labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
|
||
|
|
labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos]
|
||
|
|
labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, eos]
|
||
|
|
label_mask = labels_ids.ge(0) # [False,False,True,True]
|
||
|
|
labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos]
|
||
|
|
|
||
|
|
audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0]
|
||
|
|
audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
|
||
|
|
|
||
|
|
ids = self.tokenizer.encode(target) # token ids is different from labels_ids
|
||
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
||
|
|
text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
|
||
|
|
|
||
|
|
return {
|
||
|
|
"speech": speech,
|
||
|
|
"speech_lengths": speech_lengths,
|
||
|
|
"text": text,
|
||
|
|
"text_lengths": text_lengths,
|
||
|
|
"input_ids": input_ids,
|
||
|
|
"attention_mask": attention_mask,
|
||
|
|
"labels_ids": labels_ids,
|
||
|
|
"label_mask": label_mask,
|
||
|
|
"audio_mask": audio_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
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("dataset_classes", "AudioLLMARDataset")
|
||
|
|
class AudioLLMARDataset(torch.utils.data.Dataset):
|
||
|
|
"""
|
||
|
|
AudioLLMDataset
|
||
|
|
"""
|
||
|
|
|
||
|
|
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.float_pad_value = float_pad_value
|
||
|
|
self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
|
||
|
|
self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
|
||
|
|
self.prompt
|
||
|
|
) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
|
||
|
|
self.prompt_af = ""
|
||
|
|
self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
|
||
|
|
self.int_pad_value = self.IGNORE_INDEX
|
||
|
|
|
||
|
|
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.squeeze(0)
|
||
|
|
|
||
|
|
target = item["target"]
|
||
|
|
if self.preprocessor_text:
|
||
|
|
target = self.preprocessor_text(target)
|
||
|
|
|
||
|
|
prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt]
|
||
|
|
prompt_ids_length = len(prompt_ids_pre)
|
||
|
|
|
||
|
|
prompt_input = "{}{}".format(self.prompt_pre, target)
|
||
|
|
prompt_input_ids = self.tokenizer.encode(prompt_input)
|
||
|
|
audio_length = len(prompt_input_ids) - prompt_ids_length
|
||
|
|
input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
|
||
|
|
input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad]
|
||
|
|
input_ids[prompt_ids_length:] = -1 # [bos, prompt,-1,-1]
|
||
|
|
attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask
|
||
|
|
|
||
|
|
prompt_answer = "{}{}".format(self.prompt_pre, target)
|
||
|
|
prompt_answer_ids = self.tokenizer.encode(prompt_answer)
|
||
|
|
answer_length = len(prompt_answer_ids) - prompt_ids_length
|
||
|
|
labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
|
||
|
|
labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos]
|
||
|
|
labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, eos]
|
||
|
|
label_mask = labels_ids.ge(0) # [False,False,True,True]
|
||
|
|
labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos]
|
||
|
|
|
||
|
|
audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0]
|
||
|
|
audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
|
||
|
|
|
||
|
|
ids = self.tokenizer.encode(target) # token ids is different from labels_ids
|
||
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
||
|
|
text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
|
||
|
|
|
||
|
|
return {
|
||
|
|
"speech": speech,
|
||
|
|
"speech_lengths": speech_lengths,
|
||
|
|
"text": text,
|
||
|
|
"text_lengths": text_lengths,
|
||
|
|
"input_ids": input_ids,
|
||
|
|
"attention_mask": attention_mask,
|
||
|
|
"labels_ids": labels_ids,
|
||
|
|
"label_mask": label_mask,
|
||
|
|
"audio_mask": audio_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
|