437 lines
17 KiB
Python
437 lines
17 KiB
Python
import torch
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import copy
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from funasr.register import tables
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from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
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@tables.register("dataset_classes", "AudioLLMNARDataset")
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class AudioLLMNARDataset(torch.utils.data.Dataset):
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"""
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AudioLLMDataset
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"""
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def __init__(
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self,
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path,
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index_ds: str = None,
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frontend=None,
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tokenizer=None,
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int_pad_value: int = -1,
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float_pad_value: float = 0.0,
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**kwargs
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):
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super().__init__()
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index_ds_class = tables.index_ds_classes.get(index_ds)
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self.index_ds = index_ds_class(path, **kwargs)
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preprocessor_speech = kwargs.get("preprocessor_speech", None)
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if preprocessor_speech:
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preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
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preprocessor_speech = preprocessor_speech_class(
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**kwargs.get("preprocessor_speech_conf", {})
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)
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self.preprocessor_speech = preprocessor_speech
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preprocessor_text = kwargs.get("preprocessor_text", None)
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if preprocessor_text:
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preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
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preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
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self.preprocessor_text = preprocessor_text
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self.frontend = frontend
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self.fs = 16000 if frontend is None else frontend.fs
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self.data_type = "sound"
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self.tokenizer = tokenizer
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self.float_pad_value = float_pad_value
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self.prompt = kwargs.get("prompt", "Please copy the following text.")
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self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
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self.prompt
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) # "USER: \nINSTRUCTION: {}\nINPUT: {}\nASSISTANT: "
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self.prompt_af = ""
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self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
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self.int_pad_value = self.IGNORE_INDEX
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def get_source_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_source_len(item)
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def get_target_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_target_len(item)
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def __len__(self):
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return len(self.index_ds)
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def __getitem__(self, index):
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item = self.index_ds[index]
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# import pdb;
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# pdb.set_trace()
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source = item["source"]
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src, fs=self.fs)
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speech, speech_lengths = extract_fbank(
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data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
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) # speech: [b, T, d]
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speech = speech.squeeze(0)
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target = item["target"]
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if self.preprocessor_text:
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target = self.preprocessor_text(target)
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prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt]
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prompt_ids_length = len(prompt_ids_pre)
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# bos prompt audio bos target
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# prompt_input = "{}{}".format(self.prompt_pre, target)
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# prompt_input_ids = self.tokenizer.encode(prompt_input) #[bos, prompt, input]
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# audio_length = len(prompt_input_ids) - prompt_ids_length
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target_ids = self.tokenizer.encode(target)
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if target_ids[0] == self.tokenizer.bos_token_id:
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target_ids = target_ids[1:]
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target_ids_length = len(target_ids)
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audio_length = target_ids_length
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input_ids = (
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prompt_ids_pre + target_ids + [self.tokenizer.pad_token_id] + target_ids
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) # [bos, prompt, input, pad, target]
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input_ids = torch.tensor(
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copy.deepcopy(input_ids), dtype=torch.int64
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) # [bos, prompt, input, pad, target]
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input_ids[prompt_ids_length : prompt_ids_length + audio_length] = (
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-1
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) # [bos, prompt,-1, pad, target] # it is no need, only for check
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attention_mask = input_ids.ge(-1) # [true, true, true, true, true], length mask
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# bos prompt audio target eos
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# prompt_answer = "{}{}".format(self.prompt_pre, target)
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# prompt_answer_ids = self.tokenizer.encode(prompt_answer) #[bos, prompt, input]
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# answer_length = len(prompt_answer_ids) - prompt_ids_length
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target_ids = self.tokenizer.encode(target)
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if target_ids[0] == self.tokenizer.bos_token_id:
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target_ids = target_ids[1:]
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# target_ids_length = len(target_ids)
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labels_ids = (
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prompt_ids_pre + target_ids + target_ids + [self.tokenizer.eos_token_id]
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) # [bos, prompt, input, target, eos]
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labels_ids = torch.tensor(
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copy.deepcopy(labels_ids), dtype=torch.int64
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) # [bos, prompt, input, target, eos]
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labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, target, eos]
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label_mask = labels_ids.ge(0) # [false, false, true, true, true], length mask
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labels_ids[~label_mask] = self.IGNORE_INDEX # [-1, -1, input, target, eos]
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audio_mask = (
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[0] * prompt_ids_length + [1] * audio_length + [0] * target_ids_length + [0]
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) # [0, 0, 1, 0, 0]
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audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
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ids = target_ids # self.tokenizer.encode(target) # token ids is different from labels_ids
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text = torch.tensor(ids, dtype=torch.int64)
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text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
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prompt_bos_length = torch.tensor([len(prompt_ids_pre)], dtype=torch.int32)
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return {
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"speech": speech,
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels_ids": labels_ids,
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"label_mask": label_mask,
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"audio_mask": audio_mask,
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"prompt_bos_length": prompt_bos_length,
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}
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def collator(self, samples: list = None):
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outputs = {}
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for sample in samples:
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for key in sample.keys():
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if isinstance(data_list[0], torch.Tensor):
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if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(
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data_list, batch_first=True, padding_value=pad_value
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)
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return outputs
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@tables.register("dataset_classes", "AudioLLMDataset")
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class AudioLLMDataset(torch.utils.data.Dataset):
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"""
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AudioLLMDataset
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"""
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def __init__(
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self,
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path,
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index_ds: str = None,
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frontend=None,
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tokenizer=None,
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int_pad_value: int = -1,
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float_pad_value: float = 0.0,
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**kwargs
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):
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super().__init__()
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index_ds_class = tables.index_ds_classes.get(index_ds)
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self.index_ds = index_ds_class(path, **kwargs)
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preprocessor_speech = kwargs.get("preprocessor_speech", None)
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if preprocessor_speech:
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preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
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preprocessor_speech = preprocessor_speech_class(
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**kwargs.get("preprocessor_speech_conf", {})
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)
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self.preprocessor_speech = preprocessor_speech
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preprocessor_text = kwargs.get("preprocessor_text", None)
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if preprocessor_text:
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preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
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preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
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self.preprocessor_text = preprocessor_text
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self.frontend = frontend
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self.fs = 16000 if frontend is None else frontend.fs
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self.data_type = "sound"
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self.tokenizer = tokenizer
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self.float_pad_value = float_pad_value
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self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
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self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
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self.prompt
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) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
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self.prompt_af = ""
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self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
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self.int_pad_value = self.IGNORE_INDEX
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def get_source_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_source_len(item)
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def get_target_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_target_len(item)
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def __len__(self):
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return len(self.index_ds)
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def __getitem__(self, index):
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item = self.index_ds[index]
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# import pdb;
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# pdb.set_trace()
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source = item["source"]
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src, fs=self.fs)
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speech, speech_lengths = extract_fbank(
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data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
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) # speech: [b, T, d]
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speech = speech.squeeze(0)
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target = item["target"]
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if self.preprocessor_text:
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target = self.preprocessor_text(target)
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prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt]
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prompt_ids_length = len(prompt_ids_pre)
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prompt_input = "{}{}".format(self.prompt_pre, target)
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prompt_input_ids = self.tokenizer.encode(prompt_input)
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audio_length = len(prompt_input_ids) - prompt_ids_length
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input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
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input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad]
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input_ids[prompt_ids_length:] = -1 # [bos, prompt,-1,-1]
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attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask
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prompt_answer = "{}{}".format(self.prompt_pre, target)
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prompt_answer_ids = self.tokenizer.encode(prompt_answer)
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answer_length = len(prompt_answer_ids) - prompt_ids_length
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labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
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labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos]
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labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, eos]
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label_mask = labels_ids.ge(0) # [False,False,True,True]
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labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos]
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audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0]
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audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
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ids = self.tokenizer.encode(target) # token ids is different from labels_ids
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text = torch.tensor(ids, dtype=torch.int64)
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text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
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return {
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"speech": speech,
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels_ids": labels_ids,
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"label_mask": label_mask,
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"audio_mask": audio_mask,
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}
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def collator(self, samples: list = None):
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outputs = {}
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for sample in samples:
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for key in sample.keys():
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if isinstance(data_list[0], torch.Tensor):
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if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(
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data_list, batch_first=True, padding_value=pad_value
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)
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return outputs
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@tables.register("dataset_classes", "AudioLLMARDataset")
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class AudioLLMARDataset(torch.utils.data.Dataset):
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"""
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AudioLLMDataset
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"""
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def __init__(
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self,
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path,
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index_ds: str = None,
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frontend=None,
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tokenizer=None,
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int_pad_value: int = -1,
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float_pad_value: float = 0.0,
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**kwargs
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):
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super().__init__()
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index_ds_class = tables.index_ds_classes.get(index_ds)
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self.index_ds = index_ds_class(path, **kwargs)
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preprocessor_speech = kwargs.get("preprocessor_speech", None)
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if preprocessor_speech:
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preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
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preprocessor_speech = preprocessor_speech_class(
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**kwargs.get("preprocessor_speech_conf", {})
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)
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self.preprocessor_speech = preprocessor_speech
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preprocessor_text = kwargs.get("preprocessor_text", None)
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if preprocessor_text:
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preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
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preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
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self.preprocessor_text = preprocessor_text
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self.frontend = frontend
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self.fs = 16000 if frontend is None else frontend.fs
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self.data_type = "sound"
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self.tokenizer = tokenizer
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self.float_pad_value = float_pad_value
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self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
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self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
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self.prompt
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) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
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self.prompt_af = ""
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self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
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self.int_pad_value = self.IGNORE_INDEX
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def get_source_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_source_len(item)
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def get_target_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_target_len(item)
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def __len__(self):
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return len(self.index_ds)
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def __getitem__(self, index):
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item = self.index_ds[index]
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# import pdb;
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# pdb.set_trace()
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source = item["source"]
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src, fs=self.fs)
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speech, speech_lengths = extract_fbank(
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data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
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) # speech: [b, T, d]
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speech = speech.squeeze(0)
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target = item["target"]
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if self.preprocessor_text:
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target = self.preprocessor_text(target)
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prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt]
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prompt_ids_length = len(prompt_ids_pre)
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prompt_input = "{}{}".format(self.prompt_pre, target)
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prompt_input_ids = self.tokenizer.encode(prompt_input)
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audio_length = len(prompt_input_ids) - prompt_ids_length
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input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
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input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad]
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input_ids[prompt_ids_length:] = -1 # [bos, prompt,-1,-1]
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attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask
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prompt_answer = "{}{}".format(self.prompt_pre, target)
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prompt_answer_ids = self.tokenizer.encode(prompt_answer)
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answer_length = len(prompt_answer_ids) - prompt_ids_length
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labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
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labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos]
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labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, eos]
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label_mask = labels_ids.ge(0) # [False,False,True,True]
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labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos]
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audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0]
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audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
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ids = self.tokenizer.encode(target) # token ids is different from labels_ids
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text = torch.tensor(ids, dtype=torch.int64)
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text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
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return {
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"speech": speech,
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels_ids": labels_ids,
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"label_mask": label_mask,
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"audio_mask": audio_mask,
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}
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def collator(self, samples: list = None):
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outputs = {}
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for sample in samples:
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for key in sample.keys():
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if isinstance(data_list[0], torch.Tensor):
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if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(
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data_list, batch_first=True, padding_value=pad_value
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)
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return outputs
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