import logging
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import re
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import torch
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import random
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import traceback
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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", "OpenAIDataset")
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class OpenAIDataset(torch.utils.data.Dataset):
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"""
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SenseVoiceDataset
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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.int_pad_value = int_pad_value
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self.float_pad_value = float_pad_value
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self.sos = kwargs.get("sos", "<|startoftranscript|>")
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self.eos = kwargs.get("eos", "<|endoftext|>")
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self.batch_size = kwargs.get("batch_size")
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self.batch_type = kwargs.get("batch_type")
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self.prompt_ids_len = 0
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self.retry = kwargs.get("retry", 10)
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self.permute = False
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from funasr.frontends.whisper_frontend import WhisperFrontend
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if isinstance(self.frontend, WhisperFrontend):
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self.permute = True
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self.pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
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# self.kwargs = kwargs
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self.max_token_length = kwargs.get("max_token_length", 1024)
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self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5)
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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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# import pdb;
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# pdb.set_trace()
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output = None
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for idx in range(self.retry):
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badcase_flag = False
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if idx == 0:
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index_cur = index
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else:
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index_cur = torch.randint(0, len(self.index_ds), ()).item()
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item = self.index_ds[index_cur]
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system = item["system"]
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user = item["user"]
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assistant = item["assistant"]
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input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg = [], [], [], [], [], []
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for i, (system_prompt, user_prompt, target_out) in enumerate(
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zip(system, user, assistant)
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):
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source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
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splits = self.pattern.split(source_input)
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source_ids = []
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fbank_mask_i = []
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fbank_beg_i = []
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fbank_lens_i = []
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for k, sub_str in enumerate(splits):
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if not sub_str.startswith("<|startofspeech|>"):
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sub_token = self.tokenizer.encode(sub_str)
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source_ids += sub_token
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fbank_mask_i += [0] * len(sub_token)
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else:
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sub_str = sub_str.replace("<|startofspeech|>", "").replace(
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"<|endofspeech|>", ""
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)
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if sub_str.startswith("!"):
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try:
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data_src = load_audio_text_image_video(sub_str[1:], fs=self.fs)
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except Exception as e:
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logging.error(
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f"Loading wav failed! {str(e)}, {traceback.format_exc()}"
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)
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badcase_flag = True
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continue
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speech, speech_lengths = extract_fbank(
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data_src,
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data_type=self.data_type,
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frontend=self.frontend,
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is_final=True,
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) # speech: [b, T, d]
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if self.permute:
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speech = speech.permute(0, 2, 1)
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# if speech_lengths > self.batch_size:
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# continue
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olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
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olens = 1 + (olens - 3 + 2 * 1) // 2
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sub_token_len = (olens - 1) // 2 + 1
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sub_token = [0] * sub_token_len
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fbank_beg_i = [len(source_ids)]
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source_ids += sub_token
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fbank_mask_i += [1] * len(sub_token)
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if badcase_flag:
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continue
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source_mask = [-100] * len(source_ids)
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target_out = f"{target_out}<|im_end|>"
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target_ids = self.tokenizer.encode(target_out)
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input_ids += source_ids + target_ids
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labels += source_mask + target_ids
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fbank_mask += fbank_mask_i
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fbank_beg.append(fbank_beg_i)
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if len(input_ids) > self.max_token_length:
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logging.info(
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f"input_ids > max_token_length: {len(input_ids)}>{self.max_token_length}, {item}"
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)
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badcase_flag = True
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if badcase_flag:
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continue
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input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length]
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attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
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labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
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fbank = speech[0, :, :]
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fbank_lens = speech_lengths
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fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
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fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
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output = {
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"speech": fbank,
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"speech_lengths": fbank_lens,
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"fbank_mask": fbank_mask,
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"fbank_beg": fbank_beg,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels_ids": labels,
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}
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break
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return output
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def collator(self, samples: list = None):
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for idx in range(self.retry):
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badcase_flag = False
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outputs = {}
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for sample in samples:
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if sample is None:
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continue
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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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if self.batch_type != "example":
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b, t = outputs["input_ids"].shape
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if b * t > self.batch_size * self.batch_size_scale_ratio_max:
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beg = torch.randint(0, 2, ()).item()
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if b < 2:
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beg = 0
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logging.info(
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f"Warning, b * t: {b * t} > {self.batch_size}, b: {b}, t: {t}, drop half data {idx}th, beg:{beg}"
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)
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samples = samples[beg : beg + b : 2]
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continue
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break
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return outputs
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