import logging
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import torch
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import random
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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", "SenseVoiceDataset")
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class SenseVoiceDataset(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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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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if speech_lengths > self.batch_size:
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return None
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speech = speech.permute(0, 2, 1)
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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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task = item.get("prompt", "<|ASR|>")
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text_language = item.get("text_language", "<|zh|>")
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prompt = f"{self.sos}{task}{text_language}"
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prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
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prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
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self.prompt_ids_len = prompt_ids_len
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target_ids = self.tokenizer.encode(target, allowed_special="all")
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target_ids_len = len(target_ids) + 1 # [lid, text]
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if target_ids_len > 200:
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return None
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eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
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ids = prompt_ids + target_ids + eos
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ids_lengths = len(ids)
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text = torch.tensor(ids, dtype=torch.int64)
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text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
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target_mask = (
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[0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
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) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
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target_mask_lengths = len(target_mask)
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target_mask = torch.tensor(target_mask, dtype=torch.float32)
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target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
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return {
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"speech": speech[0, :, :],
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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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"target_mask": target_mask,
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"target_mask_lengths": target_mask_lengths,
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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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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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if len(outputs) < 1:
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logging.info(f"ERROR: data is empty!")
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outputs = {
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"speech": torch.rand((10, 128), dtype=torch.float32),
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"speech_lengths": torch.tensor([10], dtype=torch.int32),
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"text": torch.tensor([58836], dtype=torch.int32),
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"text_lengths": torch.tensor([1], dtype=torch.int32),
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"target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]]),
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}
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return outputs
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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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for i in range(3):
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outputs = self._filter_badcase(outputs, i=i)
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return outputs
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def _filter_badcase(self, outputs, i=0):
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b, t, _ = outputs["speech"].shape
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if b * t > self.batch_size * 1.25:
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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}, drop half data {i}th, beg:{beg}"
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)
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for key, data_list in outputs.items():
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outputs[key] = outputs[key][beg : beg + b : 2]
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speech_lengths_max = outputs["speech_lengths"].max().item()
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outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
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text_lengths_max = outputs["text_lengths"].max().item()
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outputs["text"] = outputs["text"][:, :text_lengths_max]
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target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
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outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
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return outputs
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