From 27256ed429c95ed8868a01f8555610393dd7b3a1 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 06 六月 2024 15:45:32 +0800
Subject: [PATCH] auto frontend
---
funasr/datasets/sense_voice_datasets/datasets.py | 1
funasr/models/llm_asr/model.py | 318 ++++++++++++++++++++++++++++
funasr/datasets/openai_datasets/datasets.py | 216 +++++++++++++++++++
funasr/datasets/openai_datasets/index_ds.py | 95 ++++++++
funasr/datasets/openai_datasets/__init__.py | 0
5 files changed, 630 insertions(+), 0 deletions(-)
diff --git a/funasr/datasets/openai_datasets/__init__.py b/funasr/datasets/openai_datasets/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/datasets/openai_datasets/__init__.py
diff --git a/funasr/datasets/openai_datasets/datasets.py b/funasr/datasets/openai_datasets/datasets.py
new file mode 100644
index 0000000..9a542ad
--- /dev/null
+++ b/funasr/datasets/openai_datasets/datasets.py
@@ -0,0 +1,216 @@
+import logging
+import re
+import torch
+import random
+import traceback
+from funasr.register import tables
+from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
+
+
+@tables.register("dataset_classes", "OpenAIDataset")
+class OpenAIDataset(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|>")
+ self.batch_size = kwargs.get("batch_size")
+ self.batch_type = kwargs.get("batch_type")
+ self.prompt_ids_len = 0
+ self.retry = kwargs.get("retry", 5)
+
+ self.permute = False
+ from funasr.frontends.whisper_frontend import WhisperFrontend
+
+ if isinstance(self.frontend, WhisperFrontend):
+ self.permute = True
+
+ self.pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
+
+ 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):
+ # import pdb;
+ # pdb.set_trace()
+
+ output = None
+ for idx in range(self.retry):
+ if idx == 0:
+ index_cur = index
+ else:
+ index_cur = torch.randint(0, len(self.index_ds), ()).item()
+
+ item = self.index_ds[index_cur]
+
+ system = item["system"]
+ user = item["user"]
+ assistant = item["assistant"]
+
+ input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg = [], [], [], [], [], []
+
+ for i, (system_prompt, user_prompt, target_out) in enumerate(
+ zip(system, user, assistant)
+ ):
+
+ 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"
+
+ splits = self.pattern.split(source_input)
+ source_ids = []
+ fbank_mask_i = []
+ fbank_beg_i = []
+ fbank_lens_i = []
+ for k, sub_str in enumerate(splits):
+ if not sub_str.startswith("<|startofspeech|>"):
+ sub_token = self.tokenizer.encode(sub_str)
+ source_ids += sub_token
+ fbank_mask_i += [0] * len(sub_token)
+ else:
+ sub_str = sub_str.replace("<|startofspeech|>", "").replace(
+ "<|endofspeech|>", ""
+ )
+ if sub_str.startswith("!"):
+
+ data_src = load_audio_text_image_video(sub_str[1:], 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]
+ if self.permute:
+ speech = speech.permute(0, 2, 1)
+ if speech_lengths > self.batch_size:
+ continue
+
+ fbank_lens = speech_lengths[0].item()
+ olens = 1 + (fbanks_len - 3 + 2 * 1) // 2
+ olens = 1 + (olens - 3 + 2 * 1) // 2
+ sub_token_len = (olens - 1) // 2 + 1
+ sub_token = [0] * sub_token_len[0]
+ fbank_beg_i = [len(source_ids)]
+ source_ids += sub_token
+ fbank_mask_i += [1] * len(sub_token)
+
+ source_mask = [-100] * len(source_ids)
+ target_out = f"{target_out}<|im_end|>"
+ target_ids = tokenizer.encode(target_out)
+ input_ids += source_ids + target_ids
+ labels += source_mask + target_ids
+ fbank_mask += fbank_mask_i
+ fbank_beg.append(fbank_beg_i)
+
+ input_ids = torch.tensor(input_ids, dtype=torch.int64)
+ attention_mask = torch.tensor([len(input_ids)], dtype=torch.int32)
+ labels = torch.tensor(labels, dtype=torch.int64)
+
+ fbank = speech[0, :, :]
+ fbank_lens = speech_lengths
+ fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
+ fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
+
+ output = {
+ "speech": fbank,
+ "speech_lengths": fbank_lens,
+ "fbank_mask": fbank_mask,
+ "fbank_beg": fbank_beg,
+ "input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "labels_ids": labels,
+ }
+ break
+
+ return output
+
+ def collator(self, samples: list = None):
+ outputs = {}
+ for sample in samples:
+ if sample is None:
+ continue
+ 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
+ )
+
+ if self.batch_type != "example":
+ for i in range(10):
+ outputs = self._filter_badcase(outputs, i=i)
+
+ return outputs
+
+ def _filter_badcase(self, outputs, i=0):
+ b, t, _ = outputs["speech"].shape
+
+ if b * t > self.batch_size * 1.25:
+ beg = torch.randint(0, 2, ()).item()
+ if b < 2:
+ beg = 0
+ logging.info(
+ f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
+ )
+ for key, data_list in outputs.items():
+ outputs[key] = outputs[key][beg : beg + b : 2]
+
+ speech_lengths_max = outputs["speech_lengths"].max().item()
+ outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
+ text_lengths_max = outputs["text_lengths"].max().item()
+ outputs["text"] = outputs["text"][:, :text_lengths_max]
+ target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
+ outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
+
+ return outputs
diff --git a/funasr/datasets/openai_datasets/index_ds.py b/funasr/datasets/openai_datasets/index_ds.py
new file mode 100644
index 0000000..1c48cd2
--- /dev/null
+++ b/funasr/datasets/openai_datasets/index_ds.py
@@ -0,0 +1,95 @@
+import os
+import json
+import torch
+import logging
+
+import librosa
+import random
+import torch.distributed as dist
+
+from funasr.register import tables
+
+
+@tables.register("index_ds_classes", "OpenAIIndexDSJsonl")
+class OpenAIIndexDSJsonl(torch.utils.data.Dataset): # torch.utils.data.Dataset
+
+ def __init__(self, path: str, **kwargs):
+ super().__init__()
+ self.max_source_length = kwargs.get("max_source_length", 2048)
+ self.min_source_length = kwargs.get("min_source_length", 0)
+ self.max_target_length = kwargs.get("max_target_length", 2048)
+ self.min_target_length = kwargs.get("min_target_length", 0)
+ self.max_token_length = kwargs.get("max_token_length", 2200)
+
+ is_training = kwargs.get("is_training", True)
+ if not (path.endswith(".jsonl") or path.endswith(".json")):
+ # jsonl list file
+ data_split_num = kwargs.get("data_split_num", 1)
+ data_split_i = kwargs.get("data_split_i", 0)
+
+ if not is_training:
+ data_split_num = 1
+ data_split_i = 0
+ with open(path, encoding="utf-8") as fin:
+ file_list_all = fin.readlines()
+
+ num_per_slice = (len(file_list_all) - 1) // data_split_num + 1 # 16
+ file_list = file_list_all[
+ data_split_i * num_per_slice : (data_split_i + 1) * num_per_slice
+ ]
+ logging.info(
+ f"is_training: {is_training}, data_split_num: {data_split_num}, data_split_i: {data_split_i}, \nfile_list: {file_list}, \nfile_list_all: {file_list_all}"
+ )
+
+ else:
+ file_list = [path]
+
+ contents = []
+ for file_json in file_list:
+ with open(file_json.strip(), encoding="utf-8") as fin:
+ for line in fin:
+ data = json.loads(line.strip())["messages"]
+
+ system, user, assistant = [], [], []
+ for i, item in enumerate(data):
+ role = item["role"]
+ content = item["content"]
+ if role == "system":
+ system.append(content)
+ elif role == "user":
+ user.append(content)
+ elif role == "assistant":
+ assistant.append(content)
+
+ system = system * len(user)
+
+ contents_i = {"system": system, "user": user, "assistant": assistant}
+ contents.append(contents_i)
+
+ self.contents = contents
+
+ logging.info("total_num of samplers: {}, {}".format(len(self.contents), path))
+
+ def __len__(self):
+ return len(self.contents)
+
+ def __getitem__(self, index):
+
+ data = self.contents[index]
+
+ return data
+
+ def get_source_len(self, data_dict):
+ return len(data_dict["system"]) + len(data_dict["user"])
+
+ def get_target_len(self, data_dict):
+
+ return len(data_dict["assistant"])
+
+
+if __name__ == "__main__":
+ index_ds = OpenAIIndexDSJsonl(
+ path="/Users/zhifu/funasr1.0/test_local/data_tmp/tmp_wav_10.jsonl"
+ )
+ print(index_ds.contents)
+ pass
diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 690a1c5..c0beda1 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -1,5 +1,6 @@
import logging
+import re
import torch
import random
import traceback
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 4345f69..11db009 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -341,3 +341,321 @@
ibest_writer["text"][key[0]] = text
return results, meta_data
+
+
+@tables.register("model_classes", "LLMASR2")
+class LLMASR2(nn.Module):
+ """ """
+
+ def __init__(
+ self,
+ specaug: str = None,
+ specaug_conf: dict = None,
+ normalize: str = None,
+ normalize_conf: dict = None,
+ audio_encoder: str = None,
+ audio_encoder_conf: dict = None,
+ audio_adaptor: str = None,
+ audio_adaptor_conf: dict = None,
+ decoder: str = None,
+ decoder_conf: dict = None,
+ ctc: str = None,
+ ctc_conf: dict = None,
+ ctc_weight: float = 0.5,
+ llm: str = None,
+ llm_conf: dict = None,
+ input_size: int = 80,
+ vocab_size: int = -1,
+ ignore_id: int = -1,
+ blank_id: int = 0,
+ sos: int = 1,
+ eos: int = 2,
+ lsm_weight: float = 0.0,
+ length_normalized_loss: bool = False,
+ report_cer: bool = True,
+ report_wer: bool = True,
+ sym_space: str = "<space>",
+ sym_blank: str = "<blank>",
+ # extract_feats_in_collect_stats: bool = True,
+ share_embedding: bool = False,
+ # preencoder: Optional[AbsPreEncoder] = None,
+ # postencoder: Optional[AbsPostEncoder] = None,
+ **kwargs,
+ ):
+
+ super().__init__()
+
+ if specaug is not None:
+ specaug_class = tables.specaug_classes.get(specaug)
+ specaug = specaug_class(**specaug_conf)
+ if normalize is not None:
+ normalize_class = tables.normalize_classes.get(normalize)
+ normalize = normalize_class(**normalize_conf)
+
+ # audio encoder
+ hub = audio_encoder_conf.get("hub", None)
+ if hub == "ms":
+ from funasr import AutoModel
+
+ model = AutoModel(model=audio_encoder, model_revision="master")
+ # frontend = model.kwargs.get("frontend")
+ audio_encoder_output_size = model.model.encoder_output_size
+
+ audio_encoder = model.model.model.encoder
+
+ # self.frontend = frontend
+
+ elif hub == "hf":
+ pass
+ else:
+ encoder_class = tables.encoder_classes.get(audio_encoder)
+ audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
+ audio_encoder_output_size = audio_encoder.output_size()
+ freeze = audio_encoder_conf.get("freeze", True)
+ if freeze:
+ for name, param in audio_encoder.named_parameters():
+ param.requires_grad = False
+ audio_encoder.eval()
+
+ self.audio_encoder = audio_encoder
+
+ # llm
+ hub = llm_conf.get("hub", "hf")
+ self.llm = None
+ # if hub == "hf":
+ # from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+ #
+ # init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
+ #
+ # model = AutoModelForCausalLM.from_pretrained(
+ # init_param_path,
+ # load_in_8bit=None,
+ # device_map=None,
+ # use_cache=None,
+ # )
+ # freeze = llm_conf.get("freeze", True)
+ # if freeze:
+ # for name, param in model.named_parameters():
+ # param.requires_grad = False
+ # model.eval()
+ # self.llm = model
+
+ # adaptor
+ adaptor_class = tables.adaptor_classes.get(audio_adaptor)
+ audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
+ audio_adaptor = adaptor_class(**audio_adaptor_conf)
+
+ self.audio_adaptor = audio_adaptor
+
+ self.blank_id = blank_id
+ self.sos = sos if sos is not None else vocab_size - 1
+ self.eos = eos if eos is not None else vocab_size - 1
+ self.vocab_size = vocab_size
+ self.ignore_id = ignore_id
+ self.specaug = specaug
+ self.normalize = normalize
+
+ self.criterion_att = LabelSmoothingLoss(
+ size=vocab_size,
+ padding_idx=ignore_id,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+
+ self.error_calculator = None
+
+ self.length_normalized_loss = length_normalized_loss
+ self.beam_search = None
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ input_ids: torch.Tensor,
+ attention_mask: torch.Tensor,
+ labels_ids: torch.Tensor,
+ fbank_beg: torch.Tensor,
+ fbank_mask: torch.Tensor,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ """Encoder + Decoder + Calc loss
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ """
+ # import pdb;
+ # pdb.set_trace()
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
+ batch_size = speech.shape[0]
+
+ # audio encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+ # audio_adaptor
+ encoder_out = self.audio_adaptor(encoder_out)
+
+ input_ids[input_ids == -1] = 0
+ input_ids[input_ids == -100] = 0
+ if hasattr(self.llm.model, "embed_tokens"):
+ inputs_embeds = self.llm.model.embed_tokens(input_ids)
+ elif hasattr(self.llm.model.model, "embed_tokens"):
+ inputs_embeds = self.llm.model.model.embed_tokens(input_ids)
+ else:
+ inputs_embeds = self.llm.model.model.model.embed_tokens(input_ids)
+
+ batch_size, token_num, dims = inputs_embeds.shape
+ _, l, _ = encoder_out.shape
+ for batch_idx in range(batch_size):
+ fbank_beg_idx = fbank_beg[batch_idx, 0].item()
+ inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + l, :] = encoder_out[
+ batch_idx, :l, :
+ ]
+
+ model_outputs = self.llm(
+ inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
+ )
+ loss = model_outputs.loss
+
+ stats = {}
+ with torch.no_grad():
+ preds = torch.argmax(model_outputs.logits, -1)
+ acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
+ stats["acc"] = acc_att
+
+ stats["loss"] = torch.clone(loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = int((text_lengths + 1).sum())
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+ def encode(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ **kwargs,
+ ):
+ speech = speech.permute(0, 2, 1)
+ res = self.audio_encoder(speech)
+ if isinstance(res, (list, tuple)):
+ encoder_out, encoder_out_lens = res[0], res[1]
+ else:
+ encoder_out, encoder_out_lens = res, speech_lengths
+ return encoder_out, encoder_out_lens
+
+ def inference(
+ self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+
+ prompt = kwargs.get("prompt", "Transcribe speech to text.")
+
+ if kwargs.get("batch_size", 1) > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+
+ meta_data = {}
+ if (
+ isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
+ ): # fbank
+ speech, speech_lengths = data_in, data_lengths
+ if len(speech.shape) < 3:
+ speech = speech[None, :, :]
+ if speech_lengths is None:
+ speech_lengths = speech.shape[1]
+ else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(
+ data_in,
+ fs=frontend.fs,
+ audio_fs=kwargs.get("fs", 16000),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer,
+ )
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(
+ audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
+ )
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ )
+
+ speech = speech.to(device=kwargs["device"])
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+ # Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+ # adaptor
+ encoder_out = self.audio_adaptor(encoder_out)
+
+ prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
+ prompt_ids = tokenizer.encode(prompt_pre)
+ prompt_length = len(prompt_ids)
+ prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])
+
+ if hasattr(self.llm.model, "embed_tokens"):
+ inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
+ elif hasattr(self.llm.model.model, "embed_tokens"):
+ inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
+ else:
+ inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
+
+ inputs_embeds = torch.cat(
+ (inputs_embeds[None, :, :], encoder_out), dim=1
+ ) # [prompt, audio]
+ attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
+ kwargs["device"]
+ )
+
+ preds = self.llm.generate(
+ inputs_embeds=inputs_embeds,
+ max_length=kwargs.get("max_length", 200),
+ max_new_tokens=kwargs.get("max_new_tokens", 200),
+ num_beams=kwargs.get("num_beams", 4),
+ do_sample=kwargs.get("do_sample", False),
+ min_length=kwargs.get("min_length", 1),
+ top_p=kwargs.get("top_p", 1.0),
+ repetition_penalty=kwargs.get("repetition_penalty", 1.0),
+ length_penalty=kwargs.get("length_penalty", 1.0),
+ temperature=kwargs.get("temperature", 1.0),
+ attention_mask=attention_mask,
+ bos_token_id=tokenizer.bos_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ pad_token_id=tokenizer.pad_token_id,
+ )
+
+ text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
+
+ text = text[0].split(": ")[-1]
+ text = text.strip()
+
+ # preds = torch.argmax(model_outputs.logits, -1)
+
+ ibest_writer = None
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.writer[f"{0 + 1}best_recog"]
+
+ results = []
+ result_i = {"key": key[0], "text": text}
+ results.append(result_i)
+
+ if ibest_writer is not None:
+ ibest_writer["text"][key[0]] = text
+
+ return results, meta_data
--
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