From fcc9c89eaba9a4e36c54958aeedeec7ab3756cd7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 13 二月 2023 17:43:31 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/datasets/large_datasets/dataset.py |   37 +++++++++++++++++++++++++++++--------
 1 files changed, 29 insertions(+), 8 deletions(-)

diff --git a/funasr/datasets/large_datasets/dataset.py b/funasr/datasets/large_datasets/dataset.py
index 60c5abd..2123737 100644
--- a/funasr/datasets/large_datasets/dataset.py
+++ b/funasr/datasets/large_datasets/dataset.py
@@ -1,8 +1,10 @@
 import os
 import random
+import numpy
 from functools import partial
 
 import torch
+import torchaudio
 import torch.distributed as dist
 from kaldiio import ReadHelper
 from torch.utils.data import IterableDataset
@@ -12,6 +14,7 @@
 from funasr.datasets.large_datasets.datapipes.map import MapperIterDataPipe
 from funasr.datasets.large_datasets.utils.filter import filter
 from funasr.datasets.large_datasets.utils.padding import padding
+from funasr.datasets.large_datasets.utils.clipping import clipping
 from funasr.datasets.large_datasets.utils.tokenize import tokenize
 
 
@@ -97,9 +100,11 @@
                 if data_type == "kaldi_ark":
                     ark_reader = ReadHelper('ark:{}'.format(data_file))
                     reader_list.append(ark_reader)
-                elif data_type == "text":
+                elif data_type == "text" or data_type == "sound":
                     text_reader = open(data_file, "r")
                     reader_list.append(text_reader)
+                elif data_type == "none":
+                    continue
                 else:
                     raise TypeError("Data type {} is not supported".format(data_type))
 
@@ -109,6 +114,15 @@
                     if data_type == "kaldi_ark":
                         key, mat = item
                         sample_dict[data_name] = mat
+                        if data_name == "speech":
+                            sample_dict["key"] = key
+                    elif data_type == "sound":
+                        key, path = item.strip().split()
+                        waveform, sampling_rate = torchaudio.load(path)
+                        waveform = waveform.numpy()
+                        mat = waveform[0]
+                        sample_dict[data_name] = mat
+                        sample_dict["sampling_rate"] = sampling_rate
                         if data_name == "speech":
                             sample_dict["key"] = key
                     else:
@@ -125,13 +139,18 @@
 
 def len_fn_token(data):
     assert "speech" in data
-    return data["speech"].shape[0]
+    if "sampling_rate" in data:
+        return (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
+    else:
+        return data["speech"].shape[0]
 
 
 def Dataset(data_list_file,
             dict,
+            seg_dict,
             conf,
-            mode="train"):
+            mode="train",
+            batch_mode="padding"):
     scp_lists = read_lists(data_list_file)
     shuffle = conf.get('shuffle', True)
     data_names = conf.get("data_names", "speech,text")
@@ -142,9 +161,10 @@
     filter_fn = partial(filter, **filter_conf)
     dataset = FilterIterDataPipe(dataset, fn=filter_fn)
 
-    vocab = {'vocab': dict}
-    tokenize_fn = partial(tokenize, **vocab)
-    dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)
+    if "text" in data_names:
+        vocab = {'vocab': dict, 'seg_dict': seg_dict}
+        tokenize_fn = partial(tokenize, **vocab)
+        dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)
 
     if shuffle:
         buffer_conf = conf.get('shuffle_conf', {})
@@ -168,8 +188,9 @@
                                              batch_size=batch_size,
                                              len_fn=len_fn,
                                              buffer_size=buffer_size,
-                                             sort_size=sort_size)
+                                             sort_size=sort_size,
+                                             batch_mode=batch_mode)
 
-    dataset = MapperIterDataPipe(dataset, fn=padding)
+    dataset = MapperIterDataPipe(dataset, fn=padding if batch_mode == "padding" else clipping)
 
     return dataset

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