From f57b68121a526baea43b2e93f4540d8a2995f633 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 29 四月 2024 15:15:24 +0800
Subject: [PATCH] batch
---
funasr/datasets/audio_datasets/index_ds.py | 277 +++++++++++++++++++------------------------------------
1 files changed, 97 insertions(+), 180 deletions(-)
diff --git a/funasr/datasets/audio_datasets/index_ds.py b/funasr/datasets/audio_datasets/index_ds.py
index 3270531..70581e8 100644
--- a/funasr/datasets/audio_datasets/index_ds.py
+++ b/funasr/datasets/audio_datasets/index_ds.py
@@ -2,226 +2,143 @@
import json
import torch
import logging
-import concurrent.futures
+
import librosa
+import random
import torch.distributed as dist
from funasr.register import tables
-# @tables.register("index_ds_classes", "IndexDSJsonlRankSplit")
-# class IndexDSJsonlRankSplit(torch.utils.data.Dataset):
-#
-# def __init__(self, path):
-# super().__init__()
-#
-# contents = []
-# with open(path, encoding='utf-8') as fin:
-# for line in fin:
-# data = json.loads(line.strip())
-# if "text" in data: # for sft
-# self.contents.append(data['text'])
-# if "source" in data: # for speech lab pretrain
-# prompt = data["prompt"]
-# source = data["source"]
-# target = data["target"]
-# source_len = data["source_len"]
-# target_len = data["target_len"]
-#
-# contents.append({"source": source,
-# "prompt": prompt,
-# "target": target,
-# "source_len": source_len,
-# "target_len": target_len,
-# }
-# )
-#
-# self.contents = []
-# total_num = len(contents)
-# try:
-# rank = dist.get_rank()
-# world_size = dist.get_world_size()
-# except:
-# rank = 0
-# world_size = 1
-# logging.warning("distributed is not initialized, only single shard")
-# num_per_rank = total_num // world_size
-#
-# # rank = 0
-# # import ipdb; ipdb.set_trace()
-# self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]
-#
-# logging.info("in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(rank, len(self.contents), len(contents)))
-#
-# def __len__(self):
-# return len(self.contents)
-#
-# def __getitem__(self, index):
-# try:
-# data = self.contents[index]
-# except:
-# print(index)
-# return data
-#
-# def get_source_len(self, data_dict):
-# return data_dict["source_len"]
-#
-# def get_target_len(self, data_dict):
-#
-# return data_dict["target_len"] if "target_len" in data_dict else 0
-
@tables.register("index_ds_classes", "IndexDSJsonl")
@tables.register("index_ds_classes", "IndexDSJsonlRankFull")
-class IndexDSJsonlRankFull(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)
- if isinstance(path, (list, tuple)): # wav.scp, text.txt/text.trans
- from funasr.datasets.audio_datasets.scp2jsonl import gen_jsonl_from_wav_text_list
- jsonl_outdir = os.path.dirname(path[0])
- jsonl_name = "datalist_train.jsonl" if kwargs.get("is_training", True) else "datalist_val.jsonl"
- jsonl_file_out = os.path.join(jsonl_outdir, jsonl_name)
- if not os.path.exists(jsonl_file_out):
- print(f"datalist is: {path}, generate jsonl from it")
- gen_jsonl_from_wav_text_list(path, jsonl_file_out=jsonl_file_out, **kwargs)
- path = jsonl_file_out
-
- contents = []
- with open(path, encoding='utf-8') as fin:
- for line in fin:
- data = json.loads(line.strip())
- if "text" in data: # for sft
- contents.append(data['text'])
- if "source" in data: # for speech lab pretrain
- prompt = data.get("prompt", "<ASR>")
- source = data["source"]
- target = data["target"]
- source_len = data.get("source_len", 1)
- target_len = data.get("target_len", 0)
- if "aishell" in source:
- target = target.replace(" ", "")
- if source_len < self.min_source_length or source_len > self.max_source_length:
- continue
- if target_len < self.min_target_length or target_len > self.max_target_length:
- continue
- contents_i = {"source": source,
- "prompt": prompt,
- "target": target,
- "source_len": source_len,
- "target_len": target_len,
- }
- text_language = data.get("text_language", None)
- if text_language is not None:
- contents_i["text_language"] = text_language
- audio_language = data.get("audio_language", None)
- if audio_language is not None:
- contents_i["audio_language"] = audio_language
- contents.append(contents_i)
-
- self.contents = contents
-
- logging.info(
- "total_num of samplers across ranks: {}".format(len(self.contents)))
-
- def __len__(self):
- return len(self.contents)
-
- def __getitem__(self, index):
- try:
- data = self.contents[index]
- except:
- print(index)
- return data
-
- def get_source_len(self, data_dict):
- return data_dict.get("source_len", 1)
-
- def get_target_len(self, data_dict):
-
- return data_dict.get("target_len", 0)
-
-
@tables.register("index_ds_classes", "IndexDSJsonlRankSplit")
-class IndexDSJsonlRankSplit(torch.utils.data.Dataset):
-
+class IndexDSJsonlRankFull(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)
- with open(path, encoding='utf-8') as fin:
- file_list = fin.readlines()
+ 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)
- total_num = len(file_list)
- try:
- rank = dist.get_rank()
- world_size = dist.get_world_size()
- except:
- rank = 0
- world_size = 1
- logging.warning("distributed is not initialized, only single shard")
- num_per_rank = total_num // world_size
- if num_per_rank * world_size < total_num:
- logging.warning(f"Warning, jsonl file:{total_num} could not be divided by world_size: {world_size}, {path}")
+ 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()
- file_list_rank = file_list[rank * num_per_rank:(rank + 1) * num_per_rank]
+ num_per_slice = (len(file_list_all) - 1) // data_split_num + 1
+ 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]
+
+ # total_num = len(file_list)
+ # try:
+ # rank = dist.get_rank()
+ # world_size = dist.get_world_size()
+ # except:
+ # rank = 0
+ # world_size = 1
+ # logging.info("distributed is not initialized, only single shard")
+ #
+ # if not kwargs.get("rank_split", False):
+ # logging.info(f"Warning, rank_split disenabled, batch and shuffle data in global")
+ # rank = 0
+ # world_size = 1
+ #
+ # num_per_rank = total_num // world_size
+ # if num_per_rank * world_size < total_num:
+ # logging.info(f"Warning, jsonl file:{total_num} could not be divided by world_size: {world_size}, {path}")
+ # total_num_needed = num_per_rank * world_size
+ #
+ # extra_num = total_num_needed - total_num
+ # file_list_tmp = random.choices(file_list, k=extra_num)
+ # file_list += file_list_tmp
+ # logging.info(f"Warning, after random choices: {file_list}")
+ #
+ # file_list_rank = file_list[rank * num_per_rank:(rank + 1) * num_per_rank]
+ #
+ # logging.info(
+ # f"is_training: {is_training}, file_list_rank: {file_list_rank}")
+
+ # contents = []
+ # for file_json in file_list_rank:
contents = []
- for file_json in file_list_rank:
-
- with open(file_json.strip(), encoding='utf-8') as fin:
+ 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())
if "text" in data: # for sft
- contents.append(data['text'])
+ contents.append(data["text"])
if "source" in data: # for speech lab pretrain
prompt = data.get("prompt", "<ASR>")
- source = data["source"].replace("/cpfs01", "/cpfs_speech/data")
+ source = data["source"].replace(
+ "/cpfs01", "/cpfs_speech/data"
+ ) # only use in alibaba gpu group: .replace("/cpfs01", "/cpfs_speech/data")
target = data["target"]
source_len = data.get("source_len", 1)
target_len = data.get("target_len", 0)
-
- if source_len < self.min_source_length or source_len > self.max_source_length:
+ if "aishell" in source:
+ target = target.replace(" ", "")
+ if (
+ source_len < self.min_source_length
+ or source_len > self.max_source_length
+ ):
continue
- if target_len < self.min_target_length or target_len > self.max_target_length:
+ if (
+ target_len < self.min_target_length
+ or target_len > self.max_target_length
+ ):
continue
- contents_i = {"source": source,
- "prompt": prompt,
- "target": target,
- "source_len": source_len,
- "target_len": target_len,
- }
+
+ if (source_len + target_len) > self.max_token_length:
+ continue
+
+ contents_i = {
+ "source": source,
+ "prompt": prompt,
+ "target": target,
+ "source_len": source_len,
+ "target_len": target_len,
+ }
text_language = data.get("text_language", None)
if text_language is not None:
contents_i["text_language"] = text_language
- audio_language = data.get("audio_language", None)
- if audio_language is not None:
- contents_i["audio_language"] = audio_language
+ # audio_language = data.get("audio_language", None)
+ # if audio_language is not None:
+ # contents_i["audio_language"] = audio_language
contents.append(contents_i)
-
+
self.contents = contents
-
- logging.info(f"total_num: {len(self.contents)} of samplers in ranks: {rank}")
-
+
+ logging.info("total_num of samplers: {}, {}".format(len(self.contents), path))
+
def __len__(self):
return len(self.contents)
-
+
def __getitem__(self, index):
- try:
- data = self.contents[index]
- except:
- print(index)
+
+ data = self.contents[index]
+
return data
-
+
def get_source_len(self, data_dict):
return data_dict.get("source_len", 1)
-
+
def get_target_len(self, data_dict):
-
+
return data_dict.get("target_len", 0)
--
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