From d1374e9c806f82abddaec793906b74939b5c06a0 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期三, 17 五月 2023 15:15:51 +0800
Subject: [PATCH] update repo
---
funasr/utils/prepare_data.py | 176 ++++++++++++++++++++++++++++++++--------------------------
1 files changed, 96 insertions(+), 80 deletions(-)
diff --git a/funasr/utils/prepare_data.py b/funasr/utils/prepare_data.py
index 3f55170..f61e501 100644
--- a/funasr/utils/prepare_data.py
+++ b/funasr/utils/prepare_data.py
@@ -3,6 +3,7 @@
import shutil
from multiprocessing import Pool
+import kaldiio
import numpy as np
import torch.distributed as dist
import torchaudio
@@ -48,49 +49,80 @@
def calc_shape_core(root_path, args, idx):
- wav_scp_file = os.path.join(root_path, "wav.scp.{}".format(idx))
- shape_file = os.path.join(root_path, "speech_shape.{}".format(idx))
- with open(wav_scp_file) as f:
+ file_name = args.data_file_names.split(",")[0]
+ data_name = args.dataset_conf.get("data_names", "speech,text").split(",")[0]
+ scp_file = os.path.join(root_path, "{}.{}".format(file_name, idx))
+ shape_file = os.path.join(root_path, "{}_shape.{}".format(data_name, idx))
+ with open(scp_file) as f:
lines = f.readlines()
- frontend_conf = args.frontend_conf
- dataset_conf = args.dataset_conf
- speech_length_min = dataset_conf.speech_length_min if hasattr(dataset_conf, "speech_length_min") else -1
- speech_length_max = dataset_conf.speech_length_max if hasattr(dataset_conf, "speech_length_max") else -1
- with open(shape_file, "w") as f:
- for line in lines:
- sample_name, wav_path = line.strip().split()
- n_frames, feature_dim = wav2num_frame(wav_path, frontend_conf)
- write_flag = True
- if n_frames > 0 and speech_length_min > 0:
- write_flag = n_frames >= speech_length_min
- if n_frames > 0 and speech_length_max > 0:
- write_flag = n_frames <= speech_length_max
- if write_flag:
- f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim))))
+ data_type = args.dataset_conf.get("data_types", "sound,text").split(",")[0]
+ if data_type == "sound":
+ frontend_conf = args.frontend_conf
+ dataset_conf = args.dataset_conf
+ length_min = dataset_conf.speech_length_min if hasattr(dataset_conf, "{}_length_min".format(data_name)) else -1
+ length_max = dataset_conf.speech_length_max if hasattr(dataset_conf, "{}_length_max".format(data_name)) else -1
+ with open(shape_file, "w") as f:
+ for line in lines:
+ sample_name, wav_path = line.strip().split()
+ n_frames, feature_dim = wav2num_frame(wav_path, frontend_conf)
+ write_flag = True
+ if n_frames > 0 and length_min > 0:
+ write_flag = n_frames >= length_min
+ if n_frames > 0 and length_max > 0:
+ write_flag = n_frames <= length_max
+ if write_flag:
+ f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim))))
+ f.flush()
+ elif data_type == "kaldi_ark":
+ dataset_conf = args.dataset_conf
+ length_min = dataset_conf.speech_length_min if hasattr(dataset_conf, "{}_length_min".format(data_name)) else -1
+ length_max = dataset_conf.speech_length_max if hasattr(dataset_conf, "{}_length_max".format(data_name)) else -1
+ with open(shape_file, "w") as f:
+ for line in lines:
+ sample_name, feature_path = line.strip().split()
+ feature = kaldiio.load_mat(feature_path)
+ n_frames, feature_dim = feature.shape
+ if n_frames > 0 and length_min > 0:
+ write_flag = n_frames >= length_min
+ if n_frames > 0 and length_max > 0:
+ write_flag = n_frames <= length_max
+ if write_flag:
+ f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim))))
+ f.flush()
+ elif data_type == "text":
+ with open(shape_file, "w") as f:
+ for line in lines:
+ sample_name, text = line.strip().split(maxsplit=1)
+ n_tokens = len(text.split())
+ f.write("{} {}\n".format(sample_name, str(int(np.ceil(n_tokens)))))
f.flush()
+ else:
+ raise RuntimeError("Unsupported data_type: {}".format(data_type))
def calc_shape(args, dataset, nj=64):
- shape_path = os.path.join(args.data_dir, dataset, "speech_shape")
+ data_name = args.dataset_conf.get("data_names", "speech,text").split(",")[0]
+ shape_path = os.path.join(args.data_dir, dataset, "{}_shape".format(data_name))
if os.path.exists(shape_path):
logging.info('Shape file for small dataset already exists.')
return
- split_shape_path = os.path.join(args.data_dir, dataset, "shape_files")
+ split_shape_path = os.path.join(args.data_dir, dataset, "{}_shape_files".format(data_name))
if os.path.exists(split_shape_path):
shutil.rmtree(split_shape_path)
os.mkdir(split_shape_path)
# split
- wav_scp_file = os.path.join(args.data_dir, dataset, "wav.scp")
- with open(wav_scp_file) as f:
+ file_name = args.data_file_names.split(",")[0]
+ scp_file = os.path.join(args.data_dir, dataset, file_name)
+ with open(scp_file) as f:
lines = f.readlines()
num_lines = len(lines)
num_job_lines = num_lines // nj
start = 0
for i in range(nj):
end = start + num_job_lines
- file = os.path.join(split_shape_path, "wav.scp.{}".format(str(i + 1)))
+ file = os.path.join(split_shape_path, "{}.{}".format(file_name, str(i + 1)))
with open(file, "w") as f:
if i == nj - 1:
f.writelines(lines[start:])
@@ -108,15 +140,18 @@
# combine
with open(shape_path, "w") as f:
for i in range(nj):
- job_file = os.path.join(split_shape_path, "speech_shape.{}".format(str(i + 1)))
+ job_file = os.path.join(split_shape_path, "{}_shape.{}".format(data_name, str(i + 1)))
with open(job_file) as job_f:
lines = job_f.readlines()
f.writelines(lines)
logging.info('Generating shape files done.')
-def generate_data_list(data_dir, dataset, nj=64):
- list_file = os.path.join(data_dir, dataset, "data.list")
+def generate_data_list(args, data_dir, dataset, nj=64):
+ data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
+ file_names = args.data_file_names.split(",")
+ concat_data_name = "_".join(data_names)
+ list_file = os.path.join(data_dir, dataset, "{}_data.list".format(concat_data_name))
if os.path.exists(list_file):
logging.info('Data list for large dataset already exists.')
return
@@ -125,85 +160,66 @@
shutil.rmtree(split_path)
os.mkdir(split_path)
- with open(os.path.join(data_dir, dataset, "wav.scp")) as f_wav:
- wav_lines = f_wav.readlines()
- with open(os.path.join(data_dir, dataset, "text")) as f_text:
- text_lines = f_text.readlines()
- num_lines = len(wav_lines)
+ data_lines_list = []
+ for file_name in file_names:
+ with open(os.path.join(data_dir, dataset, file_name)) as f:
+ lines = f.readlines()
+ data_lines_list.append(lines)
+ num_lines = len(data_lines_list[0])
num_job_lines = num_lines // nj
start = 0
for i in range(nj):
end = start + num_job_lines
split_path_nj = os.path.join(split_path, str(i + 1))
os.mkdir(split_path_nj)
- wav_file = os.path.join(split_path_nj, "wav.scp")
- text_file = os.path.join(split_path_nj, "text")
- with open(wav_file, "w") as fw, open(text_file, "w") as ft:
- if i == nj - 1:
- fw.writelines(wav_lines[start:])
- ft.writelines(text_lines[start:])
- else:
- fw.writelines(wav_lines[start:end])
- ft.writelines(text_lines[start:end])
+ for file_id, file_name in enumerate(file_names):
+ file = os.path.join(split_path_nj, file_name)
+ with open(file, "w") as f:
+ if i == nj - 1:
+ f.writelines(data_lines_list[file_id][start:])
+ else:
+ f.writelines(data_lines_list[file_id][start:end])
start = end
with open(list_file, "w") as f_data:
for i in range(nj):
- wav_path = os.path.join(split_path, str(i + 1), "wav.scp")
- text_path = os.path.join(split_path, str(i + 1), "text")
- f_data.write(wav_path + " " + text_path + "\n")
+ path = ""
+ for file_name in file_names:
+ path = path + os.path.join(split_path, str(i + 1), file_name)
+ f_data.write(path + "\n")
def prepare_data(args, distributed_option):
distributed = distributed_option.distributed
if not distributed or distributed_option.dist_rank == 0:
- filter_wav_text(args.data_dir, args.train_set)
- filter_wav_text(args.data_dir, args.valid_set)
+ if hasattr(args, "filter_input") and args.filter_input:
+ filter_wav_text(args.data_dir, args.train_set)
+ filter_wav_text(args.data_dir, args.valid_set)
if args.dataset_type == "small":
calc_shape(args, args.train_set)
calc_shape(args, args.valid_set)
if args.dataset_type == "large":
- generate_data_list(args.data_dir, args.train_set)
- generate_data_list(args.data_dir, args.valid_set)
+ generate_data_list(args, args.data_dir, args.train_set)
+ generate_data_list(args, args.data_dir, args.valid_set)
+ data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
+ data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
+ file_names = args.data_file_names.split(",")
+ assert len(data_names) == len(data_types) == len(file_names)
if args.dataset_type == "small":
- args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
- args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "speech_shape")]
- data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
- data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
- args.train_data_path_and_name_and_type = [
- ["{}/{}/wav.scp".format(args.data_dir, args.train_set), data_names[0], data_types[0]],
- ["{}/{}/text".format(args.data_dir, args.train_set), data_names[1], data_types[1]]
- ]
- args.valid_data_path_and_name_and_type = [
- ["{}/{}/wav.scp".format(args.data_dir, args.valid_set), data_names[0], data_types[0]],
- ["{}/{}/text".format(args.data_dir, args.valid_set), data_names[1], data_types[1]]
- ]
- if args.embed_path is not None:
+ args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "{}_shape".format(data_names[0]))]
+ args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "{}}_shape".format(data_names[0]))]
+ args.train_data_path_and_name_and_type, args.valid_data_path_and_name_and_type = [], []
+ for file_name, data_name, data_type in zip(file_names, data_names, data_types):
args.train_data_path_and_name_and_type.append(
- [os.path.join(args.embed_path, "embeds", args.train_set, "embeds.scp"), "embed", "kaldi_ark"])
+ ["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type])
args.valid_data_path_and_name_and_type.append(
- [os.path.join(args.embed_path, "embeds", args.valid_set, "embeds.scp"), "embed", "kaldi_ark"])
+ ["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type])
else:
- args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
- args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data.list")
- if args.embed_path is not None:
- if not distributed or distributed_option.dist_rank == 0:
- for d in [args.train_set, args.valid_set]:
- file = os.path.join(args.data_dir, d, "data.list")
- with open(file) as f:
- lines = f.readlines()
- out_file = os.path.join(args.data_dir, d, "data_with_embed.list")
- with open(out_file, "w") as out_f:
- for line in lines:
- parts = line.strip().split()
- idx = parts[0].split("/")[-2]
- embed_file = os.path.join(args.embed_path, "embeds", args.valid_set, "ark",
- "embeds.{}.ark".format(idx))
- out_f.write(parts[0] + " " + parts[1] + " " + embed_file + "\n")
- args.train_data_file = os.path.join(args.data_dir, args.train_set, "data_with_embed.list")
- args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data_with_embed.list")
+ concat_data_name = "_".join(data_names)
+ args.train_data_file = os.path.join(args.data_dir, args.train_set, "{}_data.list".format(concat_data_name))
+ args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name))
if distributed:
dist.barrier()
--
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