From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/utils/prepare_data.py | 208 ++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 168 insertions(+), 40 deletions(-)
diff --git a/funasr/utils/prepare_data.py b/funasr/utils/prepare_data.py
index a0d97f6..36eebdc 100644
--- a/funasr/utils/prepare_data.py
+++ b/funasr/utils/prepare_data.py
@@ -1,9 +1,13 @@
-import os
import logging
+import os
+import shutil
from multiprocessing import Pool
+import kaldiio
import numpy as np
+import librosa
import torch.distributed as dist
+import torchaudio
def filter_wav_text(data_dir, dataset):
@@ -25,7 +29,7 @@
parts = line.strip().split()
if len(parts) < 2:
continue
- text_dict[parts[0]] = " ".join(parts[1:]).lower()
+ text_dict[parts[0]] = " ".join(parts[1:])
filter_count = 0
with open(wav_file, "w") as f_wav, open(text_file, "w") as f_text:
for sample_name, wav_path in wav_dict.items():
@@ -34,50 +38,97 @@
f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
else:
filter_count += 1
- logging.info("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines),
- filter_count,
- dataset))
+ logging.info("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".
+ format(filter_count, len(wav_lines), dataset))
-def calc_shape_core(root_path, frontend_conf, speech_length_min, speech_length_max, 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:
+def wav2num_frame(wav_path, frontend_conf):
+ try:
+ waveform, sampling_rate = torchaudio.load(wav_path)
+ except:
+ waveform, sampling_rate = librosa.load(wav_path)
+ waveform = np.expand_dims(waveform, axis=0)
+ n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"])
+ feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"]
+ return n_frames, feature_dim
+
+
+def calc_shape_core(root_path, args, idx):
+ 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()
- with open(shape_file, "w") as f:
- for line in lines:
- sample_name, wav_path = line.strip().split()
- n_frames, feature_dim, speech_length = wav2num_frame(wav_path, frontend_conf)
- write_flag = True
- if speech_length_min > 0 and speech_length < speech_length_min:
- write_flag = False
- if speech_length_max > 0 and speech_length > speech_length_max:
- write_flag = False
- 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
+ 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 == "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=32):
- shape_path = os.path.join(args.data_dir, dataset, "speech_shape")
+def calc_shape(args, dataset, nj=64):
+ 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):
- print('Shape file for small dataset already exists.')
+ logging.info('Shape file for small dataset already exists.')
return
- split_shape_path = os.path.join(args.data_dir, dataset, "shape_files")
- if os.path
- os.makedirs(split_shape_path, exist_ok=True)
+ 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(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:])
@@ -87,28 +138,105 @@
p = Pool(nj)
for i in range(nj):
- p.apply_async(calc_shape_core,
- args=(shape_path, frontend_conf, speech_length_min, speech_length_max, str(i + 1)))
- print('Generating shape files, please wait a few minutes...')
+ p.apply_async(calc_shape_core, args=(split_shape_path, args, str(i + 1)))
+ logging.info("Generating shape files, please wait a few minutes...")
p.close()
p.join()
# combine
- file = os.path.join(data_dir, dataset, "speech_shape")
- with open(file, "w") as f:
+ with open(shape_path, "w") as f:
for i in range(nj):
- job_file = os.path.join(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)
- print('Generating shape files done.')
+ logging.info('Generating shape files done.')
+
+
+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
+ split_path = os.path.join(data_dir, dataset, "split")
+ if os.path.exists(split_path):
+ shutil.rmtree(split_path)
+ os.mkdir(split_path)
+
+ 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)
+ 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):
+ 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):
+ 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(",")
+ batch_type = args.dataset_conf["batch_conf"]["batch_type"]
+ print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names))
+ 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, "{}_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(
+ ["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type])
+ args.valid_data_path_and_name_and_type.append(
+ ["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type])
+ if os.path.exists(args.train_shape_file[0]):
+ assert os.path.exists(args.valid_shape_file[0])
+ print('shape file for small dataset already exists.')
+ return
+ else:
+ 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 os.path.exists(args.train_data_file):
+ assert os.path.exists(args.valid_data_file)
+ print('data list for large dataset already exists.')
+ return
+
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.dev_set)
- dist.barrier()
+ 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" and args.train_shape_file is None:
+ if args.dataset_type == "small" and batch_type != "unsorted":
+ calc_shape(args, args.train_set)
+ calc_shape(args, args.valid_set)
+
+ if args.dataset_type == "large":
+ generate_data_list(args, args.data_dir, args.train_set)
+ generate_data_list(args, args.data_dir, args.valid_set)
+
+ if distributed:
+ dist.barrier()
--
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