update

funasr/bin/train.py

@@ -1,9 +1,12 @@
+import logging
+import os
 import sys
 
 import torch
 
 from funasr.utils import config_argparse
 from funasr.utils.build_distributed import build_distributed
+from funasr.utils.prepare_data import prepare_data
 from funasr.utils.types import str2bool
 
 
@@ -318,9 +321,23 @@ if __name__ == '__main__':
     parser = get_parser()
     args = parser.parse_args()
 
+    # ddp init
     args.distributed = args.dist_world_size > 1
     distributed_option = build_distributed(args)
+    if not distributed_option.distributed or distributed_option.dist_rank == 0:
+        logging.basicConfig(
+            level="INFO",
+            format=f"[{os.uname()[1].split('.')[0]}]"
+                   f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+        )
+    else:
+        logging.basicConfig(
+            level="ERROR",
+            format=f"[{os.uname()[1].split('.')[0]}]"
+                   f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+        )
+    logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size,
+                                                                   distributed_option.dist_rank,
+                                                                   distributed_option.local_rank))
 
-    #
-
-
+    prepare_data(args, distributed_option)

funasr/utils/prepare_data.py

@@ -0,0 +1,117 @@
+import os
+import logging
+from multiprocessing import Pool
+
+import numpy as np
+import torch.distributed as dist
+
+
+def filter_wav_text(data_dir, dataset):
+    wav_file = os.path.join(data_dir, dataset, "wav.scp")
+    text_file = os.path.join(data_dir, dataset, "text")
+    with open(wav_file) as f_wav, open(text_file) as f_text:
+        wav_lines = f_wav.readlines()
+        text_lines = f_text.readlines()
+    os.rename(wav_file, "{}.bak".format(wav_file))
+    os.rename(text_file, "{}.bak".format(text_file))
+    wav_dict = {}
+    for line in wav_lines:
+        parts = line.strip().split()
+        if len(parts) < 2:
+            continue
+        wav_dict[parts[0]] = parts[1]
+    text_dict = {}
+    for line in text_lines:
+        parts = line.strip().split()
+        if len(parts) < 2:
+            continue
+        text_dict[parts[0]] = " ".join(parts[1:]).lower()
+    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():
+            if sample_name in text_dict.keys():
+                f_wav.write(sample_name + " " + wav_path + "\n")
+                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))
+
+
+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:
+        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))))
+                f.flush()
+
+
+def calc_shape(args, dataset, nj=32):
+    shape_path = os.path.join(args.data_dir, dataset, "speech_shape")
+    if os.path.exists(shape_path):
+        print('Shape file for small dataset already exists.')
+        return
+    os.makedirs(shape_path, exist_ok=True)
+    split_shape_path = os.path.join(args.data_dir, dataset, "shape_files")
+    if os.path.exists(shape_path):
+        assert os.path.exists(os.path.join(args.data_dir, dataset, "speech_shape"))
+        print('Shape file for small dataset already exists.')
+        return
+    os.makedirs(shape_path, exist_ok=True)
+
+    # split
+    wav_scp_file = os.path.join(args.data_dir, dataset, "wav.scp")
+    with open(wav_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)))
+        with open(file, "w") as f:
+            if i == nj - 1:
+                f.writelines(lines[start:])
+            else:
+                f.writelines(lines[start:end])
+        start = end
+
+    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.close()
+    p.join()
+
+    # combine
+    file = os.path.join(data_dir, dataset, "speech_shape")
+    with open(file, "w") as f:
+        for i in range(nj):
+            job_file = os.path.join(shape_path, "speech_shape.{}".format(str(i + 1)))
+            with open(job_file) as job_f:
+                lines = job_f.readlines()
+                f.writelines(lines)
+    print('Generating shape files done.')
+
+
+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.dev_set)
+        dist.barrier()
+
+        if args.dataset_type == "small" and args.train_shape_file is None: