update dpo

tools/make_parquet_list.py

@@ -34,7 +34,9 @@ def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file):
     spk_list = [utt2spk[utt] for utt in utt_list]
     uttembedding_list = [utt2embedding[utt] for utt in utt_list]
     spkembedding_list = [spk2embedding[utt2spk[utt]] for utt in utt_list]
-    speech_token_list = [utt2speech_token[utt] for utt in utt_list]
+    speech_token_list = [utt2speech_token.get(utt, []) for utt in utt_list]
+    if args.dpo:
+        reject_speech_token_list = [utt2reject_speech_token[utt] for utt in utt_list]
 
     # 保存到parquet,utt2parquet_file,spk2parquet_file
     df = pd.DataFrame()
@@ -46,6 +48,8 @@ def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file):
     df['utt_embedding'] = uttembedding_list
     df['spk_embedding'] = spkembedding_list
     df['speech_token'] = speech_token_list
+    if args.dpo:
+        df['reject_speech_token'] = reject_speech_token_list
     df.to_parquet(parquet_file)
     with open(utt2parquet_file, 'w') as f:
         json.dump({k: parquet_file for k in utt_list}, f, ensure_ascii=False, indent=2)
@@ -68,6 +72,10 @@ if __name__ == "__main__":
                         type=str)
     parser.add_argument('--des_dir',
                         type=str)
+    parser.add_argument('--dpo',
+                        action='store_true',
+                        default=False,
+                        help='Use Direct Preference Optimization')
     args = parser.parse_args()
 
     utt2wav, utt2text, utt2spk = {}, {}, {}
@@ -86,6 +94,8 @@ if __name__ == "__main__":
     utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir))
     spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir))
     utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir))
+    if args.dpo:
+        utt2reject_speech_token = torch.load('{}_reject/utt2speech_token.pt'.format(args.src_dir))
     utts = list(utt2wav.keys())
 
     # Using process pool to speedup

tools/make_parquet_list_dpo.py

@@ -1,125 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import argparse
-import logging
-import os
-import json
-from tqdm import tqdm
-import pandas as pd
-import multiprocessing
-import time
-import torch
-
-
-def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file):
-    start_time = time.time()
-    data_list = []
-    for utt in tqdm(utt_list):
-        data = open(utt2wav[utt], 'rb').read()
-        data_list.append(data)
-    wav_list = [utt2wav[utt] for utt in utt_list]
-    text_list = [utt2text[utt] for utt in utt_list]
-    spk_list = [utt2spk[utt] for utt in utt_list]
-    uttembedding_list = [utt2embedding[utt] for utt in utt_list]
-    spkembedding_list = [spk2embedding[utt2spk[utt]] for utt in utt_list]
-    speech_token_list = [utt2speech_token[utt] for utt in utt_list]
-    if utt2reject_speech_token:
-        reject_speech_token_list = [utt2reject_speech_token[utt] for utt in utt_list]
-
-    # 保存到parquet,utt2parquet_file,spk2parquet_file
-    df = pd.DataFrame()
-    df['utt'] = utt_list
-    df['wav'] = wav_list
-    df['audio_data'] = data_list
-    df['text'] = text_list
-    df['spk'] = spk_list
-    df['utt_embedding'] = uttembedding_list
-    df['spk_embedding'] = spkembedding_list
-    df['speech_token'] = speech_token_list
-    if utt2reject_speech_token:
-        df['reject_speech_token'] = reject_speech_token_list
-    df.to_parquet(parquet_file)
-    with open(utt2parquet_file, 'w') as f:
-        json.dump({k: parquet_file for k in utt_list}, f, ensure_ascii=False, indent=2)
-    with open(spk2parquet_file, 'w') as f:
-        json.dump({k: parquet_file for k in list(set(spk_list))}, f, ensure_ascii=False, indent=2)
-    logging.info('spend time {}'.format(time.time() - start_time))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--num_utts_per_parquet',
-                        type=int,
-                        default=1000,
-                        help='num utts per parquet')
-    parser.add_argument('--num_processes',
-                        type=int,
-                        default=1,
-                        help='num processes for make parquets')
-    parser.add_argument('--src_dir',
-                        type=str)
-    parser.add_argument('--des_dir',
-                        type=str)
-    parser.add_argument('--dpo',
-                        action='store_true',
-                        default=False,
-                        help='Use Direct Preference Optimization')
-    args = parser.parse_args()
-
-    utt2wav, utt2text, utt2spk = {}, {}, {}
-    with open('{}/wav.scp'.format(args.src_dir)) as f:
-        for l in f:
-            l = l.replace('\n', '').split()
-            utt2wav[l[0]] = l[1]
-    with open('{}/text'.format(args.src_dir)) as f:
-        for l in f:
-            l = l.replace('\n', '').split()
-            utt2text[l[0]] = ' '.join(l[1:])
-    with open('{}/utt2spk'.format(args.src_dir)) as f:
-        for l in f:
-            l = l.replace('\n', '').split()
-            utt2spk[l[0]] = l[1]
-    utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir))
-    spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir))
-    utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir))
-    if args.dpo:
-        utt2reject_speech_token = torch.load('{}/utt2reject_speech_token.pt'.format(args.src_dir))
-    else:
-        utt2reject_speech_token = None
-    utts = list(utt2wav.keys())
-
-    # Using process pool to speedup
-    pool = multiprocessing.Pool(processes=args.num_processes)
-    parquet_list, utt2parquet_list, spk2parquet_list = [], [], []
-    for i, j in enumerate(range(0, len(utts), args.num_utts_per_parquet)):
-        parquet_file = os.path.join(args.des_dir, 'parquet_{:09d}.tar'.format(i))
-        utt2parquet_file = os.path.join(args.des_dir, 'utt2parquet_{:09d}.json'.format(i))
-        spk2parquet_file = os.path.join(args.des_dir, 'spk2parquet_{:09d}.json'.format(i))
-        parquet_list.append(parquet_file)
-        utt2parquet_list.append(utt2parquet_file)
-        spk2parquet_list.append(spk2parquet_file)
-        pool.apply_async(job, (utts[j: j + args.num_utts_per_parquet], parquet_file, utt2parquet_file, spk2parquet_file))
-    pool.close()
-    pool.join()
-
-    with open('{}/data.list'.format(args.des_dir), 'w', encoding='utf8') as f1, \
-            open('{}/utt2data.list'.format(args.des_dir), 'w', encoding='utf8') as f2, \
-            open('{}/spk2data.list'.format(args.des_dir), 'w', encoding='utf8') as f3:
-        for name in parquet_list:
-            f1.write(name + '\n')
-        for name in utt2parquet_list:
-            f2.write(name + '\n')
-        for name in spk2parquet_list:
-            f3.write(name + '\n')