Delete tools

tools/extract_embedding.py

@@ -1,69 +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 torch
-import torchaudio
-from tqdm import tqdm
-import onnxruntime
-import torchaudio.compliance.kaldi as kaldi
-
-
-def main(args):
-    utt2wav, utt2spk = {}, {}
-    with open('{}/wav.scp'.format(args.dir)) as f:
-        for l in f:
-            l = l.replace('\n', '').split()
-            utt2wav[l[0]] = l[1]
-    with open('{}/utt2spk'.format(args.dir)) as f:
-        for l in f:
-            l = l.replace('\n', '').split()
-            utt2spk[l[0]] = l[1]
-
-    option = onnxruntime.SessionOptions()
-    option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-    option.intra_op_num_threads = 1
-    providers = ["CPUExecutionProvider"]
-    ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
-
-    utt2embedding, spk2embedding = {}, {}
-    for utt in tqdm(utt2wav.keys()):
-        audio, sample_rate = torchaudio.load(utt2wav[utt])
-        if sample_rate != 16000:
-            audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio)
-        feat = kaldi.fbank(audio,
-                           num_mel_bins=80,
-                           dither=0,
-                           sample_frequency=16000)
-        feat = feat - feat.mean(dim=0, keepdim=True)
-        embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
-        utt2embedding[utt] = embedding
-        spk = utt2spk[utt]
-        if spk not in spk2embedding:
-            spk2embedding[spk] = []
-        spk2embedding[spk].append(embedding)
-    for k, v in spk2embedding.items():
-        spk2embedding[k] = torch.tensor(v).mean(dim=0).tolist()
-
-    torch.save(utt2embedding, '{}/utt2embedding.pt'.format(args.dir))
-    torch.save(spk2embedding, '{}/spk2embedding.pt'.format(args.dir))
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dir',
-                        type=str)
-    parser.add_argument('--onnx_path',
-                        type=str)
-    args = parser.parse_args()
-    main(args)

tools/extract_speech_token.py

@@ -1,61 +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 torch
-from tqdm import tqdm
-import onnxruntime
-import numpy as np
-import torchaudio
-import whisper
-
-
-def main(args):
-    utt2wav = {}
-    with open('{}/wav.scp'.format(args.dir)) as f:
-        for l in f:
-            l = l.replace('\n', '').split()
-            utt2wav[l[0]] = l[1]
-
-    option = onnxruntime.SessionOptions()
-    option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-    option.intra_op_num_threads = 1
-    providers = ["CUDAExecutionProvider"]
-    ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers)
-
-    utt2speech_token = {}
-    for utt in tqdm(utt2wav.keys()):
-        audio, sample_rate = torchaudio.load(utt2wav[utt])
-        if sample_rate != 16000:
-            audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio)
-        if audio.shape[1] / 16000 > 30:
-            logging.warning('do not support extract speech token for audio longer than 30s')
-            speech_token = []
-        else:
-            feat = whisper.log_mel_spectrogram(audio, n_mels=128)
-            speech_token = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.detach().cpu().numpy(),
-                                                  ort_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist()
-        utt2speech_token[utt] = speech_token
-    torch.save(utt2speech_token, '{}/utt2speech_token.pt'.format(args.dir))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dir',
-                        type=str)
-    parser.add_argument('--onnx_path',
-                        type=str)
-    args = parser.parse_args()
-    main(args)

tools/make_parquet_list.py

@@ -1,112 +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]
-
-    # 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
-    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)
-    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))
-    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')