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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
sond pipeline
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志浩
parent
11cb9b21cd
commit
64bd74c7be
@ -11,6 +11,11 @@ import random
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from typing import List, Dict
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from copy import deepcopy
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import json
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logging.basicConfig(
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level="INFO",
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format=f"[{os.uname()[1].split('.')[0]}]"
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f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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class MyRunner(MultiProcessRunnerV3):
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@ -28,24 +33,20 @@ class MyRunner(MultiProcessRunnerV3):
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parser.add_argument("--embedding_dim", type=int, default=None)
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parser.add_argument("--average_emb_num", type=int, default=0)
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parser.add_argument("--subset", type=int, default=0)
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parser.add_argument("--data_dict", type=str, default=None)
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parser.add_argument("--data_json", type=str, default=None)
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parser.add_argument("--seed", type=int, default=1234)
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parser.add_argument("--log_interval", type=int, default=100)
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args = parser.parse_args()
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random.seed(args.seed)
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np.random.seed(args.seed)
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if not os.path.exists(args.out_dir):
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os.makedirs(args.out_dir)
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args.chunk_size = int(args.chunk_size / args.frame_shift)
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args.chunk_shift = int(args.chunk_shift / args.frame_shift)
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if not os.path.exists(args.data_dict):
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logging.info("Loading data...")
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if not os.path.exists(args.data_json):
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label_list = load_scp_as_list(args.label_scp)
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wav_scp = load_scp_as_dict(args.wav_scp)
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utt2spk = load_scp_as_dict(args.utt2spk)
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utt2xvec = load_scp_as_dict(args.utt2xvec)
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spk2meeting = load_scp_as_dict(args.spk2meeting)
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if args.embedding_dim is None:
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args.embedding_dim = kaldiio.load_mat(random.choice(utt2xvec)).shape[1]
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logging.info("Embedding dim is detected as {}.".format(args.embedding_dim))
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meeting2spks = OrderedDict()
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for spk, meeting in spk2meeting.items():
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@ -59,23 +60,37 @@ class MyRunner(MultiProcessRunnerV3):
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spk2utts[spk] = []
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spk2utts[spk].append(utt)
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os.makedirs(os.path.dirname(args.data_dict), exist_ok=True)
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os.makedirs(os.path.dirname(args.data_json), exist_ok=True)
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logging.info("Dump data...")
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json.dump({
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"label_list": label_list, "wav_scp": wav_scp, "utt2xvec": utt2xvec,
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"spk2utts": spk2utts, "meeting2spks": meeting2spks
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}, open(args.data_dict, "wt", encoding="utf-8"), ensure_ascii=False, indent=4)
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}, open(args.data_json, "wt", encoding="utf-8"), ensure_ascii=False, indent=4)
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else:
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data_dict = json.load(open(args.data_dict, "rt", encoding="utf-8"))
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data_dict = json.load(open(args.data_json, "rt", encoding="utf-8"))
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label_list = data_dict["label_list"]
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wav_scp = data_dict["wav_scp"]
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utt2xvec = data_dict["utt2xvec"]
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spk2utts = data_dict["spk2utts"]
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meeting2spks = data_dict["meeting2spks"]
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if not os.path.exists(args.out_dir):
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os.makedirs(args.out_dir)
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args.chunk_size = int(args.chunk_size / args.frame_shift)
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args.chunk_shift = int(args.chunk_shift / args.frame_shift)
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if args.embedding_dim is None:
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args.embedding_dim = kaldiio.load_mat(next(iter(utt2xvec.values()))).shape[1]
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logging.info("Embedding dim is detected as {}.".format(args.embedding_dim))
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logging.info("Number utt: {}, Number speaker: {}, Number meetings: {}".format(
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len(wav_scp), len(spk2utts), len(meeting2spks)
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))
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return label_list, (wav_scp, utt2xvec, spk2utts, meeting2spks), args
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def post(self, results_list, args):
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pass
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logging.info("[main]: Got {} chunks.".format(sum(results_list)))
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def simu_wav_chunk(spk, spk2utts, wav_scp, sample_length):
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@ -89,7 +104,7 @@ def simu_wav_chunk(spk, spk2utts, wav_scp, sample_length):
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cur_length += len(wav)
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concat_wav = np.concatenate(wav_list, axis=0)
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start = random.randint(0, len(concat_wav) - sample_length)
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return concat_wav[start:]
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return concat_wav[start: start+sample_length]
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def calculate_embedding(spk, spk2utts, utt2xvec, embedding_dim, average_emb_num):
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@ -103,9 +118,9 @@ def calculate_embedding(spk, spk2utts, utt2xvec, embedding_dim, average_emb_num)
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xvec_list = [kaldiio.load_mat(utt2xvec[utt]) for utt in utt_list]
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else:
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xvec_list = [kaldiio.load_mat(utt2xvec[utt]) for utt in random.sample(utt_list, average_emb_num)]
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# TODO: rerun the simulation
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xvec_list = [x / np.linalg.norm(x, axis=-1) for x in xvec_list]
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xvec = np.mean(np.concatenate(xvec_list, axis=0), axis=0)
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xvec = np.concatenate(xvec_list, axis=0)
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xvec = xvec / np.linalg.norm(xvec, axis=-1, keepdims=True)
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xvec = np.mean(xvec, axis=0)
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return xvec
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@ -124,7 +139,7 @@ def simu_chunk(
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):
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frame_length, max_spk_num = frame_label.shape
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sample_length = sample_label.shape[0]
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positive_speaker_num = np.max(frame_label.sum(axis=1), axis=0)
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positive_speaker_num = int(np.sum(frame_label.sum(axis=0) > 0))
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pos_speaker_list = deepcopy(meeting2spks[random.choice(meeting_list)])
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# get positive speakers
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@ -134,7 +149,7 @@ def simu_chunk(
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while len(pos_speaker_list) < positive_speaker_num:
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_spk = random.choice(all_speaker_list)
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if _spk not in pos_speaker_list:
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pos_speaker_list.extend(_spk)
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pos_speaker_list.append(_spk)
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# get negative speakers
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negative_speaker_num = random.randint(0, max_spk_num - positive_speaker_num)
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@ -142,12 +157,12 @@ def simu_chunk(
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while len(neg_speaker_list) < negative_speaker_num:
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_spk = random.choice(all_speaker_list)
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if _spk not in pos_speaker_list and _spk not in neg_speaker_list:
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neg_speaker_list.extend(_spk)
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neg_speaker_list.append(_spk)
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neg_speaker_list.extend(["None"] * (max_spk_num - positive_speaker_num - negative_speaker_num))
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random.shuffle(pos_speaker_list)
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random.shuffle(neg_speaker_list)
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seperated_wav = np.zeros(frame_label.shape, dtype=np.float32)
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seperated_wav = np.zeros(sample_label.shape, dtype=np.float32)
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this_spk_list = []
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for idx, frame_num in enumerate(frame_label.sum(axis=0)):
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if frame_num > 0:
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@ -166,12 +181,13 @@ def simu_chunk(
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shuffle_idx = list(range(max_spk_num))
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random.shuffle(shuffle_idx)
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this_spk_list = [this_spk_list[x] for x in shuffle_idx]
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seperated_wav = seperated_wav.transpose([0, 1])[shuffle_idx].transpose([0, 1])
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frame_label = frame_label.transpose([0, 1])[shuffle_idx].transpose([0, 1])
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seperated_wav = seperated_wav.transpose()[shuffle_idx].transpose()
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frame_label = frame_label.transpose()[shuffle_idx].transpose()
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# calculate profile and pse_label
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# calculate profile
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profile = [calculate_embedding(spk, spk2utts, utt2xvec, embedding_dim, average_emb_num)
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for spk in this_spk_list]
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profile = np.vstack(profile)
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# pse_weights = 2 ** np.arange(max_spk_num)
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# pse_label = np.sum(frame_label * pse_weights[np.newaxis, :], axis=1)
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# pse_label = pse_label.astype(str).tolist()
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@ -181,11 +197,13 @@ def simu_chunk(
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def process(task_args):
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task_idx, task_list, (wav_scp, utt2xvec, spk2utts, meeting2spks), args = task_args
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logging.info("{:02d}/{:02d}: Start simulation...".format(task_idx+1, args.nj))
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out_path = os.path.join(args.out_dir, "wav_mix.{}".format(task_idx+1))
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wav_mix_writer = kaldiio.WriteHelper('ark,scp:{}.ark,{}.scp'.format(out_path, out_path))
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out_path = os.path.join(args.out_dir, "wav_sep.{}".format(task_idx + 1))
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wav_sep_writer = kaldiio.WriteHelper('ark,scp:{}.ark,{}.scp'.format(out_path, out_path))
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# out_path = os.path.join(args.out_dir, "wav_sep.{}".format(task_idx + 1))
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# wav_sep_writer = kaldiio.WriteHelper('ark,scp:{}.ark,{}.scp'.format(out_path, out_path))
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out_path = os.path.join(args.out_dir, "profile.{}".format(task_idx + 1))
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profile_writer = kaldiio.WriteHelper('ark,scp:{}.ark,{}.scp'.format(out_path, out_path))
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@ -195,16 +213,23 @@ def process(task_args):
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speaker_list, meeting_list = list(spk2utts.keys()), list(meeting2spks.keys())
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idx = 0
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labels_list = []
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total_chunks = 0
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for org_mid, label_path in task_list:
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rand_shift = random.randint(0, int(args.chunk_shift / args.frame_shift))
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whole_label = kaldiio.load_mat(label_path)
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whole_label = whole_label[rand_shift:]
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num_chunk = (whole_label.shape[0] - args.chunk_size) // args.chunk_shift + 1
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# random offset to keep diversity
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rand_shift = random.randint(0, args.chunk_shift)
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num_chunk = (whole_label.shape[0] - rand_shift - args.chunk_size) // args.chunk_shift + 1
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labels_list.append((org_mid, whole_label, rand_shift, num_chunk))
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total_chunks += num_chunk
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idx = 0
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simu_chunk_count = 0
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for org_mid, whole_label, rand_shift, num_chunk in labels_list:
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for i in range(num_chunk):
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idx = idx + 1
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st = int((i*args.chunk_shift) / args.frame_shift)
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ed = int((i*args.chunk_shift+args.chunk_size) / args.frame_shift)
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st = i * args.chunk_shift + rand_shift
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ed = i * args.chunk_shift + args.chunk_size + rand_shift
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utt_id = "subset{}_part{}_{}_{:06d}_{:06d}".format(
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args.subset + 1, task_idx + 1, org_mid, st, ed
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)
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@ -215,15 +240,20 @@ def process(task_args):
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speaker_list, meeting_list, args.embedding_dim, args.average_emb_num
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)
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wav_mix_writer(utt_id, mix_wav)
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wav_sep_writer(utt_id, seg_wav)
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# wav_sep_writer(utt_id, seg_wav)
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profile_writer(utt_id, profile)
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label_writer(utt_id, frame_label)
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simu_chunk_count += 1
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if simu_chunk_count % args.log_interval == 0:
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logging.info("{:02d}/{:02d}: Complete {}/{} simulation, {}.".format(
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task_idx + 1, args.nj, simu_chunk_count, total_chunks, utt_id))
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wav_mix_writer.close()
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wav_sep_writer.close()
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# wav_sep_writer.close()
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profile_writer.close()
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label_writer.close()
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return None
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logging.info("[{}/{}]: Simulate {} chunks.".format(task_idx+1, args.nj, simu_chunk_count))
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return simu_chunk_count
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if __name__ == '__main__':
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