import numpy as np import os import argparse from funasr.utils.job_runner import MultiProcessRunnerV3 from funasr.utils.misc import load_scp_as_list, load_scp_as_dict import soundfile as sf from tqdm import tqdm class MyRunner(MultiProcessRunnerV3): def prepare(self, parser): assert isinstance(parser, argparse.ArgumentParser) parser.add_argument("wav_scp", type=str) parser.add_argument("rttm", type=str) parser.add_argument("out_dir", type=str) parser.add_argument("--min_dur", type=float, default=2.0) parser.add_argument("--max_spk_num", type=int, default=4) args = parser.parse_args() if not os.path.exists(args.out_dir): os.makedirs(args.out_dir) wav_scp = load_scp_as_list(args.wav_scp) meeting2rttms = {} for one_line in open(args.rttm, "rt"): parts = [x for x in one_line.strip().split(" ") if x != ""] mid, st, dur, spk_name = parts[1], float(parts[3]), float(parts[4]), parts[7] if mid not in meeting2rttms: meeting2rttms[mid] = [] meeting2rttms[mid].append(one_line) task_list = [(mid, wav_path, meeting2rttms[mid]) for (mid, wav_path) in wav_scp] return task_list, None, args def post(self, result_list, args): count = [0, 0] for result in result_list: count[0] += result[0] count[1] += result[1] print("Found {} speakers, extracted {}.".format(count[1], count[0])) # SPEAKER R8001_M8004_MS801 1 6.90 11.39 1 def calc_multi_label(rttms, length, sr=8000, max_spk_num=4): labels = np.zeros([max_spk_num, length], int) spk_list = [] for one_line in rttms: parts = [x for x in one_line.strip().split(" ") if x != ""] mid, st, dur, spk_name = parts[1], float(parts[3]), float(parts[4]), parts[7] spk_name = spk_name.replace("spk", "").replace(mid, "").replace("-", "") if spk_name.isdigit(): spk_name = "{}_S{:03d}".format(mid, int(spk_name)) else: spk_name = "{}_{}".format(mid, spk_name) if spk_name not in spk_list: spk_list.append(spk_name) st, dur = int(st*sr), int(dur*sr) idx = spk_list.index(spk_name) labels[idx, st:st+dur] = 1 return labels, spk_list def get_nonoverlap_turns(multi_label, spk_list): turns = [] label = np.sum(multi_label, axis=0) == 1 spk, in_turn, st = None, False, 0 for i in range(len(label)): if not in_turn and label[i]: st, in_turn = i, True spk = spk_list[np.argmax(multi_label[:, i], axis=0)] if in_turn: if not label[i]: in_turn = False turns.append([st, i, spk]) elif label[i] and spk != spk_list[np.argmax(multi_label[:, i], axis=0)]: turns.append([st, i, spk]) st, in_turn = i, True spk = spk_list[np.argmax(multi_label[:, i], axis=0)] if in_turn: turns.append([st, len(label), spk]) return turns def process(task_args): task_id, task_list, _, args = task_args spk_count = [0, 0] for mid, wav_path, rttms in task_list: wav, sr = sf.read(wav_path, dtype="int16") assert sr == args.sr, "args.sr {}, file sr {}".format(args.sr, sr) multi_label, spk_list = calc_multi_label(rttms, len(wav), args.sr, args.max_spk_num) turns = get_nonoverlap_turns(multi_label, spk_list) extracted_spk = [] count = 1 for st, ed, spk in tqdm(turns, total=len(turns), ascii=True, disable=args.no_pbar): if (ed - st) >= args.min_dur * args.sr: seg = wav[st: ed] save_path = os.path.join(args.out_dir, mid, spk, "{}_U{:04d}.wav".format(spk, count)) if not os.path.exists(os.path.dirname(save_path)): os.makedirs(os.path.dirname(save_path)) sf.write(save_path, seg.astype(np.int16), args.sr, "PCM_16", "LITTLE", "WAV", True) count += 1 if spk not in extracted_spk: extracted_spk.append(spk) if len(extracted_spk) != len(spk_list): print("{}: Found {} speakers, but only extracted {}. {} are filtered due to min_dur".format( mid, len(spk_list), len(extracted_spk), " ".join([x for x in spk_list if x not in extracted_spk]) )) spk_count[0] += len(extracted_spk) spk_count[1] += len(spk_list) return spk_count if __name__ == '__main__': my_runner = MyRunner(process) my_runner.run()