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