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| import argparse
| import os
|
| import numpy as np
| import soundfile as sf
| import torch
| import yaml
| from scipy.signal import medfilt
|
| import funasr.models.frontend.eend_ola_feature as eend_ola_feature
| from funasr.build_utils.build_model_from_file import build_model_from_file
|
| if __name__ == '__main__':
| parser = argparse.ArgumentParser()
| parser.add_argument(
| "--config_file",
| type=str,
| help="model config file",
| )
| parser.add_argument(
| "--model_file",
| type=str,
| help="model path",
| )
| parser.add_argument(
| "--output_rttm_file",
| type=str,
| help="output rttm path",
| )
| parser.add_argument(
| "--wav_scp_file",
| type=str,
| default="wav.scp",
| help="input data path",
| )
| parser.add_argument(
| "--frame_shift",
| type=int,
| default=80,
| help="frame shift",
| )
| parser.add_argument(
| "--frame_size",
| type=int,
| default=200,
| help="frame size",
| )
| parser.add_argument(
| "--context_size",
| type=int,
| default=7,
| help="context size",
| )
| parser.add_argument(
| "--sampling_rate",
| type=int,
| default=8000,
| help="sampling rate",
| )
| parser.add_argument(
| "--subsampling",
| type=int,
| default=10,
| help="setting subsampling",
| )
| parser.add_argument(
| "--shuffle",
| type=bool,
| default=True,
| help="shuffle speech in time",
| )
| parser.add_argument(
| "--attractor_threshold",
| type=float,
| default=0.5,
| help="threshold for selecting attractors",
| )
| parser.add_argument(
| "--device",
| type=str,
| default="cuda",
| )
| args = parser.parse_args()
|
| with open(args.config_file) as f:
| configs = yaml.safe_load(f)
| for k, v in configs.items():
| if not hasattr(args, k):
| setattr(args, k, v)
|
| np.random.seed(args.seed)
| torch.manual_seed(args.seed)
| torch.cuda.manual_seed(args.seed)
| os.environ['PYTORCH_SEED'] = str(args.seed)
|
| model, _ = build_model_from_file(config_file=args.config_file, model_file=args.model_file, task_name="diar",
| device=args.device)
| model.eval()
|
| with open(args.wav_scp_file) as f:
| wav_lines = [line.strip().split() for line in f.readlines()]
| wav_items = {x[0]: x[1] for x in wav_lines}
|
| print("Start inference")
| with open(args.output_rttm_file, "w") as wf:
| for wav_id in wav_items.keys():
| print("Process wav: {}".format(wav_id))
| data, rate = sf.read(wav_items[wav_id])
| speech = eend_ola_feature.stft(data, args.frame_size, args.frame_shift)
| speech = eend_ola_feature.transform(speech)
| speech = eend_ola_feature.splice(speech, context_size=args.context_size)
| speech = speech[::args.subsampling] # sampling
| speech = torch.from_numpy(speech)
|
| with torch.no_grad():
| speech = speech.to(args.device)
| ys, _, _, _ = model.estimate_sequential(
| [speech],
| n_speakers=None,
| th=args.attractor_threshold,
| shuffle=args.shuffle
| )
|
| a = ys[0].cpu().numpy()
| a = medfilt(a, (11, 1))
| rst = []
| for spkr_id, frames in enumerate(a.T):
| frames = np.pad(frames, (1, 1), 'constant')
| changes, = np.where(np.diff(frames, axis=0) != 0)
| fmt = "SPEAKER {:s} 1 {:7.2f} {:7.2f} <NA> <NA> {:s} <NA>"
| for s, e in zip(changes[::2], changes[1::2]):
| st = s * args.frame_shift * args.subsampling / args.sampling_rate
| dur = (e - s) * args.frame_shift * args.subsampling / args.sampling_rate
| print(fmt.format(
| wav_id,
| st,
| dur,
| wav_id + "_" + str(spkr_id)), file=wf)
|
|
