How to use the asteroid.metrics.get_metrics function in asteroid

ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix[None, None])
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'])
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],
                     conf['sample_rate'])
            # Loop over the sources and estimates
            for src_idx, src in enumerate(sources_np):
                sf.write(local_save_dir + "s{}.wav".format(src_idx+1), src,
                         conf['sample_rate'])
            for src_idx, est_src in enumerate(est_sources_np):
                sf.write(local_save_dir + "s{}_estimate.wav".format(src_idx+1),

conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix.unsqueeze(0))
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix.cpu().data.numpy()
        sources_np = sources.cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze(0).cpu().data.numpy()
        # For each utterance, we get a dictionary with the mixture path,
        # the input and output metrics
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'],
                                  metrics_list=compute_metrics)
        utt_metrics['mix_path'] = test_set.mixture_path
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np,
                     conf['sample_rate'])
            # Loop over the sources and estimates
            for src_idx, src in enumerate(sources_np):
                sf.write(local_save_dir + "s{}.wav".format(src_idx), src,
                         conf['sample_rate'])
            for src_idx, est_src in enumerate(est_sources_np):

ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix[None, None])
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'])
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],
                     conf['sample_rate'])
            # Loop over the sources and estimates
            for src_idx, src in enumerate(sources_np):
                sf.write(local_save_dir + "s{}.wav".format(src_idx+1), src,
                         conf['sample_rate'])
            for src_idx, est_src in enumerate(est_sources_np):
                sf.write(local_save_dir + "s{}_estimate.wav".format(src_idx+1),

series_list = []
    torch.no_grad().__enter__()
    cnt = 0
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        est_sources = model(mix.unsqueeze(0))
        min_len = min(est_sources.shape[-1], sources.shape[-1], mix.shape[-1])
        est_sources = est_sources[..., :min_len]
        mix, sources = mix[..., :min_len], sources[..., :min_len]
        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze(0).cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'])
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],
                     conf['sample_rate'])
            # Loop over the sources and estimates
            for src_idx, src in enumerate(sources_np):
                sf.write(local_save_dir + "s{}.wav".format(src_idx+1), src,
                         conf['sample_rate'])
            for src_idx, est_src in enumerate(est_sources_np):
                sf.write(local_save_dir + "s{}_estimate.wav".format(src_idx+1),

mix = mix[..., 0]
        sources = sources[...,0]
        #noise = noise[..., 0]
        if conf['train_conf']['training']['loss_alpha'] == 1:
            # If Deep clustering only, use DC masks.
            est_sources, dic_out = model.dc_head_separate(mix[None, None])
        else:
            # If Chimera, use mask-inference head masks
            est_sources, dic_out = model.separate(mix[None, None])

        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'],
                                  metrics_list=compute_metrics)
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],
                     conf['sample_rate'])
            # Loop over the sources and estimates
            for src_idx, src in enumerate(sources_np):
                sf.write(local_save_dir + "s{}.wav".format(src_idx+1), src,
                         conf['sample_rate'])
            for src_idx, est_src in enumerate(est_sources_np):

for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.
        mix, sources = tensors_to_device(test_set[idx], device=model_device)
        if conf['train_conf']['training']['loss_alpha'] == 1:
            # If Deep clustering only, use DC masks.
            est_sources, dic_out = model.dc_head_separate(mix[None, None])
        else:
            # If Chimera, use mask-inference head masks
            est_sources, dic_out = model.separate(mix[None, None])

        loss, reordered_sources = loss_func(est_sources, sources[None],
                                            return_est=True)
        mix_np = mix[None].cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
        utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
                                  sample_rate=conf['sample_rate'],
                                  metrics_list=compute_metrics)
        utt_metrics['mix_path'] = test_set.mix[idx][0]
        series_list.append(pd.Series(utt_metrics))

        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "mixture.wav", mix_np[0],
                     conf['sample_rate'])
            # Loop over the sources and estimates
            for src_idx, src in enumerate(sources_np):
                sf.write(local_save_dir + "s{}.wav".format(src_idx+1), src,
                         conf['sample_rate'])
            for src_idx, est_src in enumerate(est_sources_np):

model_device = next(model.parameters()).device
    # Randomly choose the indexes of sentences to save.
    if save_dir is None:
        conf['n_save_ex'] = 0
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(dict_list)
    save_idx = random.sample(range(len(dict_list)), conf['n_save_ex'])
    series_list = []
    for idx, wav_dic in enumerate(tqdm(dict_list)):
        # Forward the network on the mixture.
        noisy_np, clean_np, fs = load_wav_dic(wav_dic)
        with torch.no_grad():
            net_input = torch.tensor(noisy_np)[None, None].to(model_device)
            est_clean_np = model.denoise(net_input).squeeze().cpu().data.numpy()

        utt_metrics = get_metrics(mix=noisy_np, clean=clean_np,
                                  estimate=est_clean_np,
                                  sample_rate=fs,
                                  metrics_list=COMPUTE_METRICS)
        utt_metrics['noisy_path'] = wav_dic['noisy']
        utt_metrics['clean_path'] = wav_dic['clean']
        series_list.append(pd.Series(utt_metrics))
        # Save some examples in a folder. Wav files and metrics as text.
        if idx in save_idx:
            local_save_dir = os.path.join(save_dir, 'ex_{}/'.format(idx))
            os.makedirs(local_save_dir, exist_ok=True)
            sf.write(local_save_dir + "noisy.wav", noisy_np,
                     fs)
            sf.write(local_save_dir + "clean.wav", clean_np,
                     fs)
            sf.write(local_save_dir + "estimate.wav", est_clean_np,
                     fs)