# Buggy code
def main(conf):
    model_path = os.path.join(conf["exp_dir"], "best_model.pth")
    model = ConvTasNet.from_pretrained(model_path)
    # Handle device placement
    if conf["use_gpu"]:
        model.cuda()
    model_device = next(model.parameterss()).device
    test_set = LibriMix(
        csv_dir=conf["test_dir"],
        task=conf["task"],
        sample_rate=conf["sample_rate"],
        n_src=conf["train_conf"]["data"]["n_src"],
        segment=None,
    )  # Uses all segment length
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx")

    # Randomly choose the indexes of sentences to save.
    eval_save_dir = os.path.join(conf["exp_dir"], conf["out_dir"])
    ex_save_dir = os.path.join(eval_save_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.unsqueeze(0))
        loss, reordered_sources = loss_func(est_sources, sources[None], return_est=True)
        mix_np = mix.cpu().data.numpy()
        sources_np = sources.squeeze().cpu().data.numpy()
        est_sources_np = reordered_sources.squeeze().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"]
        )
        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):
                sf.write(
                    local_save_dir + "s{}_estimate.wav".format(src_idx),
                    est_src,
                    conf["sample_rate"],
                )
            # Write local metrics to the example folder.
            with open(local_save_dir + "metrics.json", "w") as f:
                json.dump(utt_metrics, f, indent=0)

    # Save all metrics to the experiment folder.
    all_metrics_df = pd.DataFrame(series_list)
    all_metrics_df.to_csv(os.path.join(eval_save_dir, "all_metrics.csv"))

    # Print and save summary metrics
    final_results = {}
    for metric_name in compute_metrics:
        input_metric_name = "input_" + metric_name
        ldf = all_metrics_df[metric_name] - all_metrics_df[input_metric_name]
        final_results[metric_name] = all_metrics_df[metric_name].mean()
        final_results[metric_name + "_imp"] = ldf.mean()
    print("Overall metrics :")
    pprint(final_results)
    with open(os.path.join(eval_save_dir, "final_metrics.json"), "w") as f:
        json.dump(final_results, f, indent=0)

    model_dict = torch.load(model_path, map_location="cpu")
    publishable = save_publishable(
        os.path.join(conf["exp_dir"], "publish_dir"),
        model_dict,
        metrics=final_results,
        train_conf=train_conf,
    )


# Traceback (IndexError)
Traceback (most recent call last):
File "eval.py", line 118, in <module>
main(arg_dic)
File "eval.py", line 78, in main
conf['sample_rate'])
File ".../lib/python3.7/site-packages/soundfile.py", line 313, in write
channels = data.shape[1]
IndexError: tuple index out of range

# Fixed code
def main(conf):
    model_path = os.path.join(conf["exp_dir"], "best_model.pth")
    model = ConvTasNet.from_pretrained(model_path)
    # Handle device placement
    if conf["use_gpu"]:
        model.cuda()
    model_device = next(model.parameterss()).device
    test_set = LibriMix(
        csv_dir=conf["test_dir"],
        task=conf["task"],
        sample_rate=conf["sample_rate"],
        n_src=conf["train_conf"]["data"]["n_src"],
        segment=None,
    )  # Uses all segment length
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx")

    # Randomly choose the indexes of sentences to save.
    eval_save_dir = os.path.join(conf["exp_dir"], conf["out_dir"])
    ex_save_dir = os.path.join(eval_save_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.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().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"]
        )
        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):
                sf.write(
                    local_save_dir + "s{}_estimate.wav".format(src_idx),
                    est_src,
                    conf["sample_rate"],
                )
            # Write local metrics to the example folder.
            with open(local_save_dir + "metrics.json", "w") as f:
                json.dump(utt_metrics, f, indent=0)

    # Save all metrics to the experiment folder.
    all_metrics_df = pd.DataFrame(series_list)
    all_metrics_df.to_csv(os.path.join(eval_save_dir, "all_metrics.csv"))

    # Print and save summary metrics
    final_results = {}
    for metric_name in compute_metrics:
        input_metric_name = "input_" + metric_name
        ldf = all_metrics_df[metric_name] - all_metrics_df[input_metric_name]
        final_results[metric_name] = all_metrics_df[metric_name].mean()
        final_results[metric_name + "_imp"] = ldf.mean()
    print("Overall metrics :")
    pprint(final_results)
    with open(os.path.join(eval_save_dir, "final_metrics.json"), "w") as f:
        json.dump(final_results, f, indent=0)

    model_dict = torch.load(model_path, map_location="cpu")
    publishable = save_publishable(
        os.path.join(conf["exp_dir"], "publish_dir"),
        model_dict,
        metrics=final_results,
        train_conf=train_conf,
    )