def test_save_and_load_dprnn(fb):
model1 = DPRNNTasNet(
n_src=2, n_repeats=2, bn_chan=16, hid_size=4, chunk_size=20, n_filters=32, fb_name=fb
)
test_input = torch.randn(1, 800)
model_conf = model1.serialize()
reconstructed_model = DPRNNTasNet.from_pretrained(model_conf)
assert_allclose(model1(test_input), reconstructed_model(test_input))
def DPRNN(conf):
sys.path.append('./asteroid')
from asteroid.models import DPRNNTasNet
from asteroid.utils import tensors_to_device
from asteroid.models import save_publishable
model_path = "./models/dprnn_usecase1"
model = DPRNNTasNet.from_pretrained(model_path)
# Handle device placement
if conf["use_gpu"]:
model.cuda()
model_device = next(model.parameters()).device
torch.no_grad().__enter__()
mix, fs = sf.read(conf["input_path"])
mix = torch.from_numpy(mix).type(torch.FloatTensor)
outputs = model.float()(mix)
return outputs
def model_fn(model_dir):
with open(os.path.join(model_dir, 'model.pth'), 'rb') as f:
model = DPRNNTasNet.from_pretrained(f) #
return model
def test_dprnntasnet_sep_from_hf():
model = DPRNNTasNet.from_pretrained(HF_EXAMPLE_MODEL_IDENTIFER)
assert isinstance(model, DPRNNTasNet)
def main(conf):
model_path = os.path.join(conf["exp_dir"], "best_model.pth")
model = DPRNNTasNet.from_pretrained(model_path)
# Handle device placement
if conf["use_gpu"]:
model.cuda()
model_device = next(model.parameters()).device
test_set = WhamDataset(
conf["test_dir"],
conf["task"],
sample_rate=conf["sample_rate"],
nondefault_nsrc=None,
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.
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])
_, indxs = torch.sort(torch.sqrt(torch.mean(est_sources**2, dim=-1)),
descending=True)
indxs = indxs[:, :2]
# we know a-priori that there are 2 sources in WHAM-clean (WSJ0-2mix clean)
# so we sort the estimated signals and take only the two with highest energy.
est_sources = est_sources.gather(
1,
indxs.unsqueeze(-1).repeat(1, 1, est_sources.shape[-1]))
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"],
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):
est_src *= np.max(np.abs(mix_np)) / np.max(np.abs(est_src))
sf.write(
local_save_dir + "s{}_estimate.wav".format(src_idx + 1),
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(conf["exp_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(conf["exp_dir"], "final_metrics.json"), "w") as f:
json.dump(final_results, f, indent=0)
model_dict = torch.load(model_path, map_location="cpu")
os.makedirs(os.path.join(conf["exp_dir"], "publish_dir"), exist_ok=True)
publishable = save_publishable(
os.path.join(conf["exp_dir"], "publish_dir"),
model_dict,
metrics=final_results,
train_conf=train_conf,
)