generate_script.py

# Adapted from https://github.com/vincentherrmann/pytorch-wavenet
import librosa
from wavenet_model import *
from audio_data import WavenetDataset
from wavenet_training import *
import soundfile as sf
import time
model = load_latest_model_from("snapshots", use_cuda=False)

print("model: ", model)
print("receptive field: ", model.receptive_field)
print("parameter count: ", model.parameter_count())

data = WavenetDataset(
    dataset_file="train_samples/bach_chaconne/dataset.npz",
    item_length=model.receptive_field + model.output_length - 1,
    target_length=model.output_length,
    file_location="train_samples/bach_chaconne",
    test_stride=20,
)
print("the dataset has " + str(len(data)) + " items")

start_data = data[250000][0]
start_data = torch.max(start_data, 0)[1]


def prog_callback(step, total_steps):
    print(str(100 * step // total_steps) + "% generated")

start = time.time
generated = model.generate(
    num_samples=100000,
    first_samples=start_data,
    temperature=1.0,
)
end = time.time() - start
print("Time taken: ", end)
# generated = model.generate_fast(
#     num_samples=100000,
#     first_samples=start_data,
#     progress_callback=prog_callback,
#     progress_interval=1000,
#     temperature=1.0,
#     regularize=0.0,
# )

print(generated)
# librosa.output.write_wav('latest_generated_clip.wav', generated, sr=16000)
sf.write("latest_generated_clip.wav", generated, samplerate=16000)