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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from tools import safe_int | |
| from webUI.natural_language_guided_STFT.utils import encodeBatch2GradioOutput, latent_representation_to_Gradio_image, \ | |
| add_instrument | |
| def get_testGAN(gradioWebUI, text2sound_state, virtual_instruments_state): | |
| # Load configurations | |
| gan_generator = gradioWebUI.GAN_generator | |
| freq_resolution, time_resolution = gradioWebUI.freq_resolution, gradioWebUI.time_resolution | |
| VAE_scale = gradioWebUI.VAE_scale | |
| height, width, channels = int(freq_resolution / VAE_scale), int(time_resolution / VAE_scale), gradioWebUI.channels | |
| timesteps = gradioWebUI.timesteps | |
| VAE_quantizer = gradioWebUI.VAE_quantizer | |
| VAE_decoder = gradioWebUI.VAE_decoder | |
| CLAP = gradioWebUI.CLAP | |
| CLAP_tokenizer = gradioWebUI.CLAP_tokenizer | |
| device = gradioWebUI.device | |
| squared = gradioWebUI.squared | |
| sample_rate = gradioWebUI.sample_rate | |
| noise_strategy = gradioWebUI.noise_strategy | |
| def gan_random_sample(text2sound_prompts, text2sound_negative_prompts, text2sound_batchsize, | |
| text2sound_duration, | |
| text2sound_guidance_scale, text2sound_sampler, | |
| text2sound_sample_steps, text2sound_seed, | |
| text2sound_dict): | |
| text2sound_seed = safe_int(text2sound_seed, 12345678) | |
| width = int(time_resolution * ((text2sound_duration + 1) / 4) / VAE_scale) | |
| text2sound_batchsize = int(text2sound_batchsize) | |
| text2sound_embedding = \ | |
| CLAP.get_text_features(**CLAP_tokenizer([text2sound_prompts], padding=True, return_tensors="pt"))[0].to( | |
| device) | |
| CFG = int(text2sound_guidance_scale) | |
| condition = text2sound_embedding.repeat(text2sound_batchsize, 1) | |
| noise = torch.randn(text2sound_batchsize, channels, height, width).to(device) | |
| latent_representations = gan_generator(noise, condition) | |
| print(latent_representations[0, 0, :3, :3]) | |
| latent_representation_gradio_images = [] | |
| quantized_latent_representation_gradio_images = [] | |
| new_sound_spectrogram_gradio_images = [] | |
| new_sound_rec_signals_gradio = [] | |
| quantized_latent_representations, loss, (_, _, _) = VAE_quantizer(latent_representations) | |
| # Todo: remove hard-coding | |
| flipped_log_spectrums, rec_signals = encodeBatch2GradioOutput(VAE_decoder, quantized_latent_representations, | |
| resolution=(512, width * VAE_scale), | |
| centralized=False, | |
| squared=squared) | |
| for i in range(text2sound_batchsize): | |
| latent_representation_gradio_images.append(latent_representation_to_Gradio_image(latent_representations[i])) | |
| quantized_latent_representation_gradio_images.append( | |
| latent_representation_to_Gradio_image(quantized_latent_representations[i])) | |
| new_sound_spectrogram_gradio_images.append(flipped_log_spectrums[i]) | |
| new_sound_rec_signals_gradio.append((sample_rate, rec_signals[i])) | |
| text2sound_dict["latent_representations"] = latent_representations.to("cpu").detach().numpy() | |
| text2sound_dict["quantized_latent_representations"] = quantized_latent_representations.to("cpu").detach().numpy() | |
| text2sound_dict["latent_representation_gradio_images"] = latent_representation_gradio_images | |
| text2sound_dict["quantized_latent_representation_gradio_images"] = quantized_latent_representation_gradio_images | |
| text2sound_dict["new_sound_spectrogram_gradio_images"] = new_sound_spectrogram_gradio_images | |
| text2sound_dict["new_sound_rec_signals_gradio"] = new_sound_rec_signals_gradio | |
| text2sound_dict["condition"] = condition.to("cpu").detach().numpy() | |
| # text2sound_dict["negative_condition"] = negative_condition.to("cpu").detach().numpy() | |
| text2sound_dict["guidance_scale"] = CFG | |
| text2sound_dict["sampler"] = text2sound_sampler | |
| return {text2sound_latent_representation_image: text2sound_dict["latent_representation_gradio_images"][0], | |
| text2sound_quantized_latent_representation_image: | |
| text2sound_dict["quantized_latent_representation_gradio_images"][0], | |
| text2sound_sampled_spectrogram_image: text2sound_dict["new_sound_spectrogram_gradio_images"][0], | |
| text2sound_sampled_audio: text2sound_dict["new_sound_rec_signals_gradio"][0], | |
| text2sound_seed_textbox: text2sound_seed, | |
| text2sound_state: text2sound_dict, | |
| text2sound_sample_index_slider: gr.update(minimum=0, maximum=text2sound_batchsize - 1, value=0, step=1, | |
| visible=True, | |
| label="Sample index.", | |
| info="Swipe to view other samples")} | |
| def show_random_sample(sample_index, text2sound_dict): | |
| sample_index = int(sample_index) | |
| text2sound_dict["sample_index"] = sample_index | |
| return {text2sound_latent_representation_image: text2sound_dict["latent_representation_gradio_images"][ | |
| sample_index], | |
| text2sound_quantized_latent_representation_image: | |
| text2sound_dict["quantized_latent_representation_gradio_images"][sample_index], | |
| text2sound_sampled_spectrogram_image: text2sound_dict["new_sound_spectrogram_gradio_images"][ | |
| sample_index], | |
| text2sound_sampled_audio: text2sound_dict["new_sound_rec_signals_gradio"][sample_index]} | |
| with gr.Tab("Text2sound_GAN"): | |
| gr.Markdown("Use neural networks to select random sounds using your favorite instrument!") | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=3): | |
| text2sound_prompts_textbox = gr.Textbox(label="Positive prompt", lines=2, value="organ") | |
| text2sound_negative_prompts_textbox = gr.Textbox(label="Negative prompt", lines=2, value="") | |
| with gr.Column(scale=1): | |
| text2sound_sampling_button = gr.Button(variant="primary", | |
| value="Generate a batch of samples and show " | |
| "the first one", | |
| scale=1) | |
| text2sound_sample_index_slider = gr.Slider(minimum=0, maximum=3, value=0, step=1.0, visible=False, | |
| label="Sample index", | |
| info="Swipe to view other samples") | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1, variant="panel"): | |
| text2sound_sample_steps_slider = gradioWebUI.get_sample_steps_slider() | |
| text2sound_sampler_radio = gradioWebUI.get_sampler_radio() | |
| text2sound_batchsize_slider = gradioWebUI.get_batchsize_slider() | |
| text2sound_duration_slider = gradioWebUI.get_duration_slider() | |
| text2sound_guidance_scale_slider = gradioWebUI.get_guidance_scale_slider() | |
| text2sound_seed_textbox = gradioWebUI.get_seed_textbox() | |
| with gr.Column(scale=1): | |
| text2sound_sampled_spectrogram_image = gr.Image(label="Sampled spectrogram", type="numpy", height=420) | |
| text2sound_sampled_audio = gr.Audio(type="numpy", label="Play") | |
| with gr.Row(variant="panel"): | |
| text2sound_latent_representation_image = gr.Image(label="Sampled latent representation", type="numpy", | |
| height=200, width=100) | |
| text2sound_quantized_latent_representation_image = gr.Image(label="Quantized latent representation", | |
| type="numpy", height=200, width=100) | |
| text2sound_sampling_button.click(gan_random_sample, | |
| inputs=[text2sound_prompts_textbox, | |
| text2sound_negative_prompts_textbox, | |
| text2sound_batchsize_slider, | |
| text2sound_duration_slider, | |
| text2sound_guidance_scale_slider, text2sound_sampler_radio, | |
| text2sound_sample_steps_slider, | |
| text2sound_seed_textbox, | |
| text2sound_state], | |
| outputs=[text2sound_latent_representation_image, | |
| text2sound_quantized_latent_representation_image, | |
| text2sound_sampled_spectrogram_image, | |
| text2sound_sampled_audio, | |
| text2sound_seed_textbox, | |
| text2sound_state, | |
| text2sound_sample_index_slider]) | |
| text2sound_sample_index_slider.change(show_random_sample, | |
| inputs=[text2sound_sample_index_slider, text2sound_state], | |
| outputs=[text2sound_latent_representation_image, | |
| text2sound_quantized_latent_representation_image, | |
| text2sound_sampled_spectrogram_image, | |
| text2sound_sampled_audio]) | |