uncommenting "MusicGen" tab
Browse files
app.py
CHANGED
@@ -1179,7 +1179,7 @@ def ui_full(launch_kwargs):
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refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
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"""
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)
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-
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gr.Markdown(
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"""
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### MusicGen
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@@ -1446,7 +1446,7 @@ def ui_full(launch_kwargs):
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- **[Classifier Free Guidance (number)]:**
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refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
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"""
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)
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with gr.Tab("Audio Info"):
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gr.Markdown(
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"""
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@@ -1485,14 +1485,15 @@ def ui_full(launch_kwargs):
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#gr.Image(shape=(5,5))
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gr.Image(shape=(5,5), value = "https://tulipai.co/assets/images/image01.png")'''
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-
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reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
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send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
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submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
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input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
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to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
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gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
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send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
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reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
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send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
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refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
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"""
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)
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with gr.Tab("MusicGen"):
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gr.Markdown(
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"""
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### MusicGen
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- **[Classifier Free Guidance (number)]:**
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refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
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"""
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+
)
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with gr.Tab("Audio Info"):
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gr.Markdown(
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"""
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#gr.Image(shape=(5,5))
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gr.Image(shape=(5,5), value = "https://tulipai.co/assets/images/image01.png")'''
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send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
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reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
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send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
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submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
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input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
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+
to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
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gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
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send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
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reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
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send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
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