Reconfig app.py to get ready to try to train

.gitignore

@@ -0,0 +1 @@
+old_app.py

app.py

@@ -1,212 +1,8 @@
-# Adapted app.py from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/main/app.py
 import gradio as gr
-import torch
-from diffusers import AudioLDMPipeline
 
-from transformers import AutoProcessor, ClapModel
+def greet(name):
+    return ("hello" + name)
 
-# cuda code from AudioLDM's original app.py if using GPU
-# allows support for CPU
-if torch.cuda.is_available():
-    device = "cuda"
-    torch_dtype = torch.float16
-else:
-    device = "cpu"
-    torch_dtype = torch.float32
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 
-# load AudioLDM Diffuser Pipeline
-pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm-m-full", torch_dtype=torch_dtype).to(device)
-pipe.unet = torch.compile(pipe.unet)
-
-# include CLAP model because it improves quality 
-clap_model = ClapModel.from_pretrained("sanchit-gandhi/clap-htsat-unfused-m-full").to(device)
-processor = AutoProcessor.from_pretrained("sanchit-gandhi/clap-htsat-unfused-m-full")
-
-generator = torch.Generator(device)
-
-# from audioldm app.py
-def text2audio(text, negative_prompt, duration, guidance_scale, random_seed, n_candidates):
-    if text is None:
-        raise gr.Error("Please provide a text input.")
-
-    waveforms = pipe(
-        text,
-        audio_length_in_s=duration,
-        guidance_scale=guidance_scale,
-        negative_prompt=negative_prompt,
-        num_waveforms_per_prompt=n_candidates if n_candidates else 1,
-        generator=generator.manual_seed(int(random_seed)),
-    )["audios"]
-
-    if waveforms.shape[0] > 1:
-        waveform = score_waveforms(text, waveforms)
-    else:
-        waveform = waveforms[0]
-
-    return gr.make_waveform((16000, waveform), bg_image="bg.png")
-
-def score_waveforms(text, waveforms):
-    inputs = processor(text=text, audios=list(waveforms), return_tensors="pt", padding=True)
-    inputs = {key: inputs[key].to(device) for key in inputs}
-    with torch.no_grad():
-        logits_per_text = clap_model(**inputs).logits_per_text  # this is the audio-text similarity score
-        probs = logits_per_text.softmax(dim=-1)  # we can take the softmax to get the label probabilities
-        most_probable = torch.argmax(probs)  # and now select the most likely audio waveform
-    waveform = waveforms[most_probable]
-    return waveform
-
-# duplicate CSS config
-
-css = """
-        a {
-            color: inherit; text-decoration: underline;
-        } .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        } .gr-button {
-            color: white; border-color: #000000; background: #000000;
-        } input[type='range'] {
-            accent-color: #000000;
-        } .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        } .container {
-            max-width: 730px; margin: auto; padding-top: 1.5rem;
-        } #gallery {
-            min-height: 22rem; margin-bottom: 15px; margin-left: auto; margin-right: auto; border-bottom-right-radius:
-            .5rem !important; border-bottom-left-radius: .5rem !important;
-        } #gallery>div>.h-full {
-            min-height: 20rem;
-        } .details:hover {
-            text-decoration: underline;
-        } .gr-button {
-            white-space: nowrap;
-        } .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity)); outline: none; box-shadow:
-            var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width)
-            var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px
-            var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-color: rgb(191 219 254 /
-            var(--tw-ring-opacity)); --tw-ring-opacity: .5;
-        } #advanced-btn {
-            font-size: .7rem !important; line-height: 19px; margin-top: 12px; margin-bottom: 12px; padding: 2px 8px;
-            border-radius: 14px !important;
-        } #advanced-options {
-            margin-bottom: 20px;
-        } .footer {
-            margin-bottom: 45px; margin-top: 35px; text-align: center; border-bottom: 1px solid #e5e5e5;
-        } .footer>p {
-            font-size: .8rem; display: inline-block; padding: 0 10px; transform: translateY(10px); background: white;
-        } .dark .footer {
-            border-color: #303030;
-        } .dark .footer>p {
-            background: #0b0f19;
-        } .acknowledgments h4{
-            margin: 1.25em 0 .25em 0; font-weight: bold; font-size: 115%;
-        } #container-advanced-btns{
-            display: flex; flex-wrap: wrap; justify-content: space-between; align-items: center;
-        } .animate-spin {
-            animation: spin 1s linear infinite;
-        } @keyframes spin {
-            from {
-                transform: rotate(0deg);
-            } to {
-                transform: rotate(360deg);
-            }
-        } #share-btn-container {
-            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color:
-            #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-            margin-top: 10px; margin-left: auto;
-        } #share-btn {
-            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif;
-            margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem
-            !important;right:0;
-        } #share-btn * {
-            all: unset;
-        } #share-btn-container div:nth-child(-n+2){
-            width: auto !important; min-height: 0px !important;
-        } #share-btn-container .wrap {
-            display: none !important;
-        } .gr-form{
-            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
-        } #prompt-container{
-            gap: 0;
-        } #generated_id{
-            min-height: 700px
-        } #setting_id{
-          margin-bottom: 12px; text-align: center; font-weight: 900;
-        }
-"""
-iface = gr.Blocks(css=css)
-
-# modified html to only include vital parts
-with iface:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
-                  AudioLDM Animals: Text-to-Audio Generation with Latent Diffusion Models (hopefully) Fine-Tuned for animal sounds
-                </h1>
-              </div> <p style="margin-bottom: 10px; font-size: 94%">
-                <a href="https://arxiv.org/abs/2301.12503">[Paper]</a> <a href="https://audioldm.github.io/">[Original Project
-                page]</a> <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm">[🧨
-                Diffusers]</a>
-              </p>
-            </div>
-        """
-    )
-
-    with gr.Group():
-        with gr.Box():
-            textbox = gr.Textbox(
-                value="A dog is barking",
-                max_lines=1,
-                label="Input text",
-                info="Your text is important for the audio quality. Please ensure it is descriptive by using more adjectives.",
-                elem_id="prompt-in",
-            )
-            negative_textbox = gr.Textbox(
-                value="low quality, average quality",
-                max_lines=1,
-                label="Negative prompt",
-                info="Enter a negative prompt not to guide the audio generation. Selecting appropriate negative prompts can improve the audio quality significantly.",
-                elem_id="prompt-in",
-            )
-
-            with gr.Accordion("Click to modify detailed configurations", open=False):
-                seed = gr.Number(
-                    value=45,
-                    label="Seed",
-                    info="Change this value (any integer number) will lead to a different generation result.",
-                )
-                duration = gr.Slider(2.5, 10, value=5, step=2.5, label="Duration (seconds)")
-                guidance_scale = gr.Slider(
-                    0,
-                    4,
-                    value=2.5,
-                    step=0.5,
-                    label="Guidance scale",
-                    info="Large => better quality and relevancy to text; Small => better diversity",
-                )
-                n_candidates = gr.Slider(
-                    1,
-                    3,
-                    value=3,
-                    step=1,
-                    label="Number waveforms to generate",
-                    info="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation",
-                )
-
-            outputs = gr.Video(label="Output", elem_id="output-video")
-            btn = gr.Button("Submit").style(full_width=True)
-
-        btn.click(
-            text2audio,
-            inputs=[textbox, negative_textbox, duration, guidance_scale, seed, n_candidates],
-            outputs=[outputs],
-        )
-        
-iface.queue(max_size=1).launch(debug=True)
+iface.launch()

requirements.txt

@@ -1,3 +1,4 @@
 transformers
 torch
-diffusers
+diffusers
+datasets[audio]