Update app.py

app.py

@@ -1,49 +1,90 @@
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import typing as tp
-import warnings
-
+import streamlit as st
 import torch
-import gradio as gr
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import MusicGen
-
-def ui_batched(launch_kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # MusicGen
-            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-            <br/>
-            <a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
-                style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
-                src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            for longer sequences, more control and no queue.</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
+from diffusers import AudioLDM2Pipeline
+
+# make Space compatible with CPU duplicates
+if torch.cuda.is_available():
+    device = "cuda"
+    torch_dtype = torch.float16
+else:
+    device = "cpu"
+    torch_dtype = torch.float32
+
+# load the diffusers pipeline
+repo_id = "cvssp/audioldm2"
+pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch_dtype).to(device)
+
+# set the generator for reproducibility
+generator = torch.Generator(device)
+
+
+def text2audio(text, negative_prompt, duration, guidance_scale, random_seed, n_candidates):
+    if text is None:
+        st.error("Please provide a text input.")
+        return
+
+    waveforms = pipe(
+        text,
+        audio_length_in_s=duration,
+        guidance_scale=guidance_scale,
+        num_inference_steps=200,
+        negative_prompt=negative_prompt,
+        num_waveforms_per_prompt=n_candidates if n_candidates else 1,
+        generator=generator.manual_seed(int(random_seed)),
+    )["audios"]
+
+    st.audio(waveforms[0], format="audio/wav")
+
+
+# Streamlit UI
+st.title("AudioLDM 2: A General Framework for Audio, Music, and Speech Generation")
+
+st.markdown(
+    "[Paper](https://arxiv.org/abs/2308.05734) [Project Page](https://audioldm.github.io/audioldm2) [Diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2)"
+)
+
+st.markdown("This is the demo for AudioLDM 2, powered by 🧨 Diffusers. For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings.")
+
+st.markdown("### Input")
+text = st.text_input("Input text", "The vibrant beat of Brazilian samba drums")
+negative_prompt = st.text_input("Negative prompt", "Low quality")
+
+st.markdown("### Configuration")
+duration = st.slider("Duration (seconds)", 5, 15, 10, step=2.5)
+guidance_scale = st.slider("Guidance scale", 0, 7, 3.5, step=0.5)
+n_candidates = st.slider("Number waveforms to generate", 1, 5, 3, step=1)
+random_seed = st.number_input("Seed", 1, 100, 45)
+
+if st.button("Submit"):
+    text2audio(text, negative_prompt, duration, guidance_scale, random_seed, n_candidates)
+
+st.markdown("### Output")
+st.markdown("Share your generations with the community by clicking the share icon at the top right of the generated audio!")
+
+st.markdown("Model by [Haohe Liu](https://twitter.com/LiuHaohe). Code and demo by 🤗 Hugging Face.")
+
+st.markdown("### Examples")
+examples = [
+    ["A hammer is hitting a wooden surface.", "Low quality.", 10, 3.5, 45, 3],
+    ["A cat is meowing for attention.", "Low quality.", 10, 3.5, 45, 3],
+    ["An excited crowd cheering at a sports game.", "Low quality.", 10, 3.5, 45, 3],
+    ["Birds singing sweetly in a blooming garden.", "Low quality.", 10, 3.5, 45, 3],
+    ["A modern synthesizer creating futuristic soundscapes.", "Low quality.", 10, 3.5, 45, 3],
+    ["The vibrant beat of Brazilian samba drums.", "Low quality.", 10, 3.5, 45, 3],
+]
+
+for example in examples:
+    st.markdown("---")
+    st.markdown(f"**Input**: {example[0]}")
+    st.markdown(f"**Negative Prompt**: {example[1]}")
+    st.markdown(f"**Duration**: {example[2]} seconds")
+    st.markdown(f"**Guidance Scale**: {example[3]}")
+    st.markdown(f"**Seed**: {example[4]}")
+    st.markdown(f"**Number Waveforms**: {example[5]}")
+    if st.button("Generate", key=f"example_{examples.index(example)}"):
+        text2audio(*example)
+
+st.markdown("### Additional Information")
+st.markdown(
+    "We build the model with data from [AudioSet](http://research.google.com/audioset/), [Freesound](https://freesound.org/), and [BBC Sound Effect library](https://sound-effects.bbcrewind.co.uk/). We share this demo based on the [UK copyright exception](https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf) of data for academic research."
+)