app.py

import gradio as gr
import torch
from diffusers import AudioLDM2Pipeline

import ast
import copy
import csv
import inspect
import os
import shutil
import subprocess
import tempfile
import warnings
from functools import partial
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Iterable, Literal, Optional, Sequence

import numpy as np
import PIL
import PIL.Image
from gradio_client import utils as client_utils
from gradio_client.documentation import document

from gradio import components, oauth, processing_utils, routes, utils, wasm_utils
from gradio.context import Context, LocalContext, get_blocks_context
from gradio.data_classes import GradioModel, GradioRootModel
from gradio.events import Dependency, EventData
from gradio.exceptions import Error
from gradio.flagging import CSVLogger
from gradio.utils import UnhashableKeyDict


# 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)
# pipe.unet = torch.compile(pipe.unet)

# set the generator for reproducibility
generator = torch.Generator(device)

@document()
def make_waveform(
    audio: str | tuple[int, np.ndarray],
    *,
    bg_color: str = "#f3f4f6",
    bg_image: str | None = None,
    fg_alpha: float = 0.75,
    bars_color: str | tuple[str, str] = ("#fbbf24", "#ea580c"),
    bar_count: int = 50,
    bar_width: float = 0.6,
    animate: bool = False,
) -> str:
    """
    Generates a waveform video from an audio file. Useful for creating an easy to share audio visualization. The output should be passed into a `gr.Video` component.
    Parameters:
        audio: Audio file path or tuple of (sample_rate, audio_data)
        bg_color: Background color of waveform (ignored if bg_image is provided)
        bg_image: Background image of waveform
        fg_alpha: Opacity of foreground waveform
        bars_color: Color of waveform bars. Can be a single color or a tuple of (start_color, end_color) of gradient
        bar_count: Number of bars in waveform
        bar_width: Width of bars in waveform. 1 represents full width, 0.5 represents half width, etc.
        animate: If true, the audio waveform overlay will be animated, if false, it will be static.
    Returns:
        A filepath to the output video in mp4 format.
    """
    import matplotlib.pyplot as plt
    from matplotlib.animation import FuncAnimation

    if isinstance(audio, str):
        audio_file = audio
        audio = processing_utils.audio_from_file(audio)
    else:
        tmp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
        processing_utils.audio_to_file(audio[0], audio[1], tmp_wav.name, format="wav")
        audio_file = tmp_wav.name

    if not os.path.isfile(audio_file):
        raise ValueError("Audio file not found.")

    ffmpeg = shutil.which("ffmpeg")
    if not ffmpeg:
        raise RuntimeError("ffmpeg not found.")

    duration = round(len(audio[1]) / audio[0], 4)

    # Helper methods to create waveform
    def hex_to_rgb(hex_str):
        return [int(hex_str[i : i + 2], 16) for i in range(1, 6, 2)]

    def get_color_gradient(c1, c2, n):
        if n < 1:
            raise ValueError("Must have at least one stop in gradient")
        c1_rgb = np.array(hex_to_rgb(c1)) / 255
        c2_rgb = np.array(hex_to_rgb(c2)) / 255
        mix_pcts = [x / (n - 1) for x in range(n)]
        rgb_colors = [((1 - mix) * c1_rgb + (mix * c2_rgb)) for mix in mix_pcts]
        return [
            "#" + "".join(f"{int(round(val * 255)):02x}" for val in item)
            for item in rgb_colors
        ]

    # Reshape audio to have a fixed number of bars
    samples = audio[1]
    if len(samples.shape) > 1:
        samples = np.mean(samples, 1)
    bins_to_pad = bar_count - (len(samples) % bar_count)
    samples = np.pad(samples, [(0, bins_to_pad)])
    samples = np.reshape(samples, (bar_count, -1))
    samples = np.abs(samples)
    samples = np.max(samples, 1)

    with utils.MatplotlibBackendMananger():
        plt.clf()
        # Plot waveform
        color = (
            bars_color
            if isinstance(bars_color, str)
            else get_color_gradient(bars_color[0], bars_color[1], bar_count)
        )

        if animate:
            fig = plt.figure(figsize=(5, 1), dpi=200, frameon=False)
            fig.subplots_adjust(left=0, bottom=0, right=1, top=1)
        plt.axis("off")
        plt.margins(x=0)

        bar_alpha = fg_alpha if animate else 1.0
        barcollection = plt.bar(
            np.arange(0, bar_count),
            samples * 2,
            bottom=(-1 * samples),
            width=bar_width,
            color=color,
            alpha=bar_alpha,
        )

        tmp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)

        savefig_kwargs: dict[str, Any] = {"bbox_inches": "tight"}
        if bg_image is not None:
            savefig_kwargs["transparent"] = True
            if animate:
                savefig_kwargs["facecolor"] = "none"
        else:
            savefig_kwargs["facecolor"] = bg_color
        plt.savefig(tmp_img.name, **savefig_kwargs)

        if not animate:
            waveform_img = PIL.Image.open(tmp_img.name)
            waveform_img = waveform_img.resize((1000, 400))

            # Composite waveform with background image
            if bg_image is not None:
                waveform_array = np.array(waveform_img)
                waveform_array[:, :, 3] = waveform_array[:, :, 3] * fg_alpha
                waveform_img = PIL.Image.fromarray(waveform_array)

                bg_img = PIL.Image.open(bg_image)
                waveform_width, waveform_height = waveform_img.size
                bg_width, bg_height = bg_img.size
                if waveform_width != bg_width:
                    bg_img = bg_img.resize(
                        (
                            waveform_width,
                            2 * int(bg_height * waveform_width / bg_width / 2),
                        )
                    )
                    bg_width, bg_height = bg_img.size
                composite_height = max(bg_height, waveform_height)
                composite = PIL.Image.new(
                    "RGBA", (waveform_width, composite_height), "#FFFFFF"
                )
                composite.paste(bg_img, (0, composite_height - bg_height))
                composite.paste(
                    waveform_img, (0, composite_height - waveform_height), waveform_img
                )
                composite.save(tmp_img.name)
                img_width, img_height = composite.size
            else:
                img_width, img_height = waveform_img.size
                waveform_img.save(tmp_img.name)
        else:

            def _animate(_):
                for idx, b in enumerate(barcollection):
                    rand_height = np.random.uniform(0.8, 1.2)
                    b.set_height(samples[idx] * rand_height * 2)
                    b.set_y((-rand_height * samples)[idx])

            frames = int(duration * 10)
            anim = FuncAnimation(
                fig,  # type: ignore
                _animate,  # type: ignore
                repeat=False,
                blit=False,
                frames=frames,
                interval=100,
            )
            anim.save(
                tmp_img.name,
                writer="pillow",
                fps=10,
                codec="png",
                savefig_kwargs=savefig_kwargs,
            )

    # Convert waveform to video with ffmpeg
    output_mp4 = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)

    if animate and bg_image is not None:
        ffmpeg_cmd = [
            ffmpeg,
            "-loop",
            "1",
            "-i",
            bg_image,
            "-i",
            tmp_img.name,
            "-i",
            audio_file,
            "-filter_complex",
            "[0:v]scale=w=trunc(iw/2)*2:h=trunc(ih/2)*2[bg];[1:v]format=rgba,colorchannelmixer=aa=1.0[ov];[bg][ov]overlay=(main_w-overlay_w*0.9)/2:main_h-overlay_h*0.9/2[output]",
            "-t",
            str(duration),
            "-map",
            "[output]",
            "-map",
            "2:a",
            "-c:v",
            "libx264",
            "-c:a",
            "aac",
            "-shortest",
            "-y",
            output_mp4.name,
        ]
    elif animate and bg_image is None:
        ffmpeg_cmd = [
            ffmpeg,
            "-i",
            tmp_img.name,
            "-i",
            audio_file,
            "-filter_complex",
            "[0:v][1:a]concat=n=1:v=1:a=1[v];[v]scale=1000:400,format=yuv420p[v_scaled]",
            "-map",
            "[v_scaled]",
            "-map",
            "1:a",
            "-c:v",
            "libx264",
            "-c:a",
            "aac",
            "-shortest",
            "-y",
            output_mp4.name,
        ]
    else:
        ffmpeg_cmd = [
            ffmpeg,
            "-loop",
            "1",
            "-i",
            tmp_img.name,
            "-i",
            audio_file,
            "-vf",
            f"color=c=#FFFFFF77:s={img_width}x{img_height}[bar];[0][bar]overlay=-w+(w/{duration})*t:H-h:shortest=1",  # type: ignore
            "-t",
            str(duration),
            "-y",
            output_mp4.name,
        ]

    subprocess.check_call(ffmpeg_cmd)
    return output_mp4.name


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,
        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"]

    return make_waveform((16000, waveforms[0]), bg_image="bg.png")
    # return gr.Audio(sources=["microphone"], type="filepath")


iface = gr.Blocks()

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 2: A General Framework for Audio, Music, and Speech Generation
                </h1>
              </div> <p style="margin-bottom: 10px; font-size: 94%">
                <a href="https://arxiv.org/abs/2308.05734">[Paper]</a> <a href="https://audioldm.github.io/audioldm2">[Project
                page]</a> <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2">[🧨
                Diffusers]</a>
              </p>
            </div>
        """
    )
    gr.HTML("""This is the demo for AudioLDM 2, powered by 🧨 Diffusers. Demo uses the checkpoint <a
        href="https://huggingface.co/cvssp/audioldm2"> AudioLDM 2 base</a>. For faster inference without waiting in
        queue, you may duplicate the space and upgrade to a GPU in the settings.""")
    gr.DuplicateButton()

    with gr.Group():
        textbox = gr.Textbox(
            value="The vibrant beat of Brazilian samba drums.",
            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.",
            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(5, 15, value=10, step=2.5, label="Duration (seconds)")
            guidance_scale = gr.Slider(
                0,
                7,
                value=3.5,
                step=0.5,
                label="Guidance scale",
                info="Larger => better quality and relevancy to text; Smaller => better diversity",
            )
            n_candidates = gr.Slider(
                1,
                5,
                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")

    btn.click(
        text2audio,
        inputs=[textbox, negative_textbox, duration, guidance_scale, seed, n_candidates],
        # inputs=[textbox, negative_textbox, 10, guidance_scale, seed, n_candidates],
        outputs=[outputs],
    )

    gr.HTML(
        """
    <div class="footer" style="text-align: center">
        <p>Share your generations with the community by clicking the share icon at the top right the generated audio!</p>
        <p>Follow the latest update of AudioLDM 2 on our<a href="https://audioldm.github.io/audioldm2"
        style="text-decoration: underline;" target="_blank"> Github repo</a> </p> 
        <p>Model by <a
        href="https://twitter.com/LiuHaohe" style="text-decoration: underline;" target="_blank">Haohe
        Liu</a>. Code and demo by 🤗 Hugging Face.</p>
    </div>
    """
    )
    gr.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],
        ],
        fn=text2audio,
        inputs=[textbox, negative_textbox, duration, guidance_scale, seed, n_candidates],
        outputs=[outputs],
        cache_examples=True,
    )
    gr.HTML(
        """
            <div class="acknowledgements"> <p>Essential Tricks for Enhancing the Quality of Your Generated
            Audio</p>
            <p>1. Try using more adjectives to describe your sound. For example: "A man is speaking
            clearly and slowly in a large room" is better than "A man is speaking".</p>
            <p>2. Try using different random seeds, which can significantly affect the quality of the generated 
            output.</p>
            <p>3. It's better to use general terms like 'man' or 'woman' instead of specific names for individuals or 
            abstract objects that humans may not be familiar with.</p>
            <p>4. Using a negative prompt to not guide the diffusion process can improve the
            audio quality significantly. Try using negative prompts like 'low quality'.</p>
            </div>
            """
    )
    with gr.Accordion("Additional information", open=False):
        gr.HTML(
            """
            <div class="acknowledgments">
                <p> We build the model with data from <a href="http://research.google.com/audioset/">AudioSet</a>,
                <a href="https://freesound.org/">Freesound</a> and <a
                href="https://sound-effects.bbcrewind.co.uk/">BBC Sound Effect library</a>. We share this demo
                based on the <a
                href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf">UK
                copyright exception</a> of data for academic research. 
                </p>
            </div>
            """
        )

iface.queue(max_size=20).launch()