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( """

AudioLDM 2: A General Framework for Audio, Music, and Speech Generation

[Paper] [Project page] [🧨 Diffusers]

""" ) gr.HTML("""This is the demo for AudioLDM 2, powered by 🧨 Diffusers. Demo uses the checkpoint AudioLDM 2 base. 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( """

Share your generations with the community by clicking the share icon at the top right the generated audio!

Follow the latest update of AudioLDM 2 on our Github repo

Model by Haohe Liu. Code and demo by 🤗 Hugging Face.

""" ) 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( """

Essential Tricks for Enhancing the Quality of Your Generated Audio

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".

2. Try using different random seeds, which can significantly affect the quality of the generated output.

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.

4. Using a negative prompt to not guide the diffusion process can improve the audio quality significantly. Try using negative prompts like 'low quality'.

""" ) with gr.Accordion("Additional information", open=False): gr.HTML( """

We build the model with data from AudioSet, Freesound and BBC Sound Effect library. We share this demo based on the UK copyright exception of data for academic research.

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