405 lines
13 KiB
Python
405 lines
13 KiB
Python
import io
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import typing as T
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from pathlib import Path
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import numpy as np
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import pydub
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import streamlit as st
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from PIL import Image
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from riffusion.datatypes import InferenceInput, PromptInput
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from riffusion.spectrogram_params import SpectrogramParams
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from riffusion.streamlit import util as streamlit_util
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from riffusion.streamlit.pages.interpolation import get_prompt_inputs, run_interpolation
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from riffusion.util import audio_util
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def render_audio_to_audio() -> None:
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st.set_page_config(layout="wide", page_icon="🎸")
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st.subheader(":wave: Audio to Audio")
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st.write(
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"""
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Modify existing audio from a text prompt or interpolate between two.
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"""
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)
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with st.expander("Help", False):
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st.write(
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"""
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This tool allows you to upload an audio file of arbitrary length and modify it with
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a text prompt. It does this by sweeping over the audio in overlapping clips, doing
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img2img style transfer with riffusion, then stitching the clips back together with
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cross fading to eliminate seams.
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Try a denoising strength of 0.4 for light modification and 0.55 for more heavy
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modification. The best specific denoising depends on how different the prompt is
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from the source audio. You can play with the seed to get infinite variations.
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Currently the same seed is used for all clips along the track.
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If the Interpolation check box is enabled, supports entering two sets of prompt,
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seed, and denoising value and smoothly blends between them along the selected
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duration of the audio. This is a great way to create a transition.
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"""
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)
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device = streamlit_util.select_device(st.sidebar)
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extension = streamlit_util.select_audio_extension(st.sidebar)
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use_magic_mix = st.sidebar.checkbox("Use Magic Mix", False)
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lora_path = st.sidebar.text_input("Lora Path", "")
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lora_scale = st.sidebar.number_input("Lora Scale", value=1.0)
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with st.sidebar:
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num_inference_steps = T.cast(
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int,
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st.number_input(
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"Steps per sample", value=50, help="Number of denoising steps per model run"
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),
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)
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guidance = st.number_input(
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"Guidance",
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value=7.0,
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help="How much the model listens to the text prompt",
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)
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scheduler = st.selectbox(
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"Scheduler",
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options=streamlit_util.SCHEDULER_OPTIONS,
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index=0,
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help="Which diffusion scheduler to use",
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)
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assert scheduler is not None
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audio_file = st.file_uploader(
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"Upload audio",
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type=streamlit_util.AUDIO_EXTENSIONS,
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label_visibility="collapsed",
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)
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if not audio_file:
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st.info("Upload audio to get started")
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return
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st.write("#### Original")
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st.audio(audio_file)
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segment = streamlit_util.load_audio_file(audio_file)
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# TODO(hayk): Fix
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if segment.frame_rate != 44100:
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st.warning("Audio must be 44100Hz. Converting")
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segment = segment.set_frame_rate(44100)
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st.write(f"Duration: {segment.duration_seconds:.2f}s, Sample Rate: {segment.frame_rate}Hz")
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clip_p = get_clip_params()
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start_time_s = clip_p["start_time_s"]
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clip_duration_s = clip_p["clip_duration_s"]
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overlap_duration_s = clip_p["overlap_duration_s"]
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duration_s = min(clip_p["duration_s"], segment.duration_seconds - start_time_s)
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increment_s = clip_duration_s - overlap_duration_s
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clip_start_times = start_time_s + np.arange(0, duration_s - clip_duration_s, increment_s)
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write_clip_details(
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clip_start_times=clip_start_times,
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clip_duration_s=clip_duration_s,
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overlap_duration_s=overlap_duration_s,
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)
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interpolate = st.checkbox(
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"Interpolate between two endpoints",
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value=False,
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help="Interpolate between two prompts, seeds, or denoising values along the"
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"duration of the segment",
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)
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counter = streamlit_util.StreamlitCounter()
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with st.form("audio to audio form"):
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if interpolate:
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left, right = st.columns(2)
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with left:
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st.write("##### Prompt A")
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prompt_input_a = PromptInput(guidance=guidance, **get_prompt_inputs(key="a"))
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with right:
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st.write("##### Prompt B")
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prompt_input_b = PromptInput(guidance=guidance, **get_prompt_inputs(key="b"))
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elif use_magic_mix:
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prompt = st.text_input("Prompt", key="prompt_a")
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row = st.columns(4)
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seed = T.cast(
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int,
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row[0].number_input(
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"Seed",
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value=42,
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key="seed_a",
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),
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)
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prompt_input_a = PromptInput(
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prompt=prompt,
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seed=seed,
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guidance=guidance,
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)
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magic_mix_kmin = row[1].number_input("Kmin", value=0.3)
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magic_mix_kmax = row[2].number_input("Kmax", value=0.5)
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magic_mix_mix_factor = row[3].number_input("Mix Factor", value=0.5)
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else:
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prompt_input_a = PromptInput(
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guidance=guidance,
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**get_prompt_inputs(key="a", include_negative_prompt=True, cols=True),
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)
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st.form_submit_button("Riff", type="primary", on_click=counter.increment)
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show_clip_details = st.sidebar.checkbox("Show Clip Details", True)
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show_difference = st.sidebar.checkbox("Show Difference", False)
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clip_segments = slice_audio_into_clips(
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segment=segment,
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clip_start_times=clip_start_times,
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clip_duration_s=clip_duration_s,
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)
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if not prompt_input_a.prompt:
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st.info("Enter a prompt")
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return
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if counter.value == 0:
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return
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params = SpectrogramParams()
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if interpolate:
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# TODO(hayk): Make not linspace
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alphas = list(np.linspace(0, 1, len(clip_segments)))
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alphas_str = ", ".join([f"{alpha:.2f}" for alpha in alphas])
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st.write(f"**Alphas** : [{alphas_str}]")
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result_images: T.List[Image.Image] = []
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result_segments: T.List[pydub.AudioSegment] = []
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for i, clip_segment in enumerate(clip_segments):
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st.write(f"### Clip {i} at {clip_start_times[i]:.2f}s")
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audio_bytes = io.BytesIO()
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clip_segment.export(audio_bytes, format="wav")
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init_image = streamlit_util.spectrogram_image_from_audio(
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clip_segment,
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params=params,
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device=device,
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)
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# TODO(hayk): Roll this into spectrogram_image_from_audio?
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init_image_resized = scale_image_to_32_stride(init_image)
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progress_callback = None
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if show_clip_details:
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left, right = st.columns(2)
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left.write("##### Source Clip")
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left.image(init_image, use_column_width=False)
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left.audio(audio_bytes)
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right.write("##### Riffed Clip")
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empty_bin = right.empty()
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with empty_bin.container():
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st.info("Riffing...")
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progress = st.progress(0.0)
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progress_callback = progress.progress
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if interpolate:
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assert use_magic_mix is False, "Cannot use magic mix and interpolate together"
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inputs = InferenceInput(
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alpha=float(alphas[i]),
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num_inference_steps=num_inference_steps,
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seed_image_id="og_beat",
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start=prompt_input_a,
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end=prompt_input_b,
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)
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image, audio_bytes = run_interpolation(
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inputs=inputs,
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init_image=init_image_resized,
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device=device,
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)
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elif use_magic_mix:
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assert not prompt_input_a.negative_prompt, "No negative prompt with magic mix"
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image = streamlit_util.run_img2img_magic_mix(
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prompt=prompt_input_a.prompt,
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init_image=init_image_resized,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance,
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seed=prompt_input_a.seed,
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kmin=magic_mix_kmin,
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kmax=magic_mix_kmax,
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mix_factor=magic_mix_mix_factor,
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device=device,
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scheduler=scheduler,
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)
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else:
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image = streamlit_util.run_img2img(
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prompt=prompt_input_a.prompt,
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init_image=init_image_resized,
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denoising_strength=prompt_input_a.denoising,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance,
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negative_prompt=prompt_input_a.negative_prompt,
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seed=prompt_input_a.seed,
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progress_callback=progress_callback,
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device=device,
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scheduler=scheduler,
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lora_path=lora_path,
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lora_scale=lora_scale,
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)
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# Resize back to original size
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image = image.resize(init_image.size, Image.BICUBIC)
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result_images.append(image)
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if show_clip_details:
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empty_bin.empty()
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right.image(image, use_column_width=False)
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riffed_segment = streamlit_util.audio_segment_from_spectrogram_image(
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image=image,
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params=params,
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device=device,
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)
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result_segments.append(riffed_segment)
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audio_bytes = io.BytesIO()
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riffed_segment.export(audio_bytes, format="wav")
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if show_clip_details:
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right.audio(audio_bytes)
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if show_clip_details and show_difference:
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diff_np = np.maximum(
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0, np.asarray(init_image).astype(np.float32) - np.asarray(image).astype(np.float32)
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)
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diff_image = Image.fromarray(255 - diff_np.astype(np.uint8))
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diff_segment = streamlit_util.audio_segment_from_spectrogram_image(
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image=diff_image,
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params=params,
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device=device,
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)
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audio_bytes = io.BytesIO()
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diff_segment.export(audio_bytes, format=extension)
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st.audio(audio_bytes)
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# Combine clips with a crossfade based on overlap
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combined_segment = audio_util.stitch_segments(result_segments, crossfade_s=overlap_duration_s)
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st.write(f"#### Final Audio ({combined_segment.duration_seconds}s)")
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input_name = Path(audio_file.name).stem
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output_name = f"{input_name}_{prompt_input_a.prompt.replace(' ', '_')}"
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streamlit_util.display_and_download_audio(combined_segment, output_name, extension=extension)
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def get_clip_params(advanced: bool = False) -> T.Dict[str, T.Any]:
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"""
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Render the parameters of slicing audio into clips.
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"""
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p: T.Dict[str, T.Any] = {}
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cols = st.columns(4)
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p["start_time_s"] = cols[0].number_input(
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"Start Time [s]",
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min_value=0.0,
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value=0.0,
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)
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p["duration_s"] = cols[1].number_input(
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"Duration [s]",
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min_value=0.0,
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value=15.0,
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)
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if advanced:
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p["clip_duration_s"] = cols[2].number_input(
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"Clip Duration [s]",
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min_value=3.0,
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max_value=10.0,
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value=5.0,
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)
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else:
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p["clip_duration_s"] = 5.0
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if advanced:
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p["overlap_duration_s"] = cols[3].number_input(
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"Overlap Duration [s]",
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min_value=0.0,
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max_value=10.0,
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value=0.2,
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)
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else:
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p["overlap_duration_s"] = 0.2
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return p
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def write_clip_details(
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clip_start_times: np.ndarray, clip_duration_s: float, overlap_duration_s: float
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):
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"""
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Write details of the clips to be sliced from an audio segment.
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"""
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clip_details_text = (
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f"Slicing {len(clip_start_times)} clips of duration {clip_duration_s}s "
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f"with overlap {overlap_duration_s}s"
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)
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with st.expander(clip_details_text):
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st.dataframe(
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{
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"Start Time [s]": clip_start_times,
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"End Time [s]": clip_start_times + clip_duration_s,
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"Duration [s]": clip_duration_s,
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}
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)
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def slice_audio_into_clips(
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segment: pydub.AudioSegment, clip_start_times: T.Sequence[float], clip_duration_s: float
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) -> T.List[pydub.AudioSegment]:
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"""
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Slice an audio segment into a list of clips of a given duration at the given start times.
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"""
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clip_segments: T.List[pydub.AudioSegment] = []
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for i, clip_start_time_s in enumerate(clip_start_times):
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clip_start_time_ms = int(clip_start_time_s * 1000)
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clip_duration_ms = int(clip_duration_s * 1000)
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clip_segment = segment[clip_start_time_ms : clip_start_time_ms + clip_duration_ms]
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# TODO(hayk): I don't think this is working properly
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if i == len(clip_start_times) - 1:
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silence_ms = clip_duration_ms - int(clip_segment.duration_seconds * 1000)
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if silence_ms > 0:
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clip_segment = clip_segment.append(pydub.AudioSegment.silent(duration=silence_ms))
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clip_segments.append(clip_segment)
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return clip_segments
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def scale_image_to_32_stride(image: Image.Image) -> Image.Image:
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"""
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Scale an image to a size that is a multiple of 32.
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"""
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closest_width = int(np.ceil(image.width / 32) * 32)
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closest_height = int(np.ceil(image.height / 32) * 32)
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return image.resize((closest_width, closest_height), Image.BICUBIC)
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if __name__ == "__main__":
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render_audio_to_audio()
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