276 lines
8.5 KiB
Python
276 lines
8.5 KiB
Python
import io
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import typing as T
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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.spectrogram_params import SpectrogramParams
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from riffusion.streamlit import util as streamlit_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.
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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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"""
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)
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device = streamlit_util.select_device(st.sidebar)
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audio_file = st.file_uploader(
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"Upload audio",
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type=["mp3", "m4a", "ogg", "wav", "flac", "webm"],
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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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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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if "counter" not in st.session_state:
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st.session_state.counter = 0
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def increment_counter():
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st.session_state.counter += 1
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cols = st.columns(4)
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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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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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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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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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duration_s = min(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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st.write(
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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 Times"):
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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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with st.form("Conversion Params"):
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prompt = st.text_input("Text Prompt")
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negative_prompt = st.text_input("Negative Prompt")
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cols = st.columns(4)
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denoising_strength = cols[0].number_input(
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"Denoising Strength",
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min_value=0.0,
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max_value=1.0,
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value=0.45,
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)
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guidance_scale = cols[1].number_input(
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"Guidance Scale",
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min_value=0.0,
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max_value=20.0,
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value=7.0,
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)
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num_inference_steps = int(
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cols[2].number_input(
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"Num Inference Steps",
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min_value=1,
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max_value=150,
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value=50,
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)
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)
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seed = int(
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cols[3].number_input(
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"Seed",
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min_value=0,
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value=42,
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)
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)
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submit_button = st.form_submit_button("Convert", on_click=increment_counter)
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# TODO fix
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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: 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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if not prompt:
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st.info("Enter a prompt")
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return
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if not submit_button:
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return
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params = SpectrogramParams()
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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]}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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# TODO(hayk): Scale something when computing audio
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closest_width = int(np.ceil(init_image.width / 32) * 32)
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closest_height = int(np.ceil(init_image.height / 32) * 32)
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init_image_resized = init_image.resize((closest_width, closest_height), Image.BICUBIC)
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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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image = streamlit_util.run_img2img(
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prompt=prompt,
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init_image=init_image_resized,
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denoising_strength=denoising_strength,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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seed=seed,
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progress_callback=progress_callback,
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device=device,
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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="wav")
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st.audio(audio_bytes)
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# Combine clips with a crossfade based on overlap
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crossfade_ms = int(overlap_duration_s * 1000)
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combined_segment = result_segments[0]
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for segment in result_segments[1:]:
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combined_segment = combined_segment.append(segment, crossfade=crossfade_ms)
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audio_bytes = io.BytesIO()
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combined_segment.export(audio_bytes, format="mp3")
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st.write(f"#### Final Audio ({combined_segment.duration_seconds}s)")
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st.audio(audio_bytes, format="audio/mp3")
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@st.cache
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def test(segment: pydub.AudioSegment, counter: int) -> int:
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st.write("#### Trimmed")
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st.write(segment.duration_seconds)
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return counter
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if __name__ == "__main__":
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render_audio_to_audio()
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