76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
import typing as T
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import streamlit as st
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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_text_to_audio() -> None:
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st.set_page_config(layout="wide", page_icon="🎸")
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st.subheader(":pencil2: Text to Audio")
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st.write(
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"""
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Generate audio from text prompts.
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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 runs riffusion in the simplest text to image form to generate an audio
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clip from a text prompt. There is no seed image or interpolation here. This mode
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allows more diversity and creativity than when using a seed image, but it also
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leads to having less control. Play with the seed to get infinite variations.
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"""
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)
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device = streamlit_util.select_device(st.sidebar)
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prompt = st.text_input("Prompt")
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negative_prompt = st.text_input("Negative prompt")
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with st.sidebar.expander("Text to Audio Params", expanded=True):
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seed = T.cast(int, st.number_input("Seed", value=42))
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num_inference_steps = T.cast(int, st.number_input("Inference steps", value=50))
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width = T.cast(int, st.number_input("Width", value=512))
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guidance = st.number_input(
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"Guidance", value=7.0, help="How much the model listens to the text prompt"
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)
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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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image = streamlit_util.run_txt2img(
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prompt=prompt,
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num_inference_steps=num_inference_steps,
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guidance=guidance,
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negative_prompt=negative_prompt,
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seed=seed,
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width=width,
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height=512,
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device=device,
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)
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st.image(image)
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# TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained
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params = SpectrogramParams(
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min_frequency=0,
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max_frequency=10000,
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)
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audio_bytes = streamlit_util.audio_bytes_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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output_format="mp3",
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)
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st.audio(audio_bytes)
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if __name__ == "__main__":
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render_text_to_audio()
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