Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,788 Bytes
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import os
from typing import List
import gradio as gr
import tempfile
import numpy as np
import librosa
import soundfile as sf
import spaces
from audiosr import super_resolution
from audiosr_utils import load_audiosr
audiosr_model = load_audiosr()
def split_audio_to_chunks(y, sr=48000, chunk_duration=5.12) -> List[str]:
# Calculate the number of samples per chunk
chunk_samples = int(chunk_duration * sr)
# Split the audio into chunks
chunks = [y[i : i + chunk_samples] for i in range(0, len(y), chunk_samples)]
# Save each chunk to a temporary file
temp_files = []
for i, chunk in enumerate(chunks):
# Create a temporary file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
temp_files.append(temp_file.name)
# Write the chunk to the temporary file
sf.write(temp_file.name, chunk, sr)
return temp_files
@spaces.GPU(duration=180)
def run_audiosr(
chunks: List[str], guidance_scale: float, ddim_steps: int
) -> np.ndarray:
waveforms = []
for i, chunk in enumerate(chunks):
print(f"Processing chunk {i+1}/{len(chunks)}")
waveform = super_resolution(
audiosr_model,
chunk,
guidance_scale=guidance_scale,
ddim_steps=ddim_steps,
)
waveforms.append(waveform)
waveform = np.concatenate(waveforms, axis=-1) # (1, 1, N)
waveform = waveform.squeeze()
return waveform
def audiosr_infer(audio: str) -> str:
guidance_scale = 3.5
ddim_steps = 100
y, sr = librosa.load(audio, sr=48000)
if len(y) > 60 * sr:
y = y[: 60 * sr]
gr.Info("Audio is too long, only the first 60 seconds will be processed")
chunk_files = split_audio_to_chunks(y, sr=sr, chunk_duration=5.12)
print(f"Splited audio chunks: {chunk_files}")
waveform = run_audiosr(chunk_files, guidance_scale, ddim_steps)
sr = 44100
for chunk_file in chunk_files:
os.remove(chunk_file)
with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as f:
sf.write(f.name, waveform, sr)
return f.name
models = {
"AudioSR": audiosr_infer,
}
def infer(audio: str, model: str, sr: int) -> str:
if sr > 0:
# resample audio
y, _ = librosa.load(audio, sr=sr)
# save resampled audio
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
sf.write(f.name, y, sr)
return models[model](f.name)
else:
return models[model](audio)
with gr.Blocks() as app:
with open(os.path.join(os.path.dirname(__file__), "README.md"), "r") as f:
README = f.read()
# remove yaml front matter
blocks = README.split("---")
if len(blocks) > 1:
README = "---".join(blocks[2:])
gr.Markdown(README)
with gr.Row():
with gr.Column():
gr.Markdown("## Upload an audio file")
audio = gr.Audio(label="Upload an audio file", type="filepath")
sr = gr.Slider(
value=0,
label="Resample audio to sample rate before inference, 0 means no resampling",
minimum=0,
maximum=48000,
step=1000,
)
with gr.Row():
model = gr.Radio(
label="Select a model",
choices=[s for s in models.keys()],
value="AudioSR",
)
btn = gr.Button("Infer")
with gr.Row():
with gr.Column():
out = gr.Audio(
label="Output", format="mp3", type="filepath", interactive=False
)
btn.click(
fn=infer,
inputs=[audio, model, sr],
outputs=[out],
api_name="infer",
)
app.launch(show_error=True)
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