File size: 3,027 Bytes
f18f98b d8d7a8d 60f3b28 a7dbbfe d8d7a8d a2cc897 bd40662 a7dbbfe dd53483 60f3b28 5956a6f c27eb74 5f18cf7 a7dbbfe 60f3b28 5f18cf7 dfdd7ad a7dbbfe c27eb74 a7dbbfe c9a89ac c27eb74 60f3b28 6b093ee c27eb74 6b093ee a7dbbfe c27eb74 a7dbbfe 5f18cf7 c27eb74 d07be48 60f3b28 44bab11 f18f98b c27eb74 a7dbbfe 44bab11 c6e9cf1 44bab11 f18f98b c27eb74 a7dbbfe c27eb74 44bab11 d8d7a8d f18f98b c9a89ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
import gradio as gr
import jax
import jax.numpy as jnp
import librosa
import dac_jax
from dac_jax.audio_utils import volume_norm, db2linear
import spaces
import tempfile
import os
import numpy as np
# Download a model and bind variables to it.
model, variables = dac_jax.load_model(model_type="44khz")
model = model.bind(variables)
@spaces.GPU
def encode(audio_file_path):
try:
# Load audio with librosa, specifying duration
signal, sample_rate = librosa.load(audio_file_path, sr=44100, mono=True, duration=5.0) # Set duration as needed
signal = jnp.array(signal, dtype=jnp.float32)
while signal.ndim < 3:
signal = jnp.expand_dims(signal, axis=0)
target_db = -16 # Normalize audio to -16 dB
x, input_db = volume_norm(signal, target_db, sample_rate)
# Encode audio signal
x = model.preprocess(x, sample_rate)
z, codes, latents, commitment_loss, codebook_loss = model.encode(x, train=False)
# Save encoded data to a temporary file (using numpy.savez for now)
with tempfile.NamedTemporaryFile(delete=False, suffix=".npz") as temp_file:
np.savez(temp_file.name, z=z, codes=codes, latents=latents, input_db=input_db)
return temp_file.name
except Exception as e:
gr.Warning(f"An error occurred during encoding: {e}")
return None
@spaces.GPU
def decode(compressed_file_path): # Changed input to compressed_file_path
try:
# Load encoded data directly from the file path
data = np.load(compressed_file_path) # No need for temporary files
z = data['z']
codes = data['codes']
latents = data['latents']
input_db = data['input_db']
# Decode audio signal
y = model.decode(z, length=z.shape[1] * model.hop_length)
# Undo previous loudness normalization
y = y * db2linear(input_db - (-16)) # Using -16 as the target_db
decoded_audio = np.array(y).squeeze()
return (44100, decoded_audio)
except Exception as e:
gr.Warning(f"An error occurred during decoding: {e}")
return None
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("<h1 style='text-align: center;'>Audio Compression with DAC-JAX</h1>")
with gr.Tab("Encode"):
with gr.Row():
audio_input = gr.Audio(type="filepath", label="Input Audio")
encode_button = gr.Button("Encode", variant="primary")
with gr.Row():
encoded_output = gr.File(label="Compressed Audio (.npz)")
encode_button.click(encode, inputs=audio_input, outputs=encoded_output)
with gr.Tab("Decode"):
with gr.Row():
compressed_input = gr.File(label="Compressed Audio (.npz)")
decode_button = gr.Button("Decode", variant="primary")
with gr.Row():
decoded_output = gr.Audio(label="Decompressed Audio")
decode_button.click(decode, inputs=compressed_input, outputs=decoded_output)
demo.queue().launch() |