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import gradio as gr
import spaces
import torch
import dac
import io
from audiotools import AudioSignal
from pydub import AudioSegment
# Ensure we're using CPU even if GPU is available
torch.set_default_tensor_type(torch.FloatTensor)
class DACApi:
def __init__(self, model_type="44khz", model_bitrate="16kbps"):
self.model_type = model_type
self.model_bitrate = model_bitrate
self.model_path = dac.utils.download(model_type, model_bitrate)
print("Loading DAC model...")
self.model = dac.DAC.load(self.model_path)
self.model.to('cpu')
def encode_audio(self, audio):
# Convert audio to WAV
audio = AudioSegment.from_file(audio.name)
wav_io = io.BytesIO()
audio.export(wav_io, format="wav")
wav_io.seek(0)
# Load audio signal
signal = AudioSignal(wav_io)
# Compress audio
print("Compressing audio...")
compressed = self.model.compress(signal)
# Save compressed audio to BytesIO
output = io.BytesIO()
compressed.save(output)
output.seek(0)
return output
def decode_audio(self, compressed_file):
# Load compressed audio
compressed = dac.DACFile.load(compressed_file)
# Decompress audio
print("Decompressing audio...")
decompressed = self.model.decompress(compressed)
# Save decompressed audio to BytesIO
output = io.BytesIO()
decompressed.write(output, format='wav')
output.seek(0)
return output
dac_api = DACApi()
@spaces.CPU
def encode(audio):
compressed = dac_api.encode_audio(audio)
return compressed
@spaces.CPU
def decode(compressed_file):
decompressed = dac_api.decode_audio(compressed_file)
return decompressed
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Audio Compression with DAC")
with gr.Tab("Encode"):
audio_input = gr.Audio(type="filepath", label="Input Audio")
encode_button = gr.Button("Encode")
encoded_output = gr.File(label="Compressed Audio")
encode_button.click(encode, inputs=audio_input, outputs=encoded_output)
with gr.Tab("Decode"):
compressed_input = gr.File(label="Compressed Audio")
decode_button = gr.Button("Decode")
decoded_output = gr.Audio(label="Decompressed Audio")
decode_button.click(decode, inputs=compressed_input, outputs=decoded_output)
demo.launch()
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