app.py

import gradio as gr
import torch
import torchaudio
from semanticodec import SemantiCodec
import tempfile
import numpy as np
import lz4.frame
import os
from typing import Generator
import spaces
import asyncio  # Import asyncio for cancellation

# Attempt to use GPU, fallback to CPU
try:
    torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"Using device: {torch_device}")
except Exception as e:
    print(f"Error detecting GPU. Using CPU. Error: {e}")
    torch_device = torch.device("cpu")

# Load the SemantiCodec model
semanticodec = SemantiCodec(token_rate=100, semantic_vocab_size=32768).to(torch_device)

# Global variable for cancellation
cancel_encode = False
cancel_decode = False
cancel_stream = False

@spaces.GPU(duration=500)  # Increased GPU duration to 500 seconds
def encode_audio(audio_file_path):
    global cancel_encode
    try:
        # Load the audio file
        waveform, sample_rate = torchaudio.load(audio_file_path)

        # Encode the audio
        audio = waveform.unsqueeze(0).to(torch_device)
        with torch.no_grad():
            tokens = semanticodec.encode(audio)

        # Convert to NumPy and save to a temporary .owie file
        tokens_numpy = tokens.detach().cpu().numpy()
        temp_fd, temp_file_path = tempfile.mkstemp(suffix=".owie")
        os.close(temp_fd)
        with open(temp_file_path, 'wb') as temp_file:
            # Store the sample rate as the first 4 bytes
            temp_file.write(sample_rate.to_bytes(4, byteorder='little'))
            # Compress and write the encoded data
            compressed_data = lz4.frame.compress(tokens_numpy.tobytes())
            temp_file.write(compressed_data)

        return temp_file_path

    except Exception as e:
        return f"Encoding error: {e}"

    finally:
        cancel_encode = False  # Reset cancel flag after encoding

@spaces.GPU(duration=500)  # Increased GPU duration to 500 seconds
def decode_audio(encoded_file_path):
    global cancel_decode
    try:
        # Load encoded data and sample rate from the .owie file
        with open(encoded_file_path, 'rb') as temp_file:
            sample_rate = int.from_bytes(temp_file.read(4), byteorder='little')
            compressed_data = temp_file.read()
            tokens_numpy_bytes = lz4.frame.decompress(compressed_data)
            tokens_numpy = np.frombuffer(tokens_numpy_bytes, dtype=np.int64)
            tokens = torch.from_numpy(tokens_numpy).to(torch_device)

        # Decode the audio
        with torch.no_grad():
            waveform = semanticodec.decode(tokens)

        # Save to a temporary WAV file
        temp_wav_path = tempfile.mktemp(suffix=".wav")
        torchaudio.save(temp_wav_path, waveform.squeeze(0).cpu(), sample_rate)
        return temp_wav_path

    except Exception as e:
        return f"Decoding error: {e}"

    finally:
        cancel_decode = False  # Reset cancel flag after decoding

@spaces.GPU(duration=500)  # Increased GPU duration to 500 seconds
async def stream_decode_audio(encoded_file_path) -> Generator[tuple, None, None]:
    global cancel_stream
    try:
        # Load encoded data and sample rate from the .owie file
        with open(encoded_file_path, 'rb') as temp_file:
            sample_rate = int.from_bytes(temp_file.read(4), byteorder='little')
            compressed_data = temp_file.read()
            tokens_numpy_bytes = lz4.frame.decompress(compressed_data)
            tokens_numpy = np.frombuffer(tokens_numpy_bytes, dtype=np.int64)
            tokens = torch.from_numpy(tokens_numpy).to(torch_device)

        # Decode the audio in chunks
        chunk_size = sample_rate  # Use the stored sample rate as chunk size
        with torch.no_grad():
            for i in range(0, tokens.shape[1], chunk_size):
                if cancel_stream:
                    break  # Exit the loop if cancellation is requested

                tokens_chunk = tokens[:, i:i+chunk_size]
                audio_chunk = semanticodec.decode(tokens_chunk)
                # Convert to numpy array and transpose
                audio_data = audio_chunk.squeeze(0).cpu().numpy().T
                yield (sample_rate, audio_data)
                await asyncio.sleep(0)  # Allow for cancellation check

    except Exception as e:
        print(f"Streaming decoding error: {e}")
        yield (sample_rate, np.zeros((chunk_size, 1), dtype=np.float32))  # Return silence

    finally:
        cancel_stream = False  # Reset cancel flag after streaming

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("## Audio Compression with SemantiCodec (GPU/CPU)")

    with gr.Tab("Encode"):
        input_audio = gr.Audio(label="Input Audio", type="filepath")
        encode_button = gr.Button("Encode")
        cancel_encode_button = gr.Button("Cancel")
        encoded_output = gr.File(label="Encoded File (.owie)", type="filepath")

        encode_button.click(encode_audio, inputs=input_audio, outputs=encoded_output)
        cancel_encode_button.click(lambda: globals().update(cancel_encode=True),
                                     outputs=None)  # Set cancel_encode flag

    with gr.Tab("Decode"):
        input_encoded = gr.File(label="Encoded File (.owie)", type="filepath")
        decode_button = gr.Button("Decode")
        cancel_decode_button = gr.Button("Cancel")
        decoded_output = gr.Audio(label="Decoded Audio", type="filepath")

        decode_button.click(decode_audio, inputs=input_encoded, outputs=decoded_output)
        cancel_decode_button.click(lambda: globals().update(cancel_decode=True),
                                     outputs=None)  # Set cancel_decode flag

    with gr.Tab("Streaming"):
        input_encoded_stream = gr.File(label="Encoded File (.owie)", type="filepath")
        stream_button = gr.Button("Start Streaming")
        cancel_stream_button = gr.Button("Cancel")
        audio_output = gr.Audio(label="Streaming Audio Output", streaming=True)

        stream_button.click(stream_decode_audio, inputs=input_encoded_stream, outputs=audio_output)
        cancel_stream_button.click(lambda: globals().update(cancel_stream=True),
                                     outputs=None)  # Set cancel_stream flag

demo.queue().launch()