gistfile1.txt

import os
import math
import numpy as np
import torch
import torchaudio
from torch.utils.data import DataLoader, Dataset
from scipy.io.wavfile import write as write_wav
from concurrent.futures import ThreadPoolExecutor
import pennylane as qml
from bark.generation import load_codec_model, generate_text_semantic
from encodec.utils import convert_audio
from hubert.hubert_manager import HuBERTManager
from hubert.pre_kmeans_hubert import CustomHubert
from hubert.customtokenizer import CustomTokenizer
from bark.api import generate_audio
from bark.generation import SAMPLE_RATE, preload_models, codec_decode, generate_coarse, generate_fine

def init_models(device_id):
    torch.cuda.set_device(device_id)
    model = load_codec_model(use_gpu=True)
    hubert_manager = HuBERTManager()
    hubert_manager.make_sure_hubert_installed()
    hubert_manager.make_sure_tokenizer_installed()
    hubert_model = CustomHubert(checkpoint_path='data/models/hubert/hubert.pt').to(f'cuda:{device_id}')
    tokenizer = CustomTokenizer.load_from_checkpoint('data/models/hubert/tokenizer.pth').to(f'cuda:{device_id}')
    return model, hubert_model, tokenizer

class AudioDataset(Dataset):
    def __init__(self, audio_path, chunk_size, overlap, sample_rate):
        self.wav, self.sr = torchaudio.load(audio_path, normalize=True)
        self.resampler = torchaudio.transforms.Resample(orig_freq=self.sr, new_freq=sample_rate)
        self.chunk_size = chunk_size
        self.overlap = overlap
        self.sample_rate = sample_rate
        self.num_chunks = math.ceil((self.wav.size(1) / self.sr * self.sample_rate - self.overlap) / (self.chunk_size - self.overlap))

    def __len__(self):
        return self.num_chunks

    def __getitem__(self, idx):
        start = int(idx * (self.chunk_size - self.overlap))
        end = int(min(start + self.chunk_size, self.wav.size(1) / self.sr * self.sample_rate))
        chunk = self.wav[:, start:end]
        return self.resampler(chunk)

def quantum_processor(chunk):
    num_qubits = 4
    dev = qml.device("default.qubit", wires=num_qubits)

    @qml.qnode(dev)
    def circuit(inputs):
        for i in range(num_qubits):
            qml.RY(np.pi / 4, wires=i)
        for i in range(num_qubits):
            if i < num_qubits - 1:
                qml.CNOT(wires=[i, i + 1])
        qml.CNOT(wires=[num_qubits - 1, 0])
        for i in range(num_qubits):
            qml.RX(inputs[i % len(inputs)], wires=i)
        return [qml.expval(qml.PauliZ(i)) for i in range(num_qubits)]

    processed_chunk = circuit(chunk[:4])
    return np.array(processed_chunk)

def quantum_meter(processed_chunk):
    num_qubits = len(processed_chunk)
    dev = qml.device("default.qubit", wires=num_qubits)

    @qml.qnode(dev)
    def meter_circuit(inputs):
        # Prepare the qubit state based on the processed chunk
        for i, val in enumerate(inputs):
            qml.RY(val, wires=i)

        # Entangle qubits
        for i in range(num_qubits - 1):
            qml.CNOT(wires=[i, i + 1])
        qml.CNOT(wires=[num_qubits - 1, 0])

        # Measurement
        return [qml.expval(qml.PauliZ(i)) for i in range(num_qubits)]

    # Run the circuit and return the result
    meter_result = meter_circuit(processed_chunk)
    return np.array(meter_result)
def process_chunk(chunk, device_id, model, hubert_model, tokenizer):
    torch.cuda.set_device(device_id)
    wav = convert_audio(chunk, model.sample_rate, model.channels)
    semantic_vectors = hubert_model.forward(wav, input_sample_hz=model.sample_rate)
    semantic_tokens = tokenizer.get_token(semantic_vectors)
    with torch.no_grad():
        encoded_frames = model.encode(wav.unsqueeze(0))
    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()
    quantum_results = quantum_processor(codes.cpu().numpy())
    quantum_meter_result = quantum_meter(quantum_results)
    return codes.cpu().numpy(), semantic_tokens.cpu().numpy(), quantum_meter_result

def process_audio_chunks_async(audio_path, chunk_size, overlap, sample_rate, num_workers=2):
    dataset = AudioDataset(audio_path, chunk_size, overlap, sample_rate)
    loader = DataLoader(dataset, batch_size=1, shuffle=False)

    model_0, hubert_model_0, tokenizer_0 = init_models(0)
    model_1, hubert_model_1, tokenizer_1 = init_models(1)

    futures = []
    with ThreadPoolExecutor(max_workers=num_workers) as executor:
        for i, chunk in enumerate(loader):
            device_id = i % 2
            model = model_0 if device_id == 0 else model_1
            hubert_model = hubert_model_0 if device_id == 0 else hubert_model_1
            tokenizer = tokenizer_0 if device_id == 0 else tokenizer_1
            futures.append(executor.submit(process_chunk, chunk, device_id, model, hubert_model, tokenizer))

    results = [future.result() for future in futures]
    return results

audio_path = 'path/to/your/audio.wav'
chunk_size = 16000 * 30
overlap = 16000 * 5
sample_rate = 16000

processed_data = process_audio_chunks_async(audio_path, chunk_size, overlap, sample_rate)