gistfile1.py

import numpy as np
import torch
import torchaudio
import math
from transformers import CLIPProcessor, CLIPModel
import pennylane as qml
from sklearn.svm import SVC
import joblib
from sklearn.preprocessing import StandardScaler
from torch.utils.data import DataLoader, Dataset
from concurrent.futures import ThreadPoolExecutor
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

clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

svm_model = joblib.load('path/to/pretrained_svm_model.pkl')
svm_scaler = joblib.load('path/to/pretrained_svm_scaler.pkl')

def quantum_neural_network(num_qubits=4, num_layers=3):
    dev = qml.device("default.qubit", wires=num_qubits)
    @qml.qnode(dev)
    def qnn_circuit(inputs, weights):
        qml.templates.AngleEmbedding(inputs, wires=range(num_qubits))
        qml.templates.StronglyEntanglingLayers(weights, wires=range(num_qubits))
        return [qml.expval(qml.PauliZ(i)) for i in range(num_qubits)]
    weight_shapes = {"weights": (num_layers, num_qubits, 3)}
    qlayer = qml.qnn.TorchLayer(qnn_circuit, weight_shapes)
    return qlayer

qnn = quantum_neural_network()

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('cuda')
tokenizer = CustomTokenizer.load_from_checkpoint('data/models/hubert/tokenizer.pth').to('cuda')

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 - the overlap) / (chunk_size - overlap))

    def __len__(self):
        return self.num_chunks

    def __getitem__(self, idx):
        start = int(idx * (self.chunk_size - the 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 is_silent(audio_chunk, silence_threshold=1e-4, min_silence_duration=0.5):
    silent_duration = 0.0
    for sample in audio_chunk:
        if torch.abs(sample).mean() < silence_threshold:
            silent_duration += 1.0 / audio_chunk.sample_rate
            if silent_duration >= min_silence_duration:
                return True
        else:
            silent_duration = 0
    return False

def generate_dynamic_prompts(spectrogram):
    categories = ["music", "speech", "environmental sound", "silence"]
    prompts = [f"This is a {category} spectrogram" for category in categories]
    inputs = clip_processor(text=prompts, images=spectrogram.unsqueeze(0), return_tensors="pt", padding=True)
    outputs = clip_model(**inputs)
    similarities = outputs.logits_per_text.cpu().detach().numpy()
    best_prompt_index = np.argmax(similarities, axis=1)
    return prompts[best_prompt_index[0]]

def process_chunk(chunk, qnn, clip_model, clip_processor, svm_model, svm_scaler, model, hubert_model, tokenizer):
    if is_silent(chunk):
        return 'Silence'
    spectrogram = torchaudio.transforms.Spectrogram()(chunk)
    dynamic_prompt = generate_dynamic_prompts(spectrogram)
    # Additional processing steps can be added here
    return dynamic_prompt

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, qnn, clip_model, clip_processor, svm_model, svm_scaler, 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)