import torch
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
import torchaudio
import torchaudio.transforms as transforms
import librosa
import noisereduce as nr
import numpy as np
import crepe
import madmom
from music21 import stream, note, converter, instrument
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer, HubertForCTC, HubertTokenizer, pipeline
from scipy import signal
import openunmix
from demucs import pretrained, Demucs
import openai
import yamnet as yamnet_model
import openl3
from onsets_and_frames import NonBinaryClassification, inference
import pydub
import parselmouth
from spleeter.separator import Separator
import soundfile as sf
import time
import tonic

# Установка ключа API OpenAI
openai.api_key = “your_openai_api_key_here”

# Дополнительные модели ИИ
from transformers import TFAutoModel, AutoTokenizer

def preprocess_audio(file_path):
    “”“
    Загрузка, нормализация и фильтрация аудиосигнала
    “””
    y, sr = librosa.load(file_path, sr=None)
    y = librosa.util.normalize(y)
    y = nr.reduce_noise(y, sr)
    y = signal.wiener(y)
    return y, sr

def convert_audio_format(file_path, target_format=“wav”):
    audio = pydub.AudioSegment.from_file(file_path)
    new_file_path = file_path.replace(file_path.split(‘.’)[-1], target_format)
    audio.export(new_file_path, format=target_format)
    return new_file_path

def play_audio(file_path):
    data, sr = sf.read(file_path)
    import sounddevice as sd
    sd.play(data, sr)
    sd.wait()

def extract_features(y, sr):
    “”“
    Извлечение признаков из аудиосигнала с использованием нескольких методов
    “””
    cqt = librosa.cqt(y, sr=sr)
    cqt_db = librosa.amplitude_to_db(np.abs(cqt), ref=np.max)
    
    # Pitch detection using CREPE
    _, frequency, confidence, _ = crepe.predict(y, sr)
    
    return cqt_db, frequency, confidence

def advanced_pitch_analysis(y, sr):
    audio = tonic.Audio(y, sr)
    pitch_estimation = audio.estimate_pitches()
    return pitch_estimation

def additional_features_essentia(y, sr):
    from essentia.standard import MusicExtractor, MonoLoader
    loader = MonoLoader(sampleRate=sr)
    y = loader(y)
    extractor = MusicExtractor(lowlevelStats=[‘mean’, ‘stdev’])
    features, _ = extractor(y)
    return features

def plot_spectrogram(y, sr, output_image_path):
    import matplotlib.pyplot as plt
    import librosa.display
    
    D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)
    plt.figure(figsize=(12, 8))
    librosa.display.specshow(D, sr=sr, x_axis=‘time’, y_axis=‘log’)
    plt.colorbar(format=‘%+2.0f dB’)
    plt.title(‘Spectrogram’)
    plt.savefig(output_image_path)
    print(f’Spectrogram saved to {output_image_path}‘)
    plt.close()

def plot_chromagram(y, sr, output_image_path):
    import matplotlib.pyplot as plt
    import librosa.display
    
    chromagram = librosa.feature.chroma_stft(y=y, sr=sr)
    plt.figure(figsize=(12, 8))
    librosa.display.specshow(chromagram, sr=sr, x_axis=‘time’, y_axis=‘chroma’)
    plt.colorbar(format=’%+2.0f’)
    plt.title(‘Chromagram’)
    plt.savefig(output_image_path)
    print(f’Chromagram saved to {output_image_path}‘)
    plt.close()

def transformer_features_wav2vec2(y, sr):
    tokenizer = Wav2Vec2Tokenizer.from_pretrained(“facebook/wav2vec2-base-960h”)
    model = Wav2Vec2ForCTC.from_pretrained(“facebook/wav2vec2-base-960h”)
    
    input_values = tokenizer(y, return_tensors=“pt”, padding=True).input_values
    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    
    transcription = tokenizer.batch_decode(predicted_ids)[0]
    return transcription

def transformer_features_hubert(y, sr):
    tokenizer = HubertTokenizer.from_pretrained(“facebook/hubert-large-ls960-ft”)
    model = HubertForCTC.from_pretrained(“facebook/hubert-large-ls960-ft”)
    
    input_values = tokenizer(y, return_tensors=“pt”, padding=True).input_values
    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    
    transcription = tokenizer.batch_decode(predicted_ids)[0]
    return transcription

class AudioCNN(nn.Module):
    def init(self, in_channels, out_channels):
        super(AudioCNN, self).init()
        self.conv1 = nn.Conv2d(in_channels, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(32 * 16 * 16, 128)
        self.fc2 = nn.Linear(128, out_channels)
        
    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = self.pool(x)
        x = torch.relu(self.conv2(x))
        x = self.pool(x)
        x = x.view(-1, 32 * 16 * 16)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

def source_separation_openunmix(y, sr):
    separator = openunmix.separate(
                audio_signal=y.tolist(),
                sample_rate=sr,
                targets=[“vocals”, “drums”, “bass”, “other”]
            )
    return {
        “vocals”: separator[“vocals”],
        “drums”: separator[“drums”],
        “bass”: separator[“bass”],
        “other”: separator[“other”],
    }

def source_separation_demucs_v3(y, sr):
    model = pretrained.get_model(‘demucs’)
    
    waveform = torch.tensor(y).unsqueeze(0)
    waveform = waveform.to(model.device)
    
    sources = model(waveform)
    return sources

def source_separation_spleeter(file_path):
    separator = Separator(‘spleeter:2stems’)
    separator.separate_to_file(file_path, output_path=‘output’)
    return ‘output/vocals.wav’, ‘output/accompaniment.wav’

class MusicGAN(nn.Module):
    def init(self, input_size, hidden_size, output_size):
        super(MusicGAN, self).init()
        self.generator = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(True),
            nn.Linear(hidden_size, output_size),
            nn.Tanh()
        )
        self.discriminator = nn.Sequential(
            nn.Linear(output_size, hidden_size),
            nn.ReLU(True),
            nn.Linear(hidden_size, 1),
            nn.Sigmoid()
        )
    
    def forward(self, x):
        return self.generator(x)

class WaveUNet(nn.Module):
    def init(self, in_channels, out_channels):
        super(WaveUNet, self).init()
        self.encoder = nn.ModuleList([
            nn.Sequential(
                nn.Conv1d(in_channels, 32, kernel_size=15, padding=7),
                nn.ReLU(True)
            ),
            nn.MaxPool1d(2)
        ])
        self.decoder = nn.ModuleList([
            nn.Sequential(
                nn.ConvTranspose1d(32, out_channels, kernel_size=15, padding=7),
                nn.ReLU(True)
            )
        ])
    
    def forward(self, x):
        for layer in self.encoder:
            x = layer(x)
        for layer in self.decoder:
            x = layer(x)
        return x

class MusicRLModel(nn.Module):
    def init(self, input_size, hidden_size, output_size):
        super(MusicRLModel, self).init()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

class MusicVAE(nn.Module):
    def init(self, input_size, hidden_size, latent_size):
        super(MusicVAE, self).init()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2_mean = nn.Linear(hidden_size, latent_size)
        self.fc2_logvar = nn.Linear(hidden_size, latent_size)
        self.fc3 = nn.Linear(latent_size, hidden_size)
        self.fc4 = nn.Linear(hidden_size, input_size)

    def encode(self, x):
        h1 = torch.relu(self.fc1(x))
        return self.fc2_mean(h1), self.fc2_logvar(h1)

    def decode(self, z):
        h3 = torch.relu(self.fc3(z))
        return torch.sigmoid(self.fc4(h3))

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar

def vae_loss(recon_x, x, mu, logvar):
    BCE = nn.functional.binary_cross_entropy(recon_x, x, reduction=‘sum’)
    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
    return BCE + KLD

def onsets_frames(y):
    # Пример применения onsets-and-frames
    device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)
    model = NonBinaryClassification(num_layers=4).to(device)
    
    frames, onsets, offsets, velocity = inference(y, model, device)
    
    return frames, onsets, offsets, velocity

def harmonic_rhythm_analysis(y, sr):
    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
    chords = librosa.effects.harmonic(y)
    
    # Анализ ритма с использованием madmom
    proc = madmom.features.beats.RNNBeatProcessor()(y)
    beat_times = madmom.features.beats.DBNBeatTrackingProcessor(beats_per_bar=[3, 4])(proc)
    
    return tempo, beat_frames, chords, beat_times

def generate_text_descriptions(features):
    response = openai.Completion.create(
        model=“gpt-4”,
        prompt=f"Generate a coherent description based on the following features: {features}“,
        max_tokens=100
    )
    return response.choices[0].text.strip()

def recognize_instruments(y, sr):
    params = yamnet_model.Params()
    yamnet = yamnet_model.yamnet(params)
    yamnet.load_weights(‘yamnet.h5’)

    waveform = np.reshape(y, [len(y), 1])
    
    class_scores, embeddings, spectrogram = yamnet(waveform)
    
    classes = yamnet_model.class_names(params)
    top_classes = np.argsort(class_scores, axis=-1)[0, -5:][::-1]
    recognized_classes = [classes[i] for i in top_classes]
    
    return recognized_classes

def recognize_instruments_openl3(y, sr):
    model = openl3.models.load_audio_embedding_model(input_repr=“mel256”, content_type=“music”, embedding_size=512)
    embeddings, timestamps = openl3.get_audio_embedding(y, sr, model=model, hop_size=0.1)
    
    return embeddings

def analyze_pitch_with_parselmouth(file_path):
    snd = parselmouth.Sound(file_path)
    pitch = snd.to_pitch()
    pitch_values = pitch.selected_array[‘frequency’]
    return pitch_values

def reduce_noise(file_path, noise_reduction_level=5):
    audio = pydub.AudioSegment.from_file(file_path)
    reduced_noise_audio = audio - noise_reduction_level
    reduced_noise_file_path = file_path.replace(”.wav", “_reduced_noise.wav”)
    reduced_noise_audio.export(reduced_noise_file_path, format=“wav”)
    return reduced_noise_file_path

def change_timbre_and_speed(file_path, pitch_shift=2, speed_change=1.5):
    audio = pydub.AudioSegment.from_file(file_path)
    new_timbre_audio = audio._spawn(audio.raw_data, overrides={‘frame_rate’: int(audio.frame_rate * pitch_shift)})
    new_timbre_speed_audio = new_timbre_audio.speedup(playback_speed=speed_change)
    new_file_path = file_path.replace(“.wav”, “_modified.wav”)
    new_timbre_speed_audio.export(new_file_path, format=“wav”)
    return new_file_path

def normalize_volume_level(file_path):
    audio = pydub.AudioSegment.from_file(file_path)
    normalized_audio = audio.apply_gain(-audio.dBFS)
    normalized_file_path = file_path.replace(“.wav”, “_normalized.wav”)
    normalized_audio.export(normalized_file_path, format=“wav”)
    return normalized_file_path

def denoise_audio(file_path, noise_reduction_level=0.5):
    y, sr = librosa.load(file_path, sr=None)
    reduced_noise_y = signal.wiener(y, noise_reduction_level)
    reduced_noise_file_path = file_path.replace(“.wav”, “_reduced_noise.wav”)
    sf.write(reduced_noise_file_path, reduced_noise_y, sr)
    return reduced_noise_file_path

def compute_spectral_centroid(y, sr):
    spectral_centroids = librosa.feature.spectral_centroid(y, sr=sr)[0]
    return spectral_centroids

def compute_tonality_and_stability(y, sr):
    chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
    key = librosa.estimate_tuning(y=y, sr=sr)
    return key, chroma_stft

# Построение MusicXML
def create_musicxml(frequencies, beats, frames, onsets, offsets, text_descriptions, instruments):
    score = stream.Score()
    part = stream.Part()
    
    # Чтобы добавить все инструменты, которые доступны в music21
    instrument_classes = [cls for cls in dir(instrument) if isinstance(getattr(instrument, cls), type) and issubclass(getattr(instrument, cls), instrument.Instrument)]
    
    for freq, beat, frame, onset, offset, description, inst in zip(frequencies, beats, frames, onsets, offsets, text_descriptions, instruments):
        if onset > 0:
            n = note.Note()
            n.pitch.frequency = freq
            n.quarterLength = beat
            n.offset = offset
            n.addLyric(description)
            
            # Добавление инструмента к ноте
            if inst in instrument_classes:
                instrument_class = getattr(instrument, inst)
                part.append(instrument_class())
            else:
                n.addLyric(“Unknown Instrument”)
            
            part.append(n)
    
    score.append(part)
    return score

def visualize_score(score, output_image_path):
    “”“
    Визуализация партитуры и сохранение в виде изображения
    “””
    s = converter.parse(score)
    s.show(‘text’) # Показать партитуру в текстовом виде (можно удалить, если не нужно)
    
    fp = s.write(‘musicxml.png’, fp=output_image_path)
    print(f’Партитура сохранена в {fp}’)

class MusicDataset(Dataset):
    def init(self, features, labels):
        self.features = features
        self.labels = labels
    
    def len(self):
        return len(self.features)
    
    def getitem(self, idx):
        return self.features[idx], self.labels[idx]

def train_cnn_model(train_loader, in_channels, out_channels, epochs=10):
    model = AudioCNN(in_channels, out_channels)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    for epoch in range(epochs):
        for inputs, labels in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            
        print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}“)
    
    return model

def recognize_genre(y, sr):
    features = extract_features(y, sr)
    genre_description = generate_text_descriptions(features)
    return genre_description

def modify_notes_with_gpt4(score, action=“add”, instrument=“piano”):
    notes_data = [(n.pitch.frequency, n.quarterLength, n.offset) for n in score.flat.notes]
    prompt = f"The score contains the following notes data:\n{notes_data}\n” <br/> f"Please provide necessary modifications to {‘add’ if action == ‘add’ else ‘remove’} notes for the instrument ‘{instrument}’, and ensure the notes follow the correct musical rules. Also, analyze the complexity of the composition."
    
    response = openai.Completion.create(
        model=“gpt-4”,
        prompt=prompt,
        max_tokens=150 # Увеличиваем количество токенов для получения более детального ответа
    )
    
    modified_notes = response.choices[0].text.strip().split(‘\n’)

for note_data in modified_notes:
        try:
            # Дополнительная логика обработки строки ответа
            note_info = note_data.split(‘,’)
            if len(note_info) == 3:
                freq, length, offset = map(float, note_info)
                if action == “add”:
                    new_note = note.Note()
                    new_note.pitch.frequency = freq
                    new_note.quarterLength = length
                    new_note.offset = offset
                    new_note.addLyric(instrument)
                    score.append(new_note)
                else:
                    for n in score.flat.notes:
                        if abs(n.pitch.frequency - freq) < 1e-3 and abs(n.quarterLength - length) < 1e-3 and abs(n.offset - offset) < 1e-3:
                            score.remove(n)
            else:
                print(f"Skipping invalid note data: {note_data}“)
        except ValueError as ve:
            print(f"Error processing note data: {note_data} -> {ve}”)
    
    return score

def main(file_path, output_path, output_image_path):
    # Конвертация аудио в подходящий формат
    converted_file_path = convert_audio_format(file_path)
    
    # Определить длительность MP3-файла
    audio = pydub.AudioSegment.from_file(file_path)
    duration_ms = len(audio) # Время в миллисекундах
    duration_sec = duration_ms / 1000 # Время в секундах
    
    # Предварительная обработка
    y, sr = preprocess_audio(converted_file_path)
    
    # Запуск таймера для преобразования
    start_time = time.time()
    
    # Извлечение признаков
    cqt_db, frequency, confidence = extract_features(y, sr)

    # Дополнительные признаки с использованием Essentia
    additional_features = additional_features_essentia(y, sr)

    # Признаки с использованием Transformer моделей
    transcription_wav2vec2 = transformer_features_wav2vec2(y, sr)
    transcription_hubert = transformer_features_hubert(y, sr)

    # Сепарация источников (OpenUnmix и Demucs v3)
    sources_openunmix = source_separation_openunmix(y, sr)
    sources_demucs_v3 = source_separation_demucs_v3(y, sr)
    
    # Сепарация источников с использование Spleeter
    vocals_path, accompaniment_path = source_separation_spleeter(converted_file_path)
    
    # Применение фильтрации шумов
    noise_reduced_path = reduce_noise(converted_file_path)
    
    # Изменение тембра и скорости
    modified_audio_path = change_timbre_and_speed(noise_reduced_path)
    
    # Нормализация уровня громкости
    normalized_audio_path = normalize_volume_level(modified_audio_path)
    
    # Модели глубокого обучения и автоэнкодеры (VAE)
    frames_vae, onsets_vae, offsets_vae, velocity_vae = onsets_frames(y)

    # Гармонический анализ и ритм
    tempo, beat_frames, chords, beat_times = harmonic_rhythm_analysis(y, sr)
    
    # Генерация текстовых описаний музыкальных фрагментов с использованием ChatGPT (GPT-4)
    text_descriptions = generate_text_descriptions(additional_features)
    
    # Распознавание музыкальных инструментов
    instruments = recognize_instruments(y, sr)
    embeddings_openl3 = recognize_instruments_openl3(y, sr)

    # Распознавание жанра музыки
    genre_description = recognize_genre(y, sr)
    
    # Спектральный центроид
    spectral_centroid = compute_spectral_centroid(y, sr)
    
    # Тональность и тональная стабильность
    key, chroma_stft = compute_tonality_and_stability(y, sr)
    
    # Пример Generating MusicXML из результатов VAE и анализов
    score = create_musicxml(frequency, beat_times, frames_vae, onsets_vae, offsets_vae, text_descriptions, instruments)
    
    # Логика добавления или удаления нот с использованием GPT-4 (ChatGPT)
    score = modify_notes_with_gpt4(score, action=“add”, instrument=“piano”)
    
    # Сохранение и визуализация партитуры в формате MusicXML и изображения
    score.write(‘musicxml’, fp=output_path)
    visualize_score(score, output_image_path)

    # Визуализация спектрограммы аудиофайла
    spectrogram_image_path = output_image_path.replace(“.png”, “_spectrogram.png”)
    plot_spectrogram(y, sr, spectrogram_image_path)

    # Визуализация хромограммы аудиофайла
    chromagram_image_path = output_image_path.replace(“.png”, “_chromagram.png”)
    plot_chromagram(y, sr, chromagram_image_path)
    
    # Демонстрация воспроизведения аудио
    play_audio(normalized_audio_path)
    
    # Анализ высоты тона с использованием parselmouth
    pitch_analysis = analyze_pitch_with_parselmouth(normalized_audio_path)
    print(“Pitch analysis (Parselmouth):”, pitch_analysis)
    
    # Ожидание завершения преобразования, если процесс занял меньше времени, чем длительность аудио
    elapsed_time = time.time() - start_time
    if elapsed_time < duration_sec:
        time.sleep(duration_sec - elapsed_time)
    
    # Окончательное описание всех источников
    full_description = {
        “Transcription (Wav2Vec2)”: transcription_wav2vec2,
        “Transcription (HuBERT)”: transcription_hubert,
        “Genre Description (GPT-4)”: genre_description,
        “Instrument Recognition (YAMNet)”: instruments,
        “Additional Features (Essentia)”: additional_features,
        “Pitch Analysis (Parselmouth)”: pitch_analysis,
        “Spectral Centroid”: spectral_centroid,
        “Key (Tuning)”: key,
        “Chroma (Tonality Stability)”: chroma_stft
    }
    
    print(“Full Description:”)
    for key, value in full_description.items():
        print(f"{key}: {value}")

# Запуск скрипта
if name == “main”:
    file_path = ‘path_to_your_mp3_file.mp3’
    output_path = ‘output_musicxml_file.xml’
    output_image_path = ‘output_image.png’
    main(file_path, output_path, output_image_path)