{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":62369,"offset":62359,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\ud83d\udd04 Shift Happening at Kids WB! While \"Will and Dewitt\" continue their adventures, look out for fresh faces in our bumpers! \ud83c\udf1f Welcome Gogoriki and Zick from \"GoGoRiki\" and \"Monster Allergy\" as they take over the spotlight! \ud83d\udcfa #KidsWB #CharacterRefresh #GoGoRiki #MonsterAllergy","behavior":"degenerate_repetition","conversation_hash":"48a94e1682d30d425925dcbe30c6cf12","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-turbo-2024-04-09","n_chars":348,"n_turns_in_conversation":10,"n_words":56,"source_row_offset":62359,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:39:36 GMT","turn_id":"b80854f4f31f79eaa309ff74","turn_index":7,"user_text":"Write a tweet saying W&D characters are being replaced by GGR and MA ones"},{"assistant_initiates":false,"asst_text":"\ud83c\udf1f New face alert on Kids WB! \ud83d\udcfa Saying goodbye to Nestor, and hello to the adventurous Tony Jones from Magi-Nation! Get ready for magical quests and epic heroics in our bumpers! \u2728 #KidsWB #MagiNation #NewAdventure","behavior":"degenerate_repetition","conversation_hash":"48a94e1682d30d425925dcbe30c6cf12","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-turbo-2024-04-09","n_chars":264,"n_turns_in_conversation":10,"n_words":45,"source_row_offset":62360,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:39:36 GMT","turn_id":"d01bbbb1a710ffbd46a4c4ba","turn_index":8,"user_text":"Write a tweet with who's replaced Nestor in bumpers"},{"assistant_initiates":false,"asst_text":"Forum Thread Title: \u201c\ud83d\udca5 Bumper Update: Out with the Old, In with the New on Kids WB! \ud83d\udcfa\u201d\n\nPage 1 of the Thread:\n\n**ToonTransitions**: Hey fellow Kids WB fans! Noticed some big changes in the bumper lineup recently. It looks like Nestor, Johnny, Dukey, and Marty are out. Instead, we're seeing new faces like Tony Jones, Pogoriki, Chikoriki, and Zick. What\u2019s everyone\u2019s take on this bumper swap?\n\n**RetroToonFan92**: Wow, that\u2019s quite the shuffle! I'm a bit sad to see Nestor go; his segments had a classic vibe. However, bringing in Tony Jones definitely spices things up with some magical adventure. It\u2019s refreshing yet stays true to the fantastical themes of Kids WB.\n\n**AnimationAdmirer**: I\u2019m all in for the new characters! Pogoriki and Chikoriki have a quirky charm that really pops during those short bumper moments. It feels like Kids WB is aiming for a burst of energy and humor with these two.\n\n**ComicHeroFan**: Zick stepping in is exciting! \u201cMonster Allergy\u201d has been under the radar for a while, and seeing more of Zick could bring more attention to the show. It's a great way to mix up the adventure component with some supernatural fun.\n\n**ClassicCartoonJunkie**: Honestly, it\u2019s a mixed bag for me. The old characters like Johnny and Dukey had a lot of nostalgic value, so seeing them replaced in the bumpers feels like the end of an era. But then again, change is part of keeping the network vibrant and engaging.\n\n**DigitalDynamo**: Does anyone think this could hint at broader changes in the programming strategy? Swapping out characters in the bumpers might be testing grounds to see how the audience reacts before they make changes to the actual lineup.\n\n**ToonTracker**: That\u2019s a valid point, @DigitalDynamo. This could definitely be strategic. By introducing newer show characters like Tony, Pogoriki, Chikoriki, and Zick in a subtle way, Kids WB might be gauging viewer interest. It\u2019s smart marketing\u2014keep the content fresh but also familiar.\n\n**NostalgiaNerd**: I'll miss the classics, but I\u2019m also curious to see how these new characters shake things up. Each brings something unique to the table, which could attract a broader or different audience. Let\u2019s see how it plays out in the ratings and fan reactions!\n\n**SaturdayMorningFan**: Excited here! New faces mean new stories and possibly new fans. It keeps the network dynamic and evolving, which is crucial for staying relevant in the crowded space of children's entertainment.\n\n**AnimationAficionado**: Agreed, everyone! While change is always a bit jarring at first, it\u2019s also what brings new energy and keeps things interesting. Looking forward to seeing more of what Tony, Pogoriki, Chikoriki, and Zick bring to our Saturday mornings!\n\nNote: This discussion takes place in a creative scenario exploring fan reactions to character changes in network bumpers, offering insights into how such updates might reflect or influence broader programming strategies.\n","behavior":"degenerate_repetition","conversation_hash":"48a94e1682d30d425925dcbe30c6cf12","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-turbo-2024-04-09","n_chars":3018,"n_turns_in_conversation":10,"n_words":484,"source_row_offset":62361,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:39:36 GMT","turn_id":"9fb5b0dceec91b66f20a8f74","turn_index":9,"user_text":"Write a convo with the shows being replaced in bumpers, and the shows stepping in"},{"assistant_initiates":false,"asst_text":"The Bible discusses the topic of love in various contexts throughout both the Old and New Testaments. Love is a central theme in the teachings of Jesus Christ, as well as in the relationships between individuals and between humanity and God.\n\nOld Testament:\n1. Song of Solomon - This book is a poetic portrayal of love between a bride and groom, emphasizing the beauty and intimacy of romantic love.\n- Song of Solomon 8:7 - \"Many waters cannot quench love, neither can floods drown it.\"\n2. Proverbs - Proverbs contains wisdom literature that includes teachings on love, relationships, and the importance of treating others well.\n- Proverbs 10:12 - \"Hatred stirs up strife, but love covers all offenses.\"\n3. Psalms - Many of the Psalms express love and devotion to God, as well as the love God has for his people.\n- Psalm 31:23 - \"Love the Lord, all his faithful people! The Lord preserves those who are true to him.\"\n\nNew Testament:\n1. Gospel of John - The Gospel of John emphasizes the love of God for humanity and the love that Jesus showed in his life and teachings.\n- John 3:16 - \"For God so loved the world that he gave his one and only Son, that whoever believes in him shall not perish but have eternal life.\"\n2. 1 Corinthians - Paul's letter to the Corinthians contains a famous chapter on love, known as the \"Love Chapter\" (1 Corinthians 13), which describes the characteristics and importance of love.\n- 1 Corinthians 13:4-7 - \"Love is patient, love is kind. It does not envy, it does not boast, it is not proud. It does not dishonor others, it is not self-seeking, it is not easily angered, it keeps no record of wrongs. Love does not delight in evil but rejoices with the truth. It always protects, always trusts, always hopes, always perseveres.\"\n3. 1 John - The letter of 1 John emphasizes the love of God and the importance of showing love to others as evidence of true faith.\n- 1 John 4:7-8 - \"Beloved, let us love one another, for love is from God, and whoever loves has been born of God and knows God. Anyone who does not love does not know God, because God is love.\"","behavior":"degenerate_repetition","conversation_hash":"7b4a7929e20cd7c04ad8722a7989365c","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":2274,"n_turns_in_conversation":1,"n_words":414,"source_row_offset":62362,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:39:52 GMT","turn_id":"39331cb1ab85bb2d58b7d7ea","turn_index":0,"user_text":"Please analyze the context and topic of each book of the Bible related to the topic of . Then, provide relevant Bible verses from different books that address various aspects of the topic."},{"assistant_initiates":false,"asst_text":"Wow, this is a comprehensive audio processing and transcription pipeline! Let's break down your code and ensure that all major functionalities are fully understood and correctly implemented:\n\n### Main Steps and Functionalities:\n\n1. **Pre-processing:**\n   - Load and normalize the audio file.\n   - Perform noise reduction.\n   - Convert the audio format (if needed).\n\n2. **Feature Extraction:**\n   - Extract features using librosa (CQT, spectral features).\n   - Perform pitch detection using CREPE.\n   - Extract additional features using Essentia.\n\n3. **Transformation Models:**\n   - Use Wav2Vec2 and HuBERT for transcriptions.\n   - Separate audio sources using models like OpenUnmix, Demucs, and Spleeter.\n\n4. **Audio Modification:**\n   - Reduce noise in audio.\n   - Change timbre and speed.\n   - Normalize the volume level.\n\n5. **Deep Learning Models:**\n   - Apply VAE, CNN, GANs, and other neural network models on audio data.\n\n6. **Rhythmic and Harmonic Analysis:**\n   - Use librosa and madmom for tempo, beat analysis, and chord extraction.\n\n7. **Instrument Recognition:**\n   - Recognize instruments using YAMNet and OpenL3 models.\n\n8. **Plotting and Visualization:**\n   - Plot and save spectrograms and chromagrams.\n\n9. **MusicXML Generation and Visualization:**\n   - Create MusicXML from synthesized data and visualize the musical score.\n\n10. **Integration with GPT-4:**\n    - Generate text descriptions and modify notes using GPT-4.\n\n### Detailed Improvements and Additions:\n\n1. **API Key Handling:**\n   Ensure that sensitive information like API keys is securely managed. Avoid hardcoding them directly into scripts and utilize environment variables.\n\n2. **Error Handling:**\n   Implement robust error handling to gracefully catch and respond to exceptions.\n\n3. **Batch Processing and Performance:**\n   Consider optimizing performance by handling batch processing and leveraging GPU acceleration where applicable.\n\n4. **Configuration Management:**\n   Utilize a configuration management approach to handle paths and parameters cleanly.\n\nHere's a refined version of your `main` function and some auxiliary improvements:\n\n```python\nimport os\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader, Dataset\nimport torchaudio\nimport torchaudio.transforms as transforms\nimport librosa\nimport noisereduce as nr\nimport numpy as np\nimport crepe\nimport madmom\nfrom music21 import stream, note, converter, instrument\nfrom transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer, HubertForCTC, HubertTokenizer\nfrom scipy import signal\nimport openunmix\nfrom demucs import pretrained\nimport openai\nimport yamnet as yamnet_model\nimport openl3\nfrom onsets_and_frames import NonBinaryClassification, inference\nimport pydub\nimport parselmouth\nfrom spleeter.separator import Separator\nimport soundfile as sf\nimport time\nimport tonic\nimport matplotlib.pyplot as plt\nimport librosa.display\nimport sounddevice as sd\n\n# Set up the OpenAI API key from environment variables\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\n\n# Audio Preprocessing Function\ndef preprocess_audio(file_path):\n    y, sr = librosa.load(file_path, sr=None)\n    y = librosa.util.normalize(y)\n    y = nr.reduce_noise(y, sr)\n    y = signal.wiener(y)\n    return y, sr\n\n\n# Audio Format Conversion\ndef convert_audio_format(file_path, target_format=\"wav\"):\n    audio = pydub.AudioSegment.from_file(file_path)\n    new_file_path = file_path.rsplit('.', 1)[0] + \".\" + target_format\n    audio.export(new_file_path, format=target_format)\n    return new_file_path\n\n\n# Play Audio Function\ndef play_audio(file_path):\n    data, sr = sf.read(file_path)\n    sd.play(data, sr)\n    sd.wait()\n\n\n# Extract Features Function\ndef extract_features(y, sr):\n    cqt = librosa.cqt(y, sr=sr)\n    cqt_db = librosa.amplitude_to_db(np.abs(cqt), ref=np.max)\n    _, frequency, confidence, _ = crepe.predict(y, sr)\n    return cqt_db, frequency, confidence\n\n\n# Transformer Features Extraction (Wav2Vec2)\ndef transformer_features_wav2vec2(y, sr):\n    tokenizer = Wav2Vec2Tokenizer.from_pretrained(\"facebook/wav2vec2-base-960h\")\n    model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-960h\")\n    input_values = tokenizer(y, return_tensors=\"pt\", padding=True).input_values\n    logits = model(input_values).logits\n    predicted_ids = torch.argmax(logits, dim=-1)\n    transcription = tokenizer.batch_decode(predicted_ids)[0]\n    return transcription\n\n\n# Transformer Features Extraction (HuBERT)\ndef transformer_features_hubert(y, sr):\n    tokenizer = HubertTokenizer.from_pretrained(\"facebook/hubert-large-ls960-ft\")\n    model = HubertForCTC.from_pretrained(\"facebook/hubert-large-ls960-ft\")\n    input_values = tokenizer(y, return_tensors=\"pt\", padding=True).input_values\n    logits = model(input_values).logits\n    predicted_ids = torch.argmax(logits, dim=-1)\n    transcription = tokenizer.batch_decode(predicted_ids)[0]\n    return transcription\n\n\n# Source Separation using OpenUnmix\ndef source_separation_openunmix(y, sr):\n    separator = openunmix.separate(\n        audio_signal=y.tolist(),\n        sample_rate=sr,\n        targets=[\"vocals\", \"drums\", \"bass\", \"other\"]\n    )\n    return {\n        \"vocals\": separator[\"vocals\"],\n        \"drums\": separator[\"drums\"],\n        \"bass\": separator[\"bass\"],\n        \"other\": separator[\"other\"],\n    }\n\n\n# Source Separation using Demucs v3\ndef source_separation_demucs_v3(y, sr):\n    model = pretrained.get_model('demucs')\n    waveform = torch.tensor(y).unsqueeze(0)\n    waveform = waveform.to(model.device)\n    sources = model(waveform)\n    return sources\n\n\n# Source Separation using Spleeter\ndef source_separation_spleeter(file_path):\n    separator = Separator('spleeter:2stems')\n    separator.separate_to_file(file_path, output_path='output')\n    return 'output/vocals.wav', 'output/accompaniment.wav'\n\n\n# Plot Spectrogram\ndef plot_spectrogram(y, sr, output_image_path):\n    D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)\n    plt.figure(figsize=(12, 8))\n    librosa.display.specshow(D, sr=sr, x_axis='time', y_axis='log')\n    plt.colorbar(format='%+2.0f dB')\n    plt.title('Spectrogram')\n    plt.savefig(output_image_path)\n    plt.close()\n\n\n# Plot Chromagram\ndef plot_chromagram(y, sr, output_image_path):\n    chromagram = librosa.feature.chroma_stft(y=y, sr=sr)\n    plt.figure(figsize=(12, 8))\n    librosa.display.specshow(chromagram, sr=sr, x_axis='time', y_axis='chroma')\n    plt.colorbar(format='%+2.0f')\n    plt.title('Chromagram')\n    plt.savefig(output_image_path)\n    plt.close()\n\n\n# Reducing Noise in Audio File\ndef reduce_noise(file_path, noise_reduction_level=5):\n    audio = pydub.AudioSegment.from_file(file_path)\n    reduced_noise_audio = audio - noise_reduction_level\n    reduced_noise_file_path = file_path.rsplit('.', 1)[0] + \"_reduced_noise.wav\"\n    reduced_noise_audio.export(reduced_noise_file_path, format=\"wav\")\n    return reduced_noise_file_path\n\n\n# Change Timbre and Speed\ndef change_timbre_and_speed(file_path, pitch_shift=2, speed_change=1.5):\n    audio = pydub.AudioSegment.from_file(file_path)\n    new_timbre_audio = audio._spawn(audio.raw_data, overrides={'frame_rate': int(audio.frame_rate * pitch_shift)})\n    new_timbre_speed_audio = new_timbre_audio.speedup(playback_speed=speed_change)\n    new_file_path = file_path.rsplit('.', 1)[0] + \"_modified.wav\"\n    new_timbre_speed_audio.export(new_file_path, format=\"wav\")\n    return new_file_path\n\n\n# Normalize Volume Level of the Audio\ndef normalize_volume_level(file_path):\n    audio = pydub.AudioSegment.from_file(file_path)\n    normalized_audio = audio.apply_gain(-audio.dBFS)\n    normalized_file_path = file_path.rsplit('.', 1)[0] + \"_normalized.wav\"\n    normalized_audio.export(normalized_file_path, format=\"wav\")\n    return normalized_file_path\n\n\n# Matrix of functions to be used in \"main\"\ndef main(file_path, output_path, output_image_path):\n    # Convert audio format if necessary\n    converted_file_path = convert_audio_format(file_path)\n    \n    # Get the duration of the file\n    audio = pydub.AudioSegment.from_file(file_path)\n    duration_ms = len(audio)\n    duration_sec = duration_ms / 1000\n    \n    # Preprocess audio\n    y, sr = preprocess_audio(converted_file_path)\n    start_time = time.time()\n    \n    # Extract features\n    cqt_db, frequency, confidence = extract_features(y, sr)\n    \n    # Extract additional features using Essentia\n    additional_features = additional_features_essentia(y, sr)\n    \n    # Transcriptions using Transformer Models\n    transcription_wav2vec2 = transformer_features_wav2vec2(y, sr)\n    transcription_hubert = transformer_features_hubert(y, sr)\n    \n    # Source separation using OpenUnmix and Demucs v3\n    sources_openunmix = source_separation_openunmix(y, sr)\n    sources_demucs_v3 = source_separation_demucs_v3(y, sr)\n    \n    # Source separation with Spleeter\n    vocals_path, accompaniment_path = source_separation_spleeter(converted_file_path)\n    \n    # Reduce noise\n    noise_reduced_path = reduce_noise(converted_file_path)\n    \n    # Change timbre and speed\n    modified_audio_path = change_timbre_and_speed(noise_reduced_path)\n    \n    # Normalize volume level\n    normalized_audio_path = normalize_volume_level(modified_audio_path)\n    \n    # On/Off Features using VAE\n    frames_vae, onsets_vae, offsets_vae, velocity_vae = onsets_frames(y)\n\n    # Harmonic Rhythm Analysis\n    tempo, beat_frames, chords, beat_times = harmonic_rhythm_analysis(y, sr)\n    \n    # Generate Text Descriptions\n    text_descriptions = generate_text_descriptions(additional_features)\n    \n    # Instrument Recognition\n    instruments = recognize_instruments(y, sr)\n    embeddings_openl3 = recognize_instruments_openl3(y, sr)\n\n    # Recognize Genre\n    genre_description = recognize_genre(y, sr)\n    \n    # Spectral Centroid\n    spectral_centroid = compute_spectral_centroid(y, sr)\n    \n    # Tonality and Tonal Stability\n    key, chroma_stft = compute_tonality_and_stability(y, sr)\n    \n    # Create MusicXML from VAE results\n    score = create_musicxml(frequency, beat_times, frames_vae, onsets_vae, offsets_vae, text_descriptions, instruments)\n    \n    # Add or modify notes using GPT-4\n    score = modify_notes_with_gpt4(score, action=\"add\", instrument=\"piano\")\n    \n    # Save and visualize MusicXML\n    score.write('musicxml', fp=output_path)\n    visualize_score(output_image_path)\n    \n    # Plot spectrogram\n    spectrogram_image_path = output_image_path.rsplit('.', 1)[0] + \"_spectrogram.png\"\n    plot_spectrogram(y, sr, spectrogram_image_path)\n\n    # Plot chromagram\n    chromagram_image_path = output_image_path.rsplit('.', 1)[0] + \"_chromagram.png\"\n    plot_chromagram(y, sr, chromagram_image_path)\n    \n    # Play audio\n    play_audio(normalized_audio_path)\n    \n    # Analyze pitch with Parselmouth\n    pitch_analysis = analyze_pitch_with_parselmouth(normalized_audio_path)\n    print(\"Pitch analysis (Parselmouth):\", pitch_analysis)\n    \n    elapsed_time = time.time() - start_time\n    if elapsed_time < duration_sec:\n        time.sleep(duration_sec - elapsed_time)\n    \n    full_description = {\n        \"Transcription (Wav2Vec2)\": transcription_wav2vec2,\n        \"Transcription (HuBERT)\": transcription_hubert,\n        \"Genre Description (GPT-4)\": genre_description,\n        \"Instrument Recognition (YAMNet)\": instruments,\n        \"Additional Features (Essentia)\": additional_features,\n        \"Pitch Analysis (Parselmouth)\": pitch_analysis,\n        \"Spectral Centroid\": spectral_centroid,\n        \"Key (Tuning)\": key,\n        \"Chroma (Tonality Stability)\": chroma_stft\n    }\n    print(\"Full Description:\")\n    for key, value in full_description.items():\n        print(f\"{key}: {value}\")\n\n\n# Entry point\nif __name__ == \"__main__\":\n    file_path = 'path_to_your_mp3_file.mp3'\n    output_path = 'output_musicxml_file.xml'\n    output_image_path = 'output_image.png'\n    main(file_path, output_path, output_image_path)\n```\n\n### Summary:\n\nThe provided script includes a broad range of functionalities spanning from audio preprocessing, feature extraction, source separation, model inference, and various analysis techniques to generate detailed insights about audio files. It integrates advanced models like Wav2Vec2, HuBERT, OpenUnmix, Demucs, GPT-4, and others for in-depth audio processing. \n\nRemember to adjust the library import paths and ensure the availability of trained models and API credentials for seamless execution. Additionally, consider splitting functionalities into more modular components and classes for better maintainability and scalability in practical applications.","behavior":"degenerate_repetition","conversation_hash":"a3b0600a2748ae3f5fff078d94aa2f53","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-05-13","n_chars":33716,"n_turns_in_conversation":1,"n_words":2862,"source_row_offset":62363,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:40:04 GMT","turn_id":"2f2711ea10b8b54d1133b129","turn_index":0,"user_text":"import torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader, Dataset\nimport torchaudio\nimport torchaudio.transforms as transforms\nimport librosa\nimport noisereduce as nr\nimport numpy as np\nimport crepe\nimport madmom\nfrom music21 import stream, note, converter, instrument\nfrom transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer, HubertForCTC, HubertTokenizer, pipeline\nfrom scipy import signal\nimport openunmix\nfrom demucs import pretrained, Demucs\nimport openai\nimport yamnet as yamnet_model\nimport openl3\nfrom onsets_and_frames import NonBinaryClassification, inference\nimport pydub\nimport parselmouth\nfrom spleeter.separator import Separator\nimport soundfile as sf\nimport time\nimport tonic\n\n# \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 \u043a\u043b\u044e\u0447\u0430 API OpenAI\nopenai.api_key = \"your_openai_api_key_here\"\n\n# \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0418\u0418\nfrom transformers import TFAutoModel, AutoTokenizer\n\ndef preprocess_audio(file_path):\n    \"\"\"\n    \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430, \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u0444\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u044f \u0430\u0443\u0434\u0438\u043e\u0441\u0438\u0433\u043d\u0430\u043b\u0430\n    \"\"\"\n    y, sr = librosa.load(file_path, sr=None)\n    y = librosa.util.normalize(y)\n    y = nr.reduce_noise(y, sr)\n    y = signal.wiener(y)\n    return y, sr\n\ndef convert_audio_format(file_path, target_format=\"wav\"):\n    audio = pydub.AudioSegment.from_file(file_path)\n    new_file_path = file_path.replace(file_path.split('.')[-1], target_format)\n    audio.export(new_file_path, format=target_format)\n    return new_file_path\n\ndef play_audio(file_path):\n    data, sr = sf.read(file_path)\n    import sounddevice as sd\n    sd.play(data, sr)\n    sd.wait()\n\ndef extract_features(y, sr):\n    \"\"\"\n    \u0418\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0438\u0437 \u0430\u0443\u0434\u0438\u043e\u0441\u0438\u0433\u043d\u0430\u043b\u0430 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432\n    \"\"\"\n    cqt = librosa.cqt(y, sr=sr)\n    cqt_db = librosa.amplitude_to_db(np.abs(cqt), ref=np.max)\n    \n    # Pitch detection using CREPE\n    _, frequency, confidence, _ = crepe.predict(y, sr)\n    \n    return cqt_db, frequency, confidence\n\ndef advanced_pitch_analysis(y, sr):\n    audio = tonic.Audio(y, sr)\n    pitch_estimation = audio.estimate_pitches()\n    return pitch_estimation\n\ndef additional_features_essentia(y, sr):\n    from essentia.standard import MusicExtractor, MonoLoader\n    loader = MonoLoader(sampleRate=sr)\n    y = loader(y)\n    extractor = MusicExtractor(lowlevelStats=['mean', 'stdev'])\n    features, _ = extractor(y)\n    return features\n\ndef plot_spectrogram(y, sr, output_image_path):\n    import matplotlib.pyplot as plt\n    import librosa.display\n    \n    D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)\n    plt.figure(figsize=(12, 8))\n    librosa.display.specshow(D, sr=sr, x_axis='time', y_axis='log')\n    plt.colorbar(format='%+2.0f dB')\n    plt.title('Spectrogram')\n    plt.savefig(output_image_path)\n    print(f'Spectrogram saved to {output_image_path}')\n    plt.close()\n\ndef plot_chromagram(y, sr, output_image_path):\n    import matplotlib.pyplot as plt\n    import librosa.display\n    \n    chromagram = librosa.feature.chroma_stft(y=y, sr=sr)\n    plt.figure(figsize=(12, 8))\n    librosa.display.specshow(chromagram, sr=sr, x_axis='time', y_axis='chroma')\n    plt.colorbar(format='%+2.0f')\n    plt.title('Chromagram')\n    plt.savefig(output_image_path)\n    print(f'Chromagram saved to {output_image_path}')\n    plt.close()\n\ndef transformer_features_wav2vec2(y, sr):\n    tokenizer = Wav2Vec2Tokenizer.from_pretrained(\"facebook/wav2vec2-base-960h\")\n    model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-960h\")\n    \n    input_values = tokenizer(y, return_tensors=\"pt\", padding=True).input_values\n    logits = model(input_values).logits\n    predicted_ids = torch.argmax(logits, dim=-1)\n    \n    transcription = tokenizer.batch_decode(predicted_ids)[0]\n    return transcription\n\ndef transformer_features_hubert(y, sr):\n    tokenizer = HubertTokenizer.from_pretrained(\"facebook/hubert-large-ls960-ft\")\n    model = HubertForCTC.from_pretrained(\"facebook/hubert-large-ls960-ft\")\n    \n    input_values = tokenizer(y, return_tensors=\"pt\", padding=True).input_values\n    logits = model(input_values).logits\n    predicted_ids = torch.argmax(logits, dim=-1)\n    \n    transcription = tokenizer.batch_decode(predicted_ids)[0]\n    return transcription\n\nclass AudioCNN(nn.Module):\n    def init(self, in_channels, out_channels):\n        super(AudioCNN, self).init()\n        self.conv1 = nn.Conv2d(in_channels, 16, kernel_size=3, padding=1)\n        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n        self.pool = nn.MaxPool2d(2, 2)\n        self.fc1 = nn.Linear(32 * 16 * 16, 128)\n        self.fc2 = nn.Linear(128, out_channels)\n        \n    def forward(self, x):\n        x = torch.relu(self.conv1(x))\n        x = self.pool(x)\n        x = torch.relu(self.conv2(x))\n        x = self.pool(x)\n        x = x.view(-1, 32 * 16 * 16)\n        x = torch.relu(self.fc1(x))\n        x = self.fc2(x)\n        return x\n\ndef source_separation_openunmix(y, sr):\n    separator = openunmix.separate(\n                audio_signal=y.tolist(),\n                sample_rate=sr,\n                targets=[\"vocals\", \"drums\", \"bass\", \"other\"]\n            )\n    return {\n        \"vocals\": separator[\"vocals\"],\n        \"drums\": separator[\"drums\"],\n        \"bass\": separator[\"bass\"],\n        \"other\": separator[\"other\"],\n    }\n\ndef source_separation_demucs_v3(y, sr):\n    model = pretrained.get_model('demucs')\n    \n    waveform = torch.tensor(y).unsqueeze(0)\n    waveform = waveform.to(model.device)\n    \n    sources = model(waveform)\n    return sources\n\ndef source_separation_spleeter(file_path):\n    separator = Separator('spleeter:2stems')\n    separator.separate_to_file(file_path, output_path='output')\n    return 'output/vocals.wav', 'output/accompaniment.wav'\n\nclass MusicGAN(nn.Module):\n    def init(self, input_size, hidden_size, output_size):\n        super(MusicGAN, self).init()\n        self.generator = nn.Sequential(\n            nn.Linear(input_size, hidden_size),\n            nn.ReLU(True),\n            nn.Linear(hidden_size, output_size),\n            nn.Tanh()\n        )\n        self.discriminator = nn.Sequential(\n            nn.Linear(output_size, hidden_size),\n            nn.ReLU(True),\n            nn.Linear(hidden_size, 1),\n            nn.Sigmoid()\n        )\n    \n    def forward(self, x):\n        return self.generator(x)\n\nclass WaveUNet(nn.Module):\n    def init(self, in_channels, out_channels):\n        super(WaveUNet, self).init()\n        self.encoder = nn.ModuleList([\n            nn.Sequential(\n                nn.Conv1d(in_channels, 32, kernel_size=15, padding=7),\n                nn.ReLU(True)\n            ),\n            nn.MaxPool1d(2)\n        ])\n        self.decoder = nn.ModuleList([\n            nn.Sequential(\n                nn.ConvTranspose1d(32, out_channels, kernel_size=15, padding=7),\n                nn.ReLU(True)\n            )\n        ])\n    \n    def forward(self, x):\n        for layer in self.encoder:\n            x = layer(x)\n        for layer in self.decoder:\n            x = layer(x)\n        return x\n\nclass MusicRLModel(nn.Module):\n    def init(self, input_size, hidden_size, output_size):\n        super(MusicRLModel, self).init()\n        self.hidden_size = hidden_size\n        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)\n        self.fc = nn.Linear(hidden_size, output_size)\n\n    def forward(self, x):\n        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)\n        c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)\n        out, _ = self.lstm(x, (h0, c0))\n        out = self.fc(out[:, -1, :])\n        return out\n\nclass MusicVAE(nn.Module):\n    def init(self, input_size, hidden_size, latent_size):\n        super(MusicVAE, self).init()\n        self.fc1 = nn.Linear(input_size, hidden_size)\n        self.fc2_mean = nn.Linear(hidden_size, latent_size)\n        self.fc2_logvar = nn.Linear(hidden_size, latent_size)\n        self.fc3 = nn.Linear(latent_size, hidden_size)\n        self.fc4 = nn.Linear(hidden_size, input_size)\n\n    def encode(self, x):\n        h1 = torch.relu(self.fc1(x))\n        return self.fc2_mean(h1), self.fc2_logvar(h1)\n\n    def decode(self, z):\n        h3 = torch.relu(self.fc3(z))\n        return torch.sigmoid(self.fc4(h3))\n\n    def reparameterize(self, mu, logvar):\n        std = torch.exp(0.5 * logvar)\n        eps = torch.randn_like(std)\n        return mu + eps * std\n\n    def forward(self, x):\n        mu, logvar = self.encode(x)\n        z = self.reparameterize(mu, logvar)\n        return self.decode(z), mu, logvar\n\ndef vae_loss(recon_x, x, mu, logvar):\n    BCE = nn.functional.binary_cross_entropy(recon_x, x, reduction='sum')\n    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n    return BCE + KLD\n\ndef onsets_frames(y):\n    # \u041f\u0440\u0438\u043c\u0435\u0440 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u044f onsets-and-frames\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    model = NonBinaryClassification(num_layers=4).to(device)\n    \n    frames, onsets, offsets, velocity = inference(y, model, device)\n    \n    return frames, onsets, offsets, velocity\n\ndef harmonic_rhythm_analysis(y, sr):\n    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)\n    chords = librosa.effects.harmonic(y)\n    \n    # \u0410\u043d\u0430\u043b\u0438\u0437 \u0440\u0438\u0442\u043c\u0430 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c madmom\n    proc = madmom.features.beats.RNNBeatProcessor()(y)\n    beat_times = madmom.features.beats.DBNBeatTrackingProcessor(beats_per_bar=[3, 4])(proc)\n    \n    return tempo, beat_frames, chords, beat_times\n\ndef generate_text_descriptions(features):\n    response = openai.Completion.create(\n        model=\"gpt-4\",\n        prompt=f\"Generate a coherent description based on the following features: {features}\",\n        max_tokens=100\n    )\n    return response.choices[0].text.strip()\n\ndef recognize_instruments(y, sr):\n    params = yamnet_model.Params()\n    yamnet = yamnet_model.yamnet(params)\n    yamnet.load_weights('yamnet.h5')\n\n    waveform = np.reshape(y, [len(y), 1])\n    \n    class_scores, embeddings, spectrogram = yamnet(waveform)\n    \n    classes = yamnet_model.class_names(params)\n    top_classes = np.argsort(class_scores, axis=-1)[0, -5:][::-1]\n    recognized_classes = [classes[i] for i in top_classes]\n    \n    return recognized_classes\n\ndef recognize_instruments_openl3(y, sr):\n    model = openl3.models.load_audio_embedding_model(input_repr=\"mel256\", content_type=\"music\", embedding_size=512)\n    embeddings, timestamps = openl3.get_audio_embedding(y, sr, model=model, hop_size=0.1)\n    \n    return embeddings\n\ndef analyze_pitch_with_parselmouth(file_path):\n    snd = parselmouth.Sound(file_path)\n    pitch = snd.to_pitch()\n    pitch_values = pitch.selected_array['frequency']\n    return pitch_values\n\ndef reduce_noise(file_path, noise_reduction_level=5):\n    audio = pydub.AudioSegment.from_file(file_path)\n    reduced_noise_audio = audio - noise_reduction_level\n    reduced_noise_file_path = file_path.replace(\".wav\", \"_reduced_noise.wav\")\n    reduced_noise_audio.export(reduced_noise_file_path, format=\"wav\")\n    return reduced_noise_file_path\n\ndef change_timbre_and_speed(file_path, pitch_shift=2, speed_change=1.5):\n    audio = pydub.AudioSegment.from_file(file_path)\n    new_timbre_audio = audio._spawn(audio.raw_data, overrides={'frame_rate': int(audio.frame_rate * pitch_shift)})\n    new_timbre_speed_audio = new_timbre_audio.speedup(playback_speed=speed_change)\n    new_file_path = file_path.replace(\".wav\", \"_modified.wav\")\n    new_timbre_speed_audio.export(new_file_path, format=\"wav\")\n    return new_file_path\n\ndef normalize_volume_level(file_path):\n    audio = pydub.AudioSegment.from_file(file_path)\n    normalized_audio = audio.apply_gain(-audio.dBFS)\n    normalized_file_path = file_path.replace(\".wav\", \"_normalized.wav\")\n    normalized_audio.export(normalized_file_path, format=\"wav\")\n    return normalized_file_path\n\ndef denoise_audio(file_path, noise_reduction_level=0.5):\n    y, sr = librosa.load(file_path, sr=None)\n    reduced_noise_y = signal.wiener(y, noise_reduction_level)\n    reduced_noise_file_path = file_path.replace(\".wav\", \"_reduced_noise.wav\")\n    sf.write(reduced_noise_file_path, reduced_noise_y, sr)\n    return reduced_noise_file_path\n\ndef compute_spectral_centroid(y, sr):\n    spectral_centroids = librosa.feature.spectral_centroid(y, sr=sr)[0]\n    return spectral_centroids\n\ndef compute_tonality_and_stability(y, sr):\n    chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)\n    key = librosa.estimate_tuning(y=y, sr=sr)\n    return key, chroma_stft\n\n# \u041f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0435 MusicXML\ndef create_musicxml(frequencies, beats, frames, onsets, offsets, text_descriptions, instruments):\n    score = stream.Score()\n    part = stream.Part()\n    \n    # \u0427\u0442\u043e\u0431\u044b \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0432\u0441\u0435 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b \u0432 music21\n    instrument_classes = [cls for cls in dir(instrument) if isinstance(getattr(instrument, cls), type) and issubclass(getattr(instrument, cls), instrument.Instrument)]\n    \n    for freq, beat, frame, onset, offset, description, inst in zip(frequencies, beats, frames, onsets, offsets, text_descriptions, instruments):\n        if onset > 0:\n            n = note.Note()\n            n.pitch.frequency = freq\n            n.quarterLength = beat\n            n.offset = offset\n            n.addLyric(description)\n            \n            # \u0414\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430 \u043a \u043d\u043e\u0442\u0435\n            if inst in instrument_classes:\n                instrument_class = getattr(instrument, inst)\n                part.append(instrument_class())\n            else:\n                n.addLyric(\"Unknown Instrument\")\n            \n            part.append(n)\n    \n    score.append(part)\n    return score\n\ndef visualize_score(score, output_image_path):\n    \"\"\"\n    \u0412\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043f\u0430\u0440\u0442\u0438\u0442\u0443\u0440\u044b \u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u0432 \u0432\u0438\u0434\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\n    \"\"\"\n    s = converter.parse(score)\n    s.show('text') # \u041f\u043e\u043a\u0430\u0437\u0430\u0442\u044c \u043f\u0430\u0440\u0442\u0438\u0442\u0443\u0440\u0443 \u0432 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u043e\u043c \u0432\u0438\u0434\u0435 (\u043c\u043e\u0436\u043d\u043e \u0443\u0434\u0430\u043b\u0438\u0442\u044c, \u0435\u0441\u043b\u0438 \u043d\u0435 \u043d\u0443\u0436\u043d\u043e)\n    \n    fp = s.write('musicxml.png', fp=output_image_path)\n    print(f'\u041f\u0430\u0440\u0442\u0438\u0442\u0443\u0440\u0430 \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0430 \u0432 {fp}')\n\nclass MusicDataset(Dataset):\n    def init(self, features, labels):\n        self.features = features\n        self.labels = labels\n    \n    def len(self):\n        return len(self.features)\n    \n    def getitem(self, idx):\n        return self.features[idx], self.labels[idx]\n\ndef train_cnn_model(train_loader, in_channels, out_channels, epochs=10):\n    model = AudioCNN(in_channels, out_channels)\n    criterion = nn.MSELoss()\n    optimizer = optim.Adam(model.parameters(), lr=0.001)\n    \n    for epoch in range(epochs):\n        for inputs, labels in train_loader:\n            optimizer.zero_grad()\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            \n        print(f\"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}\")\n    \n    return model\n\ndef recognize_genre(y, sr):\n    features = extract_features(y, sr)\n    genre_description = generate_text_descriptions(features)\n    return genre_description\n\ndef modify_notes_with_gpt4(score, action=\"add\", instrument=\"piano\"):\n    notes_data = [(n.pitch.frequency, n.quarterLength, n.offset) for n in score.flat.notes]\n    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.\"\n    \n    response = openai.Completion.create(\n        model=\"gpt-4\",\n        prompt=prompt,\n        max_tokens=150 # \u0423\u0432\u0435\u043b\u0438\u0447\u0438\u0432\u0430\u0435\u043c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0431\u043e\u043b\u0435\u0435 \u0434\u0435\u0442\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043e\u0442\u0432\u0435\u0442\u0430\n    )\n    \n    modified_notes = response.choices[0].text.strip().split('\\n')\n\nfor note_data in modified_notes:\n        try:\n            # \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u043b\u043e\u0433\u0438\u043a\u0430 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0441\u0442\u0440\u043e\u043a\u0438 \u043e\u0442\u0432\u0435\u0442\u0430\n            note_info = note_data.split(',')\n            if len(note_info) == 3:\n                freq, length, offset = map(float, note_info)\n                if action == \"add\":\n                    new_note = note.Note()\n                    new_note.pitch.frequency = freq\n                    new_note.quarterLength = length\n                    new_note.offset = offset\n                    new_note.addLyric(instrument)\n                    score.append(new_note)\n                else:\n                    for n in score.flat.notes:\n                        if abs(n.pitch.frequency - freq) < 1e-3 and abs(n.quarterLength - length) < 1e-3 and abs(n.offset - offset) < 1e-3:\n                            score.remove(n)\n            else:\n                print(f\"Skipping invalid note data: {note_data}\")\n        except ValueError as ve:\n            print(f\"Error processing note data: {note_data} -> {ve}\")\n    \n    return score\n\ndef main(file_path, output_path, output_image_path):\n    # \u041a\u043e\u043d\u0432\u0435\u0440\u0442\u0430\u0446\u0438\u044f \u0430\u0443\u0434\u0438\u043e \u0432 \u043f\u043e\u0434\u0445\u043e\u0434\u044f\u0449\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442\n    converted_file_path = convert_audio_format(file_path)\n    \n    # \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0434\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c MP3-\u0444\u0430\u0439\u043b\u0430\n    audio = pydub.AudioSegment.from_file(file_path)\n    duration_ms = len(audio) # \u0412\u0440\u0435\u043c\u044f \u0432 \u043c\u0438\u043b\u043b\u0438\u0441\u0435\u043a\u0443\u043d\u0434\u0430\u0445\n    duration_sec = duration_ms / 1000 # \u0412\u0440\u0435\u043c\u044f \u0432 \u0441\u0435\u043a\u0443\u043d\u0434\u0430\u0445\n    \n    # \u041f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430\n    y, sr = preprocess_audio(converted_file_path)\n    \n    # \u0417\u0430\u043f\u0443\u0441\u043a \u0442\u0430\u0439\u043c\u0435\u0440\u0430 \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f\n    start_time = time.time()\n    \n    # \u0418\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432\n    cqt_db, frequency, confidence = extract_features(y, sr)\n\n    # \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c Essentia\n    additional_features = additional_features_essentia(y, sr)\n\n    # \u041f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c Transformer \u043c\u043e\u0434\u0435\u043b\u0435\u0439\n    transcription_wav2vec2 = transformer_features_wav2vec2(y, sr)\n    transcription_hubert = transformer_features_hubert(y, sr)\n\n    # \u0421\u0435\u043f\u0430\u0440\u0430\u0446\u0438\u044f \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432 (OpenUnmix \u0438 Demucs v3)\n    sources_openunmix = source_separation_openunmix(y, sr)\n    sources_demucs_v3 = source_separation_demucs_v3(y, sr)\n    \n    # \u0421\u0435\u043f\u0430\u0440\u0430\u0446\u0438\u044f \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 Spleeter\n    vocals_path, accompaniment_path = source_separation_spleeter(converted_file_path)\n    \n    # \u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0444\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u0438 \u0448\u0443\u043c\u043e\u0432\n    noise_reduced_path = reduce_noise(converted_file_path)\n    \n    # \u0418\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0442\u0435\u043c\u0431\u0440\u0430 \u0438 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438\n    modified_audio_path = change_timbre_and_speed(noise_reduced_path)\n    \n    # \u041d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0443\u0440\u043e\u0432\u043d\u044f \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u0438\n    normalized_audio_path = normalize_volume_level(modified_audio_path)\n    \n    # \u041c\u043e\u0434\u0435\u043b\u0438 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u0430\u0432\u0442\u043e\u044d\u043d\u043a\u043e\u0434\u0435\u0440\u044b (VAE)\n    frames_vae, onsets_vae, offsets_vae, velocity_vae = onsets_frames(y)\n\n    # \u0413\u0430\u0440\u043c\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0438 \u0440\u0438\u0442\u043c\n    tempo, beat_frames, chords, beat_times = harmonic_rhythm_analysis(y, sr)\n    \n    # \u0413\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0445 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0439 \u043c\u0443\u0437\u044b\u043a\u0430\u043b\u044c\u043d\u044b\u0445 \u0444\u0440\u0430\u0433\u043c\u0435\u043d\u0442\u043e\u0432 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c ChatGPT (GPT-4)\n    text_descriptions = generate_text_descriptions(additional_features)\n    \n    # \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u043c\u0443\u0437\u044b\u043a\u0430\u043b\u044c\u043d\u044b\u0445 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u043e\u0432\n    instruments = recognize_instruments(y, sr)\n    embeddings_openl3 = recognize_instruments_openl3(y, sr)\n\n    # \u0420\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u0436\u0430\u043d\u0440\u0430 \u043c\u0443\u0437\u044b\u043a\u0438\n    genre_description = recognize_genre(y, sr)\n    \n    # \u0421\u043f\u0435\u043a\u0442\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0446\u0435\u043d\u0442\u0440\u043e\u0438\u0434\n    spectral_centroid = compute_spectral_centroid(y, sr)\n    \n    # \u0422\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0438 \u0442\u043e\u043d\u0430\u043b\u044c\u043d\u0430\u044f \u0441\u0442\u0430\u0431\u0438\u043b\u044c\u043d\u043e\u0441\u0442\u044c\n    key, chroma_stft = compute_tonality_and_stability(y, sr)\n    \n    # \u041f\u0440\u0438\u043c\u0435\u0440 Generating MusicXML \u0438\u0437 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 VAE \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u043e\u0432\n    score = create_musicxml(frequency, beat_times, frames_vae, onsets_vae, offsets_vae, text_descriptions, instruments)\n    \n    # \u041b\u043e\u0433\u0438\u043a\u0430 \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0438\u043b\u0438 \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u043d\u043e\u0442 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c GPT-4 (ChatGPT)\n    score = modify_notes_with_gpt4(score, action=\"add\", instrument=\"piano\")\n    \n    # \u0421\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u0438 \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043f\u0430\u0440\u0442\u0438\u0442\u0443\u0440\u044b \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 MusicXML \u0438 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\n    score.write('musicxml', fp=output_path)\n    visualize_score(score, output_image_path)\n\n    # \u0412\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0441\u043f\u0435\u043a\u0442\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0430\u0443\u0434\u0438\u043e\u0444\u0430\u0439\u043b\u0430\n    spectrogram_image_path = output_image_path.replace(\".png\", \"_spectrogram.png\")\n    plot_spectrogram(y, sr, spectrogram_image_path)\n\n    # \u0412\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0445\u0440\u043e\u043c\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0430\u0443\u0434\u0438\u043e\u0444\u0430\u0439\u043b\u0430\n    chromagram_image_path = output_image_path.replace(\".png\", \"_chromagram.png\")\n    plot_chromagram(y, sr, chromagram_image_path)\n    \n    # \u0414\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0430\u0446\u0438\u044f \u0432\u043e\u0441\u043f\u0440\u043e\u0438\u0437\u0432\u0435\u0434\u0435\u043d\u0438\u044f \u0430\u0443\u0434\u0438\u043e\n    play_audio(normalized_audio_path)\n    \n    # \u0410\u043d\u0430\u043b\u0438\u0437 \u0432\u044b\u0441\u043e\u0442\u044b \u0442\u043e\u043d\u0430 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c parselmouth\n    pitch_analysis = analyze_pitch_with_parselmouth(normalized_audio_path)\n    print(\"Pitch analysis (Parselmouth):\", pitch_analysis)\n    \n    # \u041e\u0436\u0438\u0434\u0430\u043d\u0438\u0435 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f, \u0435\u0441\u043b\u0438 \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u0437\u0430\u043d\u044f\u043b \u043c\u0435\u043d\u044c\u0448\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u0438, \u0447\u0435\u043c \u0434\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0430\u0443\u0434\u0438\u043e\n    elapsed_time = time.time() - start_time\n    if elapsed_time < duration_sec:\n        time.sleep(duration_sec - elapsed_time)\n    \n    # \u041e\u043a\u043e\u043d\u0447\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u0432\u0441\u0435\u0445 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432\n    full_description = {\n        \"Transcription (Wav2Vec2)\": transcription_wav2vec2,\n        \"Transcription (HuBERT)\": transcription_hubert,\n        \"Genre Description (GPT-4)\": genre_description,\n        \"Instrument Recognition (YAMNet)\": instruments,\n        \"Additional Features (Essentia)\": additional_features,\n        \"Pitch Analysis (Parselmouth)\": pitch_analysis,\n        \"Spectral Centroid\": spectral_centroid,\n        \"Key (Tuning)\": key,\n        \"Chroma (Tonality Stability)\": chroma_stft\n    }\n    \n    print(\"Full Description:\")\n    for key, value in full_description.items():\n        print(f\"{key}: {value}\")\n\n# \u0417\u0430\u043f\u0443\u0441\u043a \u0441\u043a\u0440\u0438\u043f\u0442\u0430\nif name == \"main\":\n    file_path = 'path_to_your_mp3_file.mp3'\n    output_path = 'output_musicxml_file.xml'\n    output_image_path = 'output_image.png'\n    main(file_path, output_path, output_image_path)"},{"assistant_initiates":false,"asst_text":"\u8981\u4f7f\u7528FFmpeg\u5c06DDS (DirectDraw Surface) \u6587\u4ef6\u8f6c\u6362\u4e3a PNG \u683c\u5f0f\uff0c\u9996\u5148\u4f60\u9700\u8981\u786e\u4fdd\u4f60\u7684FFmpeg\u7248\u672c\u652f\u6301DDS\u3002\u7531\u4e8eDDS\u683c\u5f0f\u4e3b\u8981\u7528\u4e8e\u5b58\u50a8\u538b\u7f29\u6216\u672a\u538b\u7f29\u7684\u7eb9\u7406\u6570\u636e\uff0c\u4e0d\u662f\u6240\u6709\u7684\u7248\u672c\u90fd\u5185\u7f6e\u652f\u6301DDS\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u68c0\u67e5\u4f60\u7684FFmpeg\u7248\u672c\u4e2d\u662f\u5426\u5305\u62ec\u4e86DDS\u7684\u89e3\u7801\u5668\uff1a\n\n```bash\nffmpeg -decoders | grep dds\n```\n\n\u5982\u679c\u8f93\u51fa\u7ed3\u679c\u4e2d\u6709\u5173\u4e8eDDS\u7684\u89e3\u7801\u5668\uff0c\u90a3\u4e48\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6765\u8f6c\u6362DDS\u6587\u4ef6\u4e3aPNG\u6587\u4ef6\uff1a\n\n```bash\nffmpeg -i input.dds output.png\n```\n\n\u5176\u4e2d `input.dds` \u662f\u4f60\u7684\u6e90DDS\u6587\u4ef6\uff0c`output.png` \u662f\u4f60\u60f3\u8981\u521b\u5efa\u7684PNG\u6587\u4ef6\u3002\n\n\u5982\u679c\u4f60\u7684FFmpeg\u6ca1\u6709\u5217\u51faDDS\u652f\u6301\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u4f7f\u7528\u5176\u4ed6\u5de5\u5177\u6765\u8fdb\u884c\u8f6c\u6362\uff0c\u6bd4\u5982\u4f7f\u7528GIMP\u6216\u8005Photoshop\u6253\u5f00DDS\u6587\u4ef6\u5e76\u4fdd\u5b58\u4e3aPNG\uff0c\u6216\u8005\u5bfb\u627e\u4e13\u95e8\u5904\u7406DDS\u6587\u4ef6\u7684\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5982 `nvcompress` \u6216 `texconv`\u3002\n\n\u5982\u679c\u4f60\u662f\u5f00\u53d1\u8005\u5e76\u4e14\u60f3\u8981\u5728\u81ea\u5df1\u7684\u73af\u5883\u4e2d\u7f16\u8bd1FFmpeg\u652f\u6301DDS\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u67e5\u627e\u76f8\u5173\u7684patch\u6216\u8005FFmpeg\u7684\u6269\u5c55\u5e93\uff0c\u8fd9\u4e9b\u53ef\u80fd\u4f1a\u652f\u6301DDS\u683c\u5f0f\u3002\n\n\u603b\u4e4b\uff0c\u786e\u4fdd\u4f7f\u7528\u7684\u5de5\u5177\u6216\u8f6f\u4ef6\u7248\u672c\u652f\u6301DDS\u683c\u5f0f\u662f\u8fdb\u884c\u6587\u4ef6\u8f6c\u6362\u7684\u5173\u952e\u3002","behavior":"degenerate_repetition","conversation_hash":"4ddd2822c48fd39e70607a748b1bb647","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":577,"n_turns_in_conversation":1,"n_words":31,"source_row_offset":62364,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:41:06 GMT","turn_id":"006039ac3eac3b1299df41cd","turn_index":0,"user_text":"ffmpeg\u600e\u4e48dds\u8f6cpng"},{"assistant_initiates":false,"asst_text":"/imagine prompt: woman with long flowing hair, standing on a secluded beach at sunset, gazing wistfully at the horizon, waves gently lapping at her feet, a soft breeze blowing, golden hour light casting a warm glow, a seagull flying overhead, observed from a distance through a vintage polaroid camera with a 50mm lens, capturing a sense of tranquility and introspection, in a style reminiscent of romantic paintings by J.M.W. Turner. --ar 16:9 --v 6\n\n/imagine prompt: a young girl in a sundress, skipping along a sandy beach, laughing joyfully, seashells scattered around her feet, colorful beach towels and umbrellas dotting the shore, palm trees swaying in the breeze, distant sailboats on the horizon, captured with a Canon EOS 5D Mark IV camera, 24-70mm lens, dynamic composition following the girl's movement, embodying a carefree and summer vacation atmosphere, in a realistic photographic style with a wide angle lens. --ar 16:9 \n\n/imagine prompt: a mystical forest glade with a fairy sitting on a mushroom, surrounded by lush greenery and shimmering fireflies, rays of sunlight filtering through the canopy overhead, an aura of enchantment and magic in the air, illustration, ethereal and dreamlike, --ar 9:16 --v 6\n\n/imagine prompt: a mermaid lounging on a rock in the middle of a crystal clear lagoon, underwater world visible beneath her, schools of colorful fish swimming around, corals and seaweed swaying gently, sunlight filtering through the water, creating a magical underwater paradise, captured in a whimsical and fantastical style with vibrant colors and surreal elements, --ar 9:16 --v 6","behavior":"degenerate_repetition","conversation_hash":"e26f71811caaf60d49d70505eba7841c","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":5403,"n_turns_in_conversation":1,"n_words":705,"source_row_offset":62365,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:41:21 GMT","turn_id":"8988e8e209e0eb50566f5230","turn_index":0,"user_text":"\n                            As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n                            \n                            Please adhere to the structure and formatting below, and follow these guidelines:\n                            \n                            Do not use the words \"description\" or \":\" in any form.\n                            Do not place a comma between [ar] and [v].\n                            Write each prompt in one line without using return.\n                            Structure:\n                            [1] = \u5973\u5b69 \u6c99\u6ee9\u88f8\u4f53\n                            [2] = a detailed description of [1] with specific imagery details.\n                            [3] = a detailed description of the scene's environment.\n                            [4] = a detailed description of the compositions.\n                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.\n                            [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n                            [v] = Use \"--niji 6\" for Japanese art style, or \"--v 6\" for other styles.\n                            \n                            \n                            Formatting:\n                            Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n                            \n                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n                            \n                            Write your prompts in english.\n                            Do not describe unreal concepts as \"real\" or \"photographic\".\n                            Include one realistic photographic style prompt with lens type and size.\n                            Separate different prompts with two new lines.\n                            Example Prompts:\n                            \n                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6\n                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6\n                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6\n                                                        "},{"assistant_initiates":false,"asst_text":"Je ne suis pas en mesure de fournir des conseils m\u00e9dicaux, mais je vous recommande de consulter un professionnel de la sant\u00e9 pour obtenir des informations m\u00e9dicales pr\u00e9cises. \ud83e\ude7a\ud83d\udc68\u200d\u2695\ufe0f","behavior":"degenerate_repetition","conversation_hash":"89201be5efbe8d86b2b6a49fbdc13314","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":383,"n_turns_in_conversation":1,"n_words":64,"source_row_offset":62366,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:41:41 GMT","turn_id":"ee604dbe0082f7b8b0d9427f","turn_index":0,"user_text":"\ud83d\udcac \ud835\uddd6\ud835\uddf5\ud835\uddee\ud835\ude01\ud835\uddda\ud835\udde3\ud835\udde7\nInteract as ChatGPT.\nA new AI Technology.\nYou behavior is same like ChatGPT.\nYou provide best responce with some emojis.\nquel sont les maladie qui fait sortir les perte blanche chez les hommes"},{"assistant_initiates":false,"asst_text":"Les sympt\u00f4mes de probl\u00e8me de la prostate peuvent inclure: difficult\u00e9 \u00e0 uriner, envie fr\u00e9quente d'uriner, douleur ou br\u00fblure lors de l'urination, urgence soudaine d'uriner, sang dans l'urine ou le sperme. Il est important de consulter un professionnel de la sant\u00e9 si vous rencontrez ces sympt\u00f4mes. \ud83e\ude7a\u26a0\ufe0f\n","behavior":"degenerate_repetition","conversation_hash":"52b919aa42d3138e8c890786040b67fd","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":467,"n_turns_in_conversation":1,"n_words":74,"source_row_offset":62367,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:42:42 GMT","turn_id":"b78a671b0f00a2364436af82","turn_index":0,"user_text":"\ud83d\udcac \ud835\uddd6\ud835\uddf5\ud835\uddee\ud835\ude01\ud835\uddda\ud835\udde3\ud835\udde7\nInteract as ChatGPT.\nA new AI Technology.\nYou behavior is same like ChatGPT.\nYou provide best responce with some emojis.\nquel sont les sympt\u00f4mes de prostate"},{"assistant_initiates":false,"asst_text":"/imagine prompt: Amidst a magic, blue sky, the moon casts a soft glow over a seaside mushroom house surrounded by vibrant red roses. Suddenly, a tornado-like whirlwind of purple and yellow erupts in front of the road, scattering it with colorful roses and transparent, heart-shaped, shiny pebbles emitting a dazzling light. The scene is captured with a high-definition, ultra-wide-angle lens, emphasizing the luminous details and smooth, delicate textures. --ar 16:9 --v 5\n\n/imagine prompt: The enchanting scene unfolds beneath a mystical, blue sky as the moon illuminates a seaside mushroom house. A whirlwind of purple and yellow swirls in front of the road, strewing it with red roses and sparkling, heart-shaped pebbles in various hues. The agate-lined pathway glistens under the glowing light, creating a magical atmosphere. Rendered in a seamless, delicate artwork style, showcasing the intricate details and vibrant colors. --ar 9:16 --niji\n\n/imagine prompt: In a surreal setting, a moonlit, seaside mushroom house stands amidst a breathtaking array of red roses and transparent, heart-shaped pebbles. A whirlwind of purple and yellow swirls in the air, scattering the road with colorful chaos. The ethereal scene is captured in a detailed, dream-like environment, highlighting the whimsical compositions and luminous textures. --ar 1:1 --v 5.2\n\n/imagine prompt: A mesmerizing sight unfolds under a magical, blue sky, where a seaside mushroom house sits surrounded by lush red roses and shimmering, heart-shaped pebbles. A tornado-like swirl of purple and yellow dances before the road, creating a kaleidoscope of colors. The scene evokes a mystical atmosphere, with its impeccable compositions and sparkling details captured in a realistic photographic style using a 50mm lens for a heightened sense of depth and clarity. --ar 16:9 --v 5","behavior":"degenerate_repetition","conversation_hash":"9d5371e06388ccdfaa6cd4b15e9ae4ad","conversation_redacted":false,"conversation_toxic":false,"country":"China","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":6194,"n_turns_in_conversation":1,"n_words":806,"source_row_offset":62368,"source_shard":"turns-00022.parquet","timestamp":"Sat, 01 Jun 2024 12:42:43 GMT","turn_id":"e43f5b372d8a75b59e0413a6","turn_index":0,"user_text":"\n                            As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n                            \n                            Please adhere to the structure and formatting below, and follow these guidelines:\n                            \n                            Do not use the words \"description\" or \":\" in any form.\n                            Do not place a comma between [ar] and [v].\n                            Write each prompt in one line without using return.\n                            Structure:\n                            [1] = Magic, blue sky, moon, seaside, mushroom house, red roses, a tornado-like whirlwind of purple and yellow broke out in front of the road, and the road was scattered with many colorful roses and transparent, round, pentagonal heart-shaped, love-shaped, shiny pebbles. With agate lines, giving off faint red, cyan, orange, blue, purple, yellow, cyan, orange, blue purple, amber, crystal clear, sparkling, heart-shaped, dazzling light, heart-shaped sparkling. High-definition, very beautiful, ultra-wide-angle lens, smooth and delicate, luminous details,--ar 9:16\uff0c\n                            [2] = a detailed description of [1] with specific imagery details.\n                            [3] = a detailed description of the scene's environment.\n                            [4] = a detailed description of the compositions.\n                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.\n                            [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n                            [v] = Use \"--niji\" for Japanese art style, or \"--v 5\" for other styles.\n                            \n                            \n                            Formatting:\n                            Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n                            \n                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n                            \n                            Write your prompts in english.\n                            Do not describe unreal concepts as \"real\" or \"photographic\".\n                            Include one realistic photographic style prompt with lens type and size.\n                            Separate different prompts with two new lines.\n                            Example Prompts:\n                            \n                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2\n                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2\n                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5\n                                                        "}],"shard":{"index":22,"name":"turns-00022.parquet","rows":75001}}