AI作曲家与人类音乐人：ElevenLabs Music的协作新范式

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;

import "@openzeppelin/contracts/access/AccessControl.sol";
import "@openzeppelin/contracts/token/ERC721/ERC721.sol";
import "@openzeppelin/contracts/utils/ReentrancyGuard.sol";

contract AIMusicCollaboration is ERC721, AccessControl, ReentrancyGuard {
    bytes32 public constant AI_COMPOSER_ROLE = keccak256("AI_COMPOSER_ROLE");
    bytes32 public constant HUMAN_MUSICIAN_ROLE = keccak256("HUMAN_MUSICIAN_ROLE");
    bytes32 public constant PRODUCER_ROLE = keccak256("PRODUCER_ROLE");

    enum CollaborationType {
        TEXT_TO_MUSIC, REFERENCE_STYLE, HYBRID, LIVE_PERFORMANCE, REMIX
    }

    enum ContributionType {
        MELODY, HARMONY, RHYTHM, BASS, LYRICS, ARRANGEMENT, PRODUCTION, MIXING
    }

    struct MusicTrack {
        uint256 trackId;
        string title;
        string ipfsCID;
        uint256 duration;
        string genre;
        string bpm;
        string key;
        address[] humanContributors;
        address[] aiContributors;
        uint256 aiContributionPercent;
        bool isRegistered;
    }

    struct CollaborationSession {
        uint256 sessionId;
        address[] participants;
        string prompt;
        string referenceIPFS;
        CollaborationType collabType;
        uint256 startTime;
        uint256 endTime;
        uint256 iterations;
        uint256[] generatedTrackIds;
        bool isComplete;
    }

    struct Contribution {
        address contributor;
        ContributionType contributionType;
        uint256 contributionPercent;
        uint256 royaltyShare;
        bool isVerified;
    }

    uint256 public trackCount;
    uint256 public sessionCount;

    mapping(uint256 => MusicTrack) public tracks;
    mapping(uint256 => CollaborationSession) public sessions;
    mapping(uint256 => Contribution[]) public trackContributions;
    mapping(address => uint256) public totalRoyalties;
    mapping(address => uint256) public aiContributionScore;

    uint256 public baseRoyaltyRate = 100; // basis points
    uint256 public aiRoyaltyPool = 0;
    uint256 public minContributionPercent = 100; // 1%

    event TrackRegistered(
        uint256 indexed trackId,
        string title,
        address indexed creator,
        uint256 aiContributionPercent
    );

    event CollaborationCompleted(
        uint256 indexed sessionId,
        uint256 indexed trackId,
        address[] participants
    );

    function registerTrack(
        string memory _title,
        string memory _ipfsCID,
        uint256 _duration,
        string memory _genre,
        address[] memory _humanContributors,
        address[] memory _aiContributors,
        uint256 _aiContributionPercent
    ) external onlyRole(PRODUCER_ROLE) returns (uint256) {
        trackCount++;
        uint256 trackId = trackCount;

        tracks[trackId] = MusicTrack({
            trackId: trackId,
            title: _title,
            ipfsCID: _ipfsCID,
            duration: _duration,
            genre: _genre,
            bpm: "120",
            key: "C",
            humanContributors: _humanContributors,
            aiContributors: _aiContributors,
            aiContributionPercent: _aiContributionPercent,
            isRegistered: true
        });

        _mint(msg.sender, trackId);

        emit TrackRegistered(trackId, _title, msg.sender, _aiContributionPercent);
        return trackId;
    }

    function calculateRoyaltyDistribution(uint256 _trackId) external view returns (Contribution[] memory) {
        MusicTrack storage track = tracks[_trackId];
        uint256 humanShare = 10000 - track.aiContributionPercent;
        uint256 perHumanShare = humanShare / track.humanContributors.length;

        Contribution[] memory distributions = new Contribution[](track.humanContributors.length);
        for (uint256 i = 0; i < track.humanContributors.length; i++) {
            distributions[i] = Contribution({
                contributor: track.humanContributors[i],
                contributionType: ContributionType.ARRANGEMENT,
                contributionPercent: perHumanShare,
                royaltyShare: perHumanShare * baseRoyaltyRate / 10000,
                isVerified: true
            });
        }
        return distributions;
    }

    function claimRoyalties() external nonReentrant {
        uint256 amount = totalRoyalties[msg.sender];
        require(amount > 0, "No royalties to claim");
        totalRoyalties[msg.sender] = 0;
        payable(msg.sender).transfer(amount);
    }

    function supportsInterface(bytes4 interfaceId) public view override(ERC721, AccessControl) returns (bool) {
        return super.supportsInterface(interfaceId);
    }
}

import torch
import torchaudio
from transformers import AutoModelForAudioGeneration, AutoProcessor
from diffusers import AudioDiffusionPipeline
import numpy as np
from typing import Optional, Dict, List
from dataclasses import dataclass
import librosa

@dataclass
class MusicParameters:
    bpm: int = 120
    key: str = "C"
    genre: str = "classical"
    instruments: List[str] = None
    mood: str = "peaceful"
    duration: int = 30

class AIMusicCollaborator:
    def __init__(self, model_name: str = "elevenlabs/music-v2"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.processor = AutoProcessor.from_pretrained(model_name)
        self.model = AutoModelForAudioGeneration.from_pretrained(model_name).to(self.device)
        self.diffusion_pipeline = AudioDiffusionPipeline.from_pretrained(
            "audio-diffusion/music-gen",
            torch_dtype=torch.float16
        ).to(self.device)

    def generate_from_text(self, prompt: str, params: MusicParameters) -> np.ndarray:
        inputs = self.processor(
            text=prompt,
            return_tensors="pt",
            padding=True,
            truncation=True
        ).to(self.device)

        with torch.no_grad():
            audio_values = self.model.generate(
                **inputs,
                max_length=params.duration * 16000,
                temperature=0.8,
                top_k=50,
                top_p=0.95,
                do_sample=True
            )

        audio = audio_values.cpu().numpy().squeeze()
        return self.apply_parameters(audio, params)

    def generate_from_reference(self, reference_path: str, params: MusicParameters) -> np.ndarray:
        reference_audio, sr = torchaudio.load(reference_path)
        if sr != 16000:
            resampler = torchaudio.transforms.Resample(sr, 16000)
            reference_audio = resampler(reference_audio)

        reference_audio = reference_audio.to(self.device)
        inputs = self.processor(
            audio=reference_audio,
            sampling_rate=16000,
            return_tensors="pt"
        ).to(self.device)

        with torch.no_grad():
            audio_values = self.model.generate(
                **inputs,
                max_length=params.duration * 16000,
                temperature=0.7,
                do_sample=True
            )

        audio = audio_values.cpu().numpy().squeeze()
        return self.apply_parameters(audio, params)

    def apply_parameters(self, audio: np.ndarray, params: MusicParameters) -> np.ndarray:
        current_bpm, _ = librosa.beat.beat_track(y=audio, sr=16000)
        if abs(current_bpm - params.bpm) > 10:
            audio = librosa.effects.time_stretch(audio, rate=current_bpm / params.bpm)
        return audio

    def collaborative_loop(self, human_input: np.ndarray, style_prompt: str) -> np.ndarray:
        params = MusicParameters(bpm=120, genre="electronic", duration=len(human_input) // 16000)
        ai_generated = self.generate_from_text(style_prompt, params)
        min_len = min(len(human_input), len(ai_generated))
        mixed = 0.6 * human_input[:min_len] + 0.4 * ai_generated[:min_len]
        return mixed

    def extract_features(self, audio_path: str) -> Dict:
        audio, sr = librosa.load(audio_path, sr=16000)
        features = {
            'tempo': librosa.beat.beat_track(y=audio, sr=sr)[0],
            'key': librosa.feature.spectral_centroid(y=audio, sr=sr).mean(),
            'mfccs': librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13).mean(axis=1).tolist(),
            'chroma': librosa.feature.chroma_stft(y=audio, sr=sr).mean(axis=1).tolist(),
            'spectral_bandwidth': librosa.feature.spectral_bandwidth(y=audio, sr=sr).mean(),
            'zero_crossing_rate': librosa.feature.zero_crossing_rate(audio).mean()
        }
        return features

collaborator = AIMusicCollaborator()
params = MusicParameters(bpm=90, genre="jazz", mood="melancholic", duration=45)
audio = collaborator.generate_from_text("A melancholic jazz piano piece with soft double bass", params)

const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const path = require('path');

class ElevenLabsMusicClient {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.baseURL = 'https://api.elevenlabs.io/v1';
    this.client = axios.create({
      baseURL: this.baseURL,
      headers: {
        'xi-api-key': this.apiKey,
        'Content-Type': 'application/json'
      }
    });
  }

  async generateMusic(prompt, options = {}) {
    const payload = {
      text: prompt,
      duration: options.duration || 30,
      temperature: options.temperature || 0.8,
      style: options.style || 'instrumental',
      genre: options.genre || 'pop',
      instruments: options.instruments || [],
      bpm: options.bpm || 120,
      key: options.key || 'C',
      mode: options.mode || 'major'
    };

    try {
      const response = await this.client.post('/music/generate', payload);
      return {
        taskId: response.data.task_id,
        status: response.data.status,
        estimatedTime: response.data.estimated_time
      };
    } catch (error) {
      console.error('Generation error:', error.response?.data || error.message);
      throw error;
    }
  }

  async getGenerationStatus(taskId) {
    try {
      const response = await this.client.get(`/music/task/${taskId}`);
      return {
        status: response.data.status,
        progress: response.data.progress,
        result: response.data.result
      };
    } catch (error) {
      console.error('Status check error:', error.message);
      throw error;
    }
  }

  async generateFromReference(referencePath, prompt, options = {}) {
    const formData = new FormData();
    formData.append('audio', fs.createReadStream(referencePath));
    formData.append('text', prompt);
    formData.append('duration', options.duration || 30);
    formData.append('style', options.style || 'reference');

    try {
      const response = await axios.post(
        `${this.baseURL}/music/generate-from-reference`,
        formData,
        {
          headers: {
            'xi-api-key': this.apiKey,
            ...formData.getHeaders()
          }
        }
      );
      return response.data;
    } catch (error) {
      console.error('Reference generation error:', error.message);
      throw error;
    }
  }

  async editTrack(trackId, edits) {
    const payload = {
      track_id: trackId,
      edits: edits.map(edit => ({
        start_time: edit.startTime,
        end_time: edit.endTime,
        type: edit.type,
        parameters: edit.parameters
      }))
    };

    try {
      const response = await this.client.post('/music/edit', payload);
      return response.data;
    } catch (error) {
      console.error('Edit error:', error.response?.data || error.message);
      throw error;
    }
  }

  async collaborate(humanTrackId, aiPrompt, options = {}) {
    const collaborationPayload = {
      human_track_id: humanTrackId,
      ai_prompt: aiPrompt,
      collaboration_mode: options.mode || 'alternating',
      human_bars: options.humanBars || 4,
      ai_bars: options.aiBars || 4,
      crossover_style: options.crossover || 'smooth',
      total_bars: options.totalBars || 32
    };

    try {
      const response = await this.client.post('/music/collaborate', collaborationPayload);
      return {
        sessionId: response.data.session_id,
        tracks: response.data.tracks,
        mix: response.data.mix
      };
    } catch (error) {
      console.error('Collaboration error:', error.message);
      throw error;
    }
  }

  async getRoyaltyInfo(trackId) {
    try {
      const response = await this.client.get(`/music/royalty/${trackId}`);
      return {
        humanShare: response.data.human_share,
        aiShare: response.data.ai_share,
        platformShare: response.data.platform_share,
        totalRevenue: response.data.total_revenue
      };
    } catch (error) {
      console.error('Royalty info error:', error.message);
      throw error;
    }
  }
}

const client = new ElevenLabsMusicClient('YOUR_ELEVENLABS_API_KEY');
client.generateMusic('A cinematic orchestral piece with rising tension', {
  duration: 60,
  genre: 'cinematic',
  instruments: ['strings', 'brass', 'percussion'],
  bpm: 80
}).then(result => console.log('Task:', result));