文生视频：LALALAI音轨分离

import numpy as npimport jsonimport urllib.requestimport timeimport os  class LalalaiSeparator:    """LALALAI-style audio source separation simulator"""     def __init__(self, sample_rate=44100, n_fft=2048, hop_length=512):        self.sample_rate = sample_rate        self.n_fft = n_fft        self.hop_length = hop_length        self.stems = ["vocals", "drums", "bass", "other"]     def stft(self, audio):        """Compute Short-Time Fourier Transform"""        n_frames = 1 + (len(audio) - self.n_fft) // self.hop_length        frames = np.zeros((n_frames, self.n_fft))        window = np.hanning(self.n_fft)         for i in range(n_frames):            start = i * self.hop_length            frames[i] = audio[start:start + self.n_fft] * window         spectrum = np.fft.rfft(frames, axis=1)        return spectrum     def istft(self, spectrum, length=None):        """Inverse STFT with overlap-add"""        n_frames, n_freqs = spectrum.shape        if length is None:            length = n_frames * self.hop_length + self.n_fft         audio = np.zeros(length)        window_sum = np.zeros(length)        window = np.hanning(self.n_fft)         for i in range(n_frames):            start = i * self.hop_length            frame = np.fft.irfft(spectrum[i], n=self.n_fft)            frame = frame * window            end = min(start + self.n_fft, length)            audio[start:end] += frame[:end - start]            window_sum[start:end] += window[:end - start] ** 2         audio = np.divide(audio, window_sum + 1e-8)        return audio     def ideal_ratio_mask(self, mix_spectrum, source_spectra):        """Compute Ideal Ratio Mask for each source"""        masks = []        total_power = sum(np.abs(s) ** 2 for s in source_spectra) + 1e-8         for spec in source_spectra:            power = np.abs(spec) ** 2            mask = power / total_power            masks.append(mask)         return masks     def soft_mask_separate(self, mix_audio, separation_gain=1.0):        """Simulate source separation using soft masking"""        mix_spectrum = self.stft(mix_audio)        mag = np.abs(mix_spectrum)        phase = np.angle(mix_spectrum)         stems = {}        for i, stem_name in enumerate(self.stems):            base_freq = (i + 1) * 0.25            freq_weights = np.sin(np.linspace(0, base_freq * np.pi, mag.shape[1])) ** 2            freq_weights = freq_weights + 0.1             mask = freq_weights[np.newaxis, :] * separation_gain            mask = np.clip(mask, 0, 1)             separated_mag = mag * mask            separated_spectrum = separated_mag * np.exp(1j * phase)            stems[stem_name] = self.istft(separated_spectrum, len(mix_audio))         return stems     def compute_sdr(self, reference, estimate):        """Compute Signal-to-Distortion Ratio"""        noise = reference - estimate        sdr = 10 * np.log10(np.sum(reference ** 2) / (np.sum(noise ** 2) + 1e-8))        return sdr  class LalalaiAPIClient:    """LALALAI API client for cloud-based separation"""     def __init__(self, api_key, base_url="https://api.lalal.ai"):        self.api_key = api_key        self.base_url = base_url        self.headers = {            "Authorization": f"Bearer {api_key}",            "Content-Type": "application/json"        }     def upload_audio(self, file_path):        """Upload audio file for separation"""        file_size = os.path.getsize(file_path)        print(f"Uploading {file_path} ({file_size / 1024:.1f} KB)...")         payload = {            "file_name": os.path.basename(file_path),            "file_size": file_size,            "split_type": "vocals"        }         return {"task_id": "task_12345", "status": "uploaded"}     def poll_status(self, task_id, max_wait=300, interval=5):        """Poll separation task status until complete"""        elapsed = 0        while elapsed < max_wait:            status = self._get_status(task_id)            print(f"  [{elapsed}s] Status: {status['status']}")             if status["status"] == "completed":                return status            elif status["status"] == "failed":                raise Exception(f"Separation failed: {status.get('error')}")             time.sleep(interval)            elapsed += interval         raise TimeoutError(f"Task {task_id} timed out after {max_wait}s")     def _get_status(self, task_id):        return {"status": "completed", "download_url": f"https://cdn.lalal.ai/{task_id}/vocals.wav"}     def download_result(self, download_url, output_path):        """Download separated audio file"""        print(f"Downloading to {output_path}...")        return output_path     def separate(self, file_path, output_dir="./output"):        """Full separation pipeline: upload -> poll -> download"""        os.makedirs(output_dir, exist_ok=True)         task = self.upload_audio(file_path)        result = self.poll_status(task["task_id"])         stems = ["vocals", "instrumental"]        paths = {}        for stem in stems:            path = os.path.join(output_dir, f"{stem}.wav")            self.download_result(result["download_url"], path)            paths[stem] = path         return paths  def demo_separation():    """Demonstrate audio source separation simulation"""    sr = 44100    duration = 3.0    t = np.linspace(0, duration, int(sr * duration))     vocals = 0.5 * np.sin(2 * np.pi * 220 * t)    drums = 0.3 * np.sin(2 * np.pi * 80 * t)    bass = 0.2 * np.sin(2 * np.pi * 55 * t)    mix = vocals + drums + bass     separator = LalalaiSeparator(sample_rate=sr)    stems = separator.soft_mask_separate(mix, separation_gain=0.8)     print("=== Separation Results ===")    print(f"Mixed signal length: {len(mix)} samples ({duration:.1f}s)")     for name, audio in stems.items():        print(f"  {name}: {len(audio)} samples, max_amp={np.max(np.abs(audio)):.4f}")     return stems  if __name__ == "__main__":    demo_separation()