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| """PitchDetector class for real-time pitch detection using FCPE.""" | |
| import numpy as np | |
| import torch | |
| from torchfcpe import spawn_bundled_infer_model | |
| from improvisation_lab.config import PitchDetectorConfig | |
| class PitchDetector: | |
| """Class for real-time pitch detection using FCPE.""" | |
| def __init__(self, config: PitchDetectorConfig): | |
| """Initialize pitch detector. | |
| Args: | |
| config: Configuration settings for pitch detection. | |
| """ | |
| self.sample_rate = config.sample_rate | |
| self.hop_length = config.hop_length | |
| self.decoder_mode = config.decoder_mode | |
| self.threshold = config.threshold | |
| self.f0_min = config.f0_min | |
| self.f0_max = config.f0_max | |
| self.interp_uv = config.interp_uv | |
| self.model = spawn_bundled_infer_model(device=config.device) | |
| def detect_pitch(self, audio_frame: np.ndarray) -> float: | |
| """Detect pitch from audio frame. | |
| Args: | |
| audio_frame: Numpy array of audio samples | |
| Returns: | |
| Frequency in Hz | |
| """ | |
| audio_length = len(audio_frame) | |
| f0_target_length = (audio_length // self.hop_length) + 1 | |
| # Convert to torch tensor and reshape to match expected dimensions | |
| # Add batch and channel dimensions | |
| audio_tensor = torch.from_numpy(audio_frame).float() | |
| audio_tensor = audio_tensor.unsqueeze(0).unsqueeze(-1) | |
| pitch = self.model.infer( | |
| audio_tensor, | |
| sr=self.sample_rate, | |
| decoder_mode=self.decoder_mode, | |
| threshold=self.threshold, | |
| f0_min=self.f0_min, | |
| f0_max=self.f0_max, | |
| interp_uv=self.interp_uv, | |
| output_interp_target_length=f0_target_length, | |
| ) | |
| # Extract the middle frequency value from the pitch tensor | |
| # Taking the middle value helps avoid potential inaccuracies at the edges | |
| # of the audio frame, providing a more stable frequency estimate. | |
| middle_index = pitch.size(1) // 2 | |
| frequency = pitch[0, middle_index, 0].item() | |
| return frequency | |