crepe-predictor

A dependency-light reimplementation of CREPE pitch estimation, exported to ONNX for inference without PyTorch.

• crepe_predictor.py — the runtime package: framing, Viterbi-decoded pitch prediction from an ONNX session, and Kaldi-compatible (NCCF, pitch) postprocessing, wrapped in a CrepePredictor class.
• export_torchcrepe_to_onnx.py — script to (re-)generate the ONNX checkpoints.

Installation

pip install crepe-predictor

Inference only depends on numpy, onnxruntime, and scipy. Exporting checkpoints additionally requires torch and onnxscript, listed as script dependencies at the top of export_torchcrepe_to_onnx.py.

API

Everything is exposed through crepe_predictor.CrepePredictor.

CrepePredictor(capacity="full", *, checkpoint=None, onnx_providers=None)

Resolves a checkpoint and opens an ONNX Runtime session for it.

• capacity: "tiny", "small", "medium", "large", or "full" — model size, trading accuracy for speed.
• checkpoint: path to a local .onnx file. If omitted, the checkpoint matching capacity is downloaded and cached under $CREPE_CACHE_DIR, $XDG_CACHE_HOME, or ~/.cache/crepe_predictor/checkpoints.
• onnx_providers: ONNX Runtime execution providers, e.g. ["CUDAExecutionProvider", "CPUExecutionProvider"]. Defaults to ["CPUExecutionProvider"].

predict(audio, *, viterbi=True, center=True, frame_shift=0.01, frame_length=0.025) -> np.ndarray

Estimates pitch from 16 kHz mono audio, returning an (n_frames, 2) array of (POV, pitch): probability of voicing in [0, 1], and pitch in Hz.

• viterbi: decode pitch bins along a Viterbi path enforcing pitch continuity, instead of a per-frame argmax.
• center: pad audio so each frame is centered on its timestamp.
• frame_shift, frame_length: frame spacing and length in seconds, used to resample the output to the frame count they imply.

predict_kaldi(audio, *, viterbi=True, center=True, frame_shift=0.01, frame_length=0.025) -> np.ndarray

Same arguments as predict, but returns (n_frames, 2) of (NCCF, pitch), compatible with Kaldi's process-pitch: unvoiced frames are detected with a voicing HMM, pitch is interpolated over them, and POV is converted to an NCCF value. Raises ValueError if no frame is voiced.

Usage

Estimate pitch from a synthetic tone:

import numpy as np
from crepe_predictor import CrepePredictor

predictor = CrepePredictor("full")  # "tiny", "small", "medium", "large", or "full"

t = np.arange(16000) / 16000  # 1 second at 16 kHz
audio = np.sin(2 * np.pi * 220 * t).astype(np.float32)  # a 220 Hz tone

pov, pitch = predictor.predict(audio).T
print(pitch[pov > 0.5])  # pitch in Hz for confidently voiced frames

Process a recording and produce Kaldi-compatible pitch features, running on GPU when available:

from scipy.io import wavfile
from crepe_predictor import CrepePredictor

predictor = CrepePredictor(
    "full",
    onnx_providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
)

sample_rate, audio = wavfile.read("speech.wav")
assert sample_rate == 16000
audio = audio.astype("float32") / 32768.0  # int16 PCM -> float32 in [-1, 1]

nccf, pitch = predictor.predict_kaldi(audio).T