| """Compare two voices with TitaNet.mlpackage (macOS, Python 3.9+). |
| |
| pip install coremltools kaldi-native-fbank soundfile numpy |
| python example.py a.wav b.wav |
| |
| The WAV files must be mono 16 kHz. Prints the cosine similarity of the two |
| speaker embeddings. |
| """ |
|
|
| import sys |
|
|
| import coremltools as ct |
| import kaldi_native_fbank as knf |
| import numpy as np |
| import soundfile as sf |
|
|
|
|
| def features(samples: np.ndarray) -> np.ndarray: |
| """The features sherpa-onnx feeds TitaNet-L, shaped (1, 80, T).""" |
| o = knf.FbankOptions() |
| o.frame_opts.samp_freq = 16000 |
| o.frame_opts.frame_length_ms = 25 |
| o.frame_opts.frame_shift_ms = 10 |
| o.frame_opts.snip_edges = True |
| o.frame_opts.dither = 0 |
| o.frame_opts.remove_dc_offset = False |
| o.frame_opts.preemph_coeff = 0.97 |
| o.frame_opts.window_type = "hann" |
| o.mel_opts.num_bins = 80 |
| o.mel_opts.low_freq = 0 |
| o.mel_opts.high_freq = -400 |
| o.mel_opts.is_librosa = True |
| fbank = knf.OnlineFbank(o) |
| fbank.accept_waveform(16000, samples.tolist()) |
| fbank.input_finished() |
| f = np.array([fbank.get_frame(i) for i in range(fbank.num_frames_ready)], dtype=np.float32) |
| f = (f - f.mean(0)) / (f.std(0) + 1e-5) |
| return f.T[None].copy() |
|
|
|
|
| def embed(model: ct.models.MLModel, path: str) -> np.ndarray: |
| samples, rate = sf.read(path, dtype="float32") |
| assert rate == 16000 and samples.ndim == 1, "expected mono 16 kHz audio" |
| return model.predict({"features": features(samples)})["embedding"][0] |
|
|
|
|
| if __name__ == "__main__": |
| model = ct.models.MLModel("TitaNet.mlpackage", compute_units=ct.ComputeUnit.CPU_AND_GPU) |
| a, b = embed(model, sys.argv[1]), embed(model, sys.argv[2]) |
| print(f"cosine similarity: {a @ b / np.linalg.norm(a) / np.linalg.norm(b):.3f}") |
|
|
