Export speaker verification models from NeMo to ONNX (#526)

scripts/nemo/speaker-verification/test-onnx.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+# Copyright      2023-2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script computes speaker similarity score in the range [0-1]
+of two wave files using a speaker embedding model.
+"""
+import argparse
+import wave
+from pathlib import Path
+
+import kaldi_native_fbank as knf
+import numpy as np
+import onnxruntime as ort
+from numpy.linalg import norm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help="Path to the input onnx model. Example value: model.onnx",
+    )
+
+    parser.add_argument(
+        "--file1",
+        type=str,
+        required=True,
+        help="Input wave 1",
+    )
+
+    parser.add_argument(
+        "--file2",
+        type=str,
+        required=True,
+        help="Input wave 2",
+    )
+
+    return parser.parse_args()
+
+
+def read_wavefile(filename, expected_sample_rate: int = 16000) -> np.ndarray:
+    """
+    Args:
+      filename:
+        Path to a wave file, which must be of 16-bit and 16kHz.
+     expected_sample_rate:
+       Expected sample rate of the wave file.
+    Returns:
+      Return a 1-D float32 array containing audio samples. Each sample is in
+      the range [-1, 1].
+    """
+    filename = str(filename)
+    with wave.open(filename) as f:
+        wave_file_sample_rate = f.getframerate()
+        assert wave_file_sample_rate == expected_sample_rate, (
+            wave_file_sample_rate,
+            expected_sample_rate,
+        )
+
+        num_channels = f.getnchannels()
+        assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
+        num_samples = f.getnframes()
+        samples = f.readframes(num_samples)
+        samples_int16 = np.frombuffer(samples, dtype=np.int16)
+        samples_int16 = samples_int16.reshape(-1, num_channels)[:, 0]
+        samples_float32 = samples_int16.astype(np.float32)
+
+        samples_float32 = samples_float32 / 32768
+
+        return samples_float32
+
+
+def compute_features(samples: np.ndarray, model: "OnnxModel") -> np.ndarray:
+    fbank_opts = knf.FbankOptions()
+    fbank_opts.frame_opts.samp_freq = model.sample_rate
+    fbank_opts.frame_opts.frame_length_ms = model.window_size_ms
+    fbank_opts.frame_opts.frame_shift_ms = model.window_stride_ms
+    fbank_opts.frame_opts.dither = 0
+    fbank_opts.frame_opts.remove_dc_offset = False
+    fbank_opts.frame_opts.window_type = model.window_type
+
+    fbank_opts.mel_opts.num_bins = model.feat_dim
+    fbank_opts.mel_opts.low_freq = 0
+    fbank_opts.mel_opts.is_librosa = True
+
+    fbank = knf.OnlineFbank(fbank_opts)
+    fbank.accept_waveform(model.sample_rate, samples)
+    fbank.input_finished()
+
+    features = []
+    for i in range(fbank.num_frames_ready):
+        f = fbank.get_frame(i)
+        features.append(f)
+    features = np.stack(features, axis=0)
+    # at this point, the shape of features is (T, C)
+
+    if model.feature_normalize_type != "":
+        assert model.feature_normalize_type == "per_feature"
+        mean = np.mean(features, axis=0, keepdims=True)
+        std = np.std(features, axis=0, keepdims=True)
+        features = (features - mean) / std
+
+    feature_len = features.shape[0]
+    pad = 16 - feature_len % 16
+
+    if pad > 0:
+        padding = np.zeros((pad, features.shape[1]), dtype=np.float32)
+        features = np.concatenate([features, padding])
+
+    features = np.expand_dims(features, axis=0)
+
+    return features, feature_len
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+        )
+
+        meta = self.model.get_modelmeta().custom_metadata_map
+        self.framework = meta["framework"]
+        self.sample_rate = int(meta["sample_rate"])
+        self.output_dim = int(meta["output_dim"])
+        self.feature_normalize_type = meta["feature_normalize_type"]
+        self.window_size_ms = int(meta["window_size_ms"])
+        self.window_stride_ms = int(meta["window_stride_ms"])
+        self.window_type = meta["window_type"]
+        self.feat_dim = int(meta["feat_dim"])
+        print(meta)
+
+        assert self.framework == "nemo", self.framework
+
+    def __call__(self, x: np.ndarray, x_lens: int) -> np.ndarray:
+        """
+        Args:
+          x:
+            A 2-D float32 tensor of shape (T, C).
+          y:
+            A 1-D float32 tensor containing model output.
+        """
+        x = x.transpose(0, 2, 1)  # (B, T, C) -> (B, C, T)
+        x_lens = np.asarray([x_lens], dtype=np.int64)
+
+        return self.model.run(
+            [
+                self.model.get_outputs()[1].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x,
+                self.model.get_inputs()[1].name: x_lens,
+            },
+        )[0][0]
+
+
+def main():
+    args = get_args()
+    print(args)
+    filename = Path(args.model)
+    file1 = Path(args.file1)
+    file2 = Path(args.file2)
+    assert filename.is_file(), filename
+    assert file1.is_file(), file1
+    assert file2.is_file(), file2
+
+    model = OnnxModel(filename)
+    wave1 = read_wavefile(file1, model.sample_rate)
+    wave2 = read_wavefile(file2, model.sample_rate)
+
+    features1, features1_len = compute_features(wave1, model)
+    features2, features2_len = compute_features(wave2, model)
+
+    output1 = model(features1, features1_len)
+    output2 = model(features2, features2_len)
+
+    similarity = np.dot(output1, output2) / (norm(output1) * norm(output2))
+    print(f"similarity in the range [0-1]: {similarity}")
+
+
+if __name__ == "__main__":
+    main()