test.py
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#!/usr/bin/env python3
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
from typing import Tuple
import kaldi_native_fbank as knf
import numpy as np
import onnxruntime as ort
import soundfile as sf
"""
NodeArg(name='feats', type='tensor(float)', shape=[1, 'T', 40])
-----
NodeArg(name='logits', type='tensor(float)', shape=['Addlogits_dim_0', 1, 7535])
"""
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,
providers=["CPUExecutionProvider"],
)
self.show()
def show(self):
for i in self.model.get_inputs():
print(i)
print("-----")
for i in self.model.get_outputs():
print(i)
def __call__(self, x):
"""
Args:
x: a float32 tensor of shape (N, T, C)
"""
logits = self.model.run(
[
self.model.get_outputs()[0].name,
],
{
self.model.get_inputs()[0].name: x,
},
)[0]
return logits
def load_audio(filename: str) -> Tuple[np.ndarray, int]:
data, sample_rate = sf.read(
filename,
always_2d=True,
dtype="float32",
)
data = data[:, 0] # use only the first channel
samples = np.ascontiguousarray(data)
return samples, sample_rate
def get_features(test_wav_filename):
samples, sample_rate = load_audio(test_wav_filename)
if sample_rate != 16000:
import librosa
samples = librosa.resample(samples, orig_sr=sample_rate, target_sr=16000)
sample_rate = 16000
samples *= 372768
opts = knf.MfccOptions()
# See https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/mfcc_hires.conf
opts.frame_opts.dither = 0
opts.num_ceps = 40
opts.use_energy = False
opts.mel_opts.num_bins = 40
opts.mel_opts.low_freq = 40
opts.mel_opts.high_freq = -200
mfcc = knf.OnlineMfcc(opts)
mfcc.accept_waveform(16000, samples)
frames = []
for i in range(mfcc.num_frames_ready):
frames.append(mfcc.get_frame(i))
frames = np.stack(frames, axis=0)
return frames
def cmvn(features):
# See https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/wenet_representation/conf/train_d2v2_ark_conformer.yaml#L70
# https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/wenet_representation/wenet/dataset/dataset.py#L184
# https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/wenet_representation/wenet/dataset/processor.py#L278
mean = features.mean(axis=0, keepdims=True)
std = features.std(axis=0, keepdims=True)
return (features - mean) / (std + 1e-5)
def main():
# Please download the test data from
# https://hf-mirror.com/csukuangfj/sherpa-onnx-paraformer-zh-small-2024-03-09/tree/main/test_wavs
test_wav_filename = "./3-sichuan.wav"
test_wav_filename = "./4-tianjin.wav"
test_wav_filename = "./5-henan.wav"
features = get_features(test_wav_filename)
features = cmvn(features)
features = np.expand_dims(features, axis=0) # (T, C) -> (N, T, C)
model_filename = "./model.int8.onnx"
model = OnnxModel(model_filename)
logits = model(features)
logits = logits.squeeze(axis=1) # remove batch axis
ids = logits.argmax(axis=-1)
id2token = dict()
with open("./tokens.txt", encoding="utf-8") as f:
for line in f:
t, idx = line.split()
id2token[int(idx)] = t
tokens = []
blank = 0
prev = -1
for k in ids:
if k != blank and k != prev:
tokens.append(k)
prev = k
tokens = [id2token[i] for i in tokens]
text = "".join(tokens)
print(text)
if __name__ == "__main__":
main()