169 lines
4.7 KiB
Python
Executable File
169 lines
4.7 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
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import argparse
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from pathlib import Path
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import kaldi_native_fbank as knf
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import numpy as np
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import onnxruntime as ort
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import torch
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import soundfile as sf
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import librosa
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, required=True, help="Path to model.onnx")
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parser.add_argument("--tokens", type=str, required=True, help="Path to tokens.txt")
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parser.add_argument("--wav", type=str, required=True, help="Path to test.wav")
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return parser.parse_args()
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def create_fbank():
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opts = knf.FbankOptions()
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opts.frame_opts.dither = 0
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opts.frame_opts.remove_dc_offset = False
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opts.frame_opts.window_type = "hann"
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opts.mel_opts.low_freq = 0
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opts.mel_opts.num_bins = 80
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opts.mel_opts.is_librosa = True
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fbank = knf.OnlineFbank(opts)
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return fbank
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def compute_features(audio, fbank):
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assert len(audio.shape) == 1, audio.shape
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fbank.accept_waveform(16000, audio)
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ans = []
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processed = 0
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while processed < fbank.num_frames_ready:
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ans.append(np.array(fbank.get_frame(processed)))
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processed += 1
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ans = np.stack(ans)
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return ans
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class OnnxModel:
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def __init__(
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self,
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filename: str,
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):
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session_opts = ort.SessionOptions()
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session_opts.inter_op_num_threads = 1
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session_opts.intra_op_num_threads = 1
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self.session_opts = session_opts
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self.model = ort.InferenceSession(
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filename,
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sess_options=self.session_opts,
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providers=["CPUExecutionProvider"],
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)
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print("==========Input==========")
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for i in self.model.get_inputs():
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print(i)
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print("==========Output==========")
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for i in self.model.get_outputs():
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print(i)
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"""
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==========Input==========
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NodeArg(name='audio_signal', type='tensor(float)', shape=['audio_signal_dynamic_axes_1', 80, 'audio_signal_dynamic_axes_2'])
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NodeArg(name='length', type='tensor(int64)', shape=['length_dynamic_axes_1'])
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==========Output==========
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NodeArg(name='logprobs', type='tensor(float)', shape=['logprobs_dynamic_axes_1', 'logprobs_dynamic_axes_2', 1025])
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"""
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meta = self.model.get_modelmeta().custom_metadata_map
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self.normalize_type = meta["normalize_type"]
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print(meta)
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def __call__(self, x: np.ndarray):
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# x: (T, C)
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x = torch.from_numpy(x)
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x = x.t().unsqueeze(0)
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# x: [1, C, T]
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x_lens = torch.tensor([x.shape[-1]], dtype=torch.int64)
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log_probs = self.model.run(
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[
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self.model.get_outputs()[0].name,
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],
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{
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self.model.get_inputs()[0].name: x.numpy(),
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self.model.get_inputs()[1].name: x_lens.numpy(),
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},
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)[0]
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# [batch_size, T, vocab_size]
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return torch.from_numpy(log_probs)
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def main():
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args = get_args()
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assert Path(args.model).is_file(), args.model
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assert Path(args.tokens).is_file(), args.tokens
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assert Path(args.wav).is_file(), args.wav
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print(vars(args))
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model = OnnxModel(args.model)
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id2token = dict()
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with open(args.tokens, encoding="utf-8") as f:
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for line in f:
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t, idx = line.split()
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id2token[int(idx)] = t
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fbank = create_fbank()
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audio, sample_rate = sf.read(args.wav, dtype="float32", always_2d=True)
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audio = audio[:, 0] # only use the first channel
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if sample_rate != 16000:
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audio = librosa.resample(
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audio,
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orig_sr=sample_rate,
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target_sr=16000,
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)
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sample_rate = 16000
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blank = len(id2token) - 1
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ans = []
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prev = -1
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print(audio.shape)
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features = compute_features(audio, fbank)
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if model.normalize_type != "":
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assert model.normalize_type == "per_feature", model.normalize_type
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features = torch.from_numpy(features)
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mean = features.mean(dim=1, keepdims=True)
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stddev = features.std(dim=1, keepdims=True) + 1e-5
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features = (features - mean) / stddev
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features = features.numpy()
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print("features.shape", features.shape)
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log_probs = model(features)
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print("log_probs.shape", log_probs.shape)
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log_probs = log_probs[0, :, :] # remove batch dim
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ids = torch.argmax(log_probs, dim=1).tolist()
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for k in ids:
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if k != blank and k != prev:
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ans.append(k)
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prev = k
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tokens = [id2token[i] for i in ans]
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underline = "▁"
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# underline = b"\xe2\x96\x81".decode()
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text = "".join(tokens).replace(underline, " ").strip()
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print(args.wav)
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print(text)
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main()
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