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Running
on
Zero
| #!/usr/bin/env python3 | |
| # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import argparse | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| import onnxruntime | |
| import torch | |
| import torchaudio | |
| import torchaudio.compliance.kaldi as kaldi | |
| from tqdm import tqdm | |
| def single_job(utt): | |
| audio, sample_rate = torchaudio.load(utt2wav[utt]) | |
| if sample_rate != 16000: | |
| audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio) | |
| feat = kaldi.fbank(audio, | |
| num_mel_bins=80, | |
| dither=0, | |
| sample_frequency=16000) | |
| feat = feat - feat.mean(dim=0, keepdim=True) | |
| embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() | |
| return utt, embedding | |
| def main(args): | |
| all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()] | |
| utt2embedding, spk2embedding = {}, {} | |
| for future in tqdm(as_completed(all_task)): | |
| utt, embedding = future.result() | |
| utt2embedding[utt] = embedding | |
| spk = utt2spk[utt] | |
| if spk not in spk2embedding: | |
| spk2embedding[spk] = [] | |
| spk2embedding[spk].append(embedding) | |
| for k, v in spk2embedding.items(): | |
| spk2embedding[k] = torch.tensor(v).mean(dim=0).tolist() | |
| torch.save(utt2embedding, "{}/utt2embedding.pt".format(args.dir)) | |
| torch.save(spk2embedding, "{}/spk2embedding.pt".format(args.dir)) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--dir", type=str) | |
| parser.add_argument("--onnx_path", type=str) | |
| parser.add_argument("--num_thread", type=int, default=8) | |
| args = parser.parse_args() | |
| utt2wav, utt2spk = {}, {} | |
| with open('{}/wav.scp'.format(args.dir)) as f: | |
| for l in f: | |
| l = l.replace('\n', '').split() | |
| utt2wav[l[0]] = l[1] | |
| with open('{}/utt2spk'.format(args.dir)) as f: | |
| for l in f: | |
| l = l.replace('\n', '').split() | |
| utt2spk[l[0]] = l[1] | |
| option = onnxruntime.SessionOptions() | |
| option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL | |
| option.intra_op_num_threads = 1 | |
| providers = ["CPUExecutionProvider"] | |
| ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers) | |
| executor = ThreadPoolExecutor(max_workers=args.num_thread) | |
| main(args) | |