From ff8e63567af8f1620550caddf18e78ebf4a7d671 Mon Sep 17 00:00:00 2001 From: "lyuxiang.lx" Date: Wed, 18 Sep 2024 17:41:15 +0800 Subject: [PATCH] use thread pool in tools --- tools/extract_embedding.py | 77 ++++++++++++++--------------------- tools/extract_speech_token.py | 54 +++++++++++++----------- 2 files changed, 61 insertions(+), 70 deletions(-) diff --git a/tools/extract_embedding.py b/tools/extract_embedding.py index 982a841..cb198cb 100755 --- a/tools/extract_embedding.py +++ b/tools/extract_embedding.py @@ -13,74 +13,39 @@ # See the License for the specific language governing permissions and # limitations under the License. import argparse -import os -from concurrent.futures import ThreadPoolExecutor - +from concurrent.futures import ThreadPoolExecutor, as_completed import onnxruntime import torch import torchaudio import torchaudio.compliance.kaldi as kaldi from tqdm import tqdm -from itertools import repeat -def extract_embedding(utt: str, wav_file: str, ort_session: onnxruntime.InferenceSession): - audio, sample_rate = torchaudio.load(wav_file) +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) + 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) + return utt, embedding def main(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] - - assert os.path.exists(args.onnx_path), "onnx_path not exists" - - 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 - ) - - all_utt = utt2wav.keys() - - with ThreadPoolExecutor(max_workers=args.num_thread) as executor: - results = list( - tqdm( - executor.map(extract_embedding, all_utt, [utt2wav[utt] for utt in all_utt], repeat(ort_session)), - total=len(utt2wav), - desc="Process data: " - ) - ) - + all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()] utt2embedding, spk2embedding = {}, {} - for utt, embedding in results: + 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)) @@ -91,4 +56,22 @@ if __name__ == "__main__": 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) diff --git a/tools/extract_speech_token.py b/tools/extract_speech_token.py index fac0b0b..2829624 100755 --- a/tools/extract_speech_token.py +++ b/tools/extract_speech_token.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import argparse +from concurrent.futures import ThreadPoolExecutor, as_completed import logging import torch from tqdm import tqdm @@ -22,7 +23,36 @@ import torchaudio import whisper +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) + if audio.shape[1] / 16000 > 30: + logging.warning('do not support extract speech token for audio longer than 30s') + speech_token = [] + else: + feat = whisper.log_mel_spectrogram(audio, n_mels=128) + speech_token = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.detach().cpu().numpy(), + ort_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() + return utt, speech_token + + def main(args): + all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()] + utt2speech_token = {} + for future in tqdm(as_completed(all_task)): + utt, speech_token = future.result() + utt2speech_token[utt] = speech_token + torch.save(utt2speech_token, '{}/utt2speech_token.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 = {} with open('{}/wav.scp'.format(args.dir)) as f: for l in f: @@ -34,28 +64,6 @@ def main(args): option.intra_op_num_threads = 1 providers = ["CUDAExecutionProvider"] ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers) + executor = ThreadPoolExecutor(max_workers=args.num_thread) - utt2speech_token = {} - for utt in tqdm(utt2wav.keys()): - audio, sample_rate = torchaudio.load(utt2wav[utt]) - if sample_rate != 16000: - audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio) - if audio.shape[1] / 16000 > 30: - logging.warning('do not support extract speech token for audio longer than 30s') - speech_token = [] - else: - feat = whisper.log_mel_spectrogram(audio, n_mels=128) - speech_token = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.detach().cpu().numpy(), - ort_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() - utt2speech_token[utt] = speech_token - torch.save(utt2speech_token, '{}/utt2speech_token.pt'.format(args.dir)) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('--dir', - type=str) - parser.add_argument('--onnx_path', - type=str) - args = parser.parse_args() main(args)