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| # Copyright (c) microsoft | |
| # 2022 Chengdong Liang (liangchengdong@mail.nwpu.edu.cn) | |
| # | |
| # 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 gradio as gr | |
| import torchaudio | |
| import torchaudio.compliance.kaldi as kaldi | |
| import torch | |
| import onnxruntime as ort | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| STYLE = """ | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous"> | |
| """ | |
| OUTPUT_OK = (STYLE + """ | |
| <div class="container"> | |
| <div class="row"><h1 style="text-align: center">The speakers are</h1></div> | |
| <div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div> | |
| <div class="row"><h1 style="text-align: center">similar</h1></div> | |
| <div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div> | |
| <div class="row"><small style="text-align: center">(You must get at least 50% to be considered the same person)</small><div class="row"> | |
| </div> | |
| """) | |
| OUTPUT_FAIL = (STYLE + """ | |
| <div class="container"> | |
| <div class="row"><h1 style="text-align: center">The speakers are</h1></div> | |
| <div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div> | |
| <div class="row"><h1 style="text-align: center">similar</h1></div> | |
| <div class="row"><h1 class="text-danger" style="text-align: center">Warning! stranger!</h1></div> | |
| <div class="row"><small style="text-align: center">(You must get at least 50% to be considered the same person)</small><div class="row"> | |
| </div> | |
| """) | |
| OUTPUT_ERROR = (STYLE + """ | |
| <div class="container"> | |
| <div class="row"><h1 style="text-align: center">Input Error</h1></div> | |
| <div class="row"><h1 class="text-danger" style="text-align: center">{}!</h1></div> | |
| </div> | |
| """) | |
| def compute_fbank(wav_path, | |
| num_bel_bins=80, | |
| frame_length=25, | |
| frame_shift=10, | |
| dither=0.0, | |
| resample_rate=16000): | |
| """ Extract fbank, simlilar to the one in wespeaker.dataset.processor, | |
| While integrating the wave reading and CMN. | |
| """ | |
| waveform, sample_rate = torchaudio.load(wav_path) | |
| # resample | |
| if sample_rate != resample_rate: | |
| waveform = torchaudio.transforms.Resample( | |
| orig_freq=sample_rate, new_freq=resample_rate)(waveform) | |
| waveform = waveform * (1 << 15) | |
| mat = kaldi.fbank(waveform, | |
| num_mel_bins=num_bel_bins, | |
| frame_length=frame_length, | |
| frame_shift=frame_shift, | |
| dither=dither, | |
| sample_frequency=sample_rate, | |
| window_type='hamming', | |
| use_energy=False) | |
| # CMN, without CVN | |
| mat = mat - torch.mean(mat, dim=0) | |
| return mat | |
| class OnnxModel(object): | |
| def __init__(self, model_path): | |
| so = ort.SessionOptions() | |
| so.inter_op_num_threads = 1 | |
| so.intra_op_num_threads = 1 | |
| self.session = ort.InferenceSession(model_path, sess_options=so) | |
| def extract_embedding(self, wav_path): | |
| feats = compute_fbank(wav_path) | |
| feats = feats.unsqueeze(0).numpy() | |
| embeddings = self.session.run(output_names=['embs'], | |
| input_feed={'feats': feats}) | |
| return embeddings[0] | |
| def speaker_verification(audio_path1, audio_path2, lang='CN'): | |
| if audio_path1 == None or audio_path2 == None: | |
| output = OUTPUT_ERROR.format('Please enter two audios') | |
| return output | |
| if lang == 'EN': | |
| model = OnnxModel('pre_model/voxceleb_resnet34_LM.onnx') | |
| elif lang == 'CN': | |
| model = OnnxModel('pre_model/cnceleb_resnet34_LM.onnx') | |
| else: | |
| output = OUTPUT_ERROR.format('Please select a language') | |
| return output | |
| emb1 = model.extract_embedding(audio_path1) | |
| emb2 = model.extract_embedding(audio_path2) | |
| cos_score = cosine_similarity(emb1.reshape(1, -1), emb2.reshape(1, | |
| -1))[0][0] | |
| if cos_score >= 0.5: | |
| output = OUTPUT_OK.format(cos_score * 100) | |
| else: | |
| output = OUTPUT_FAIL.format(cos_score * 100) | |
| return output | |
| # input | |
| inputs = [ | |
| gr.inputs.Audio(source="microphone", | |
| type="filepath", | |
| optional=True, | |
| label='Speaker#1'), | |
| gr.inputs.Audio(source="microphone", | |
| type="filepath", | |
| optional=True, | |
| label='Speaker#2'), | |
| gr.Radio(['CN', 'EN'], label='Language'), | |
| ] | |
| output = gr.outputs.HTML(label="") | |
| # description | |
| description = ("WeSpeaker Demo ! Try it with your own voice !") | |
| article = ( | |
| "<p style='text-align: center'>" | |
| "<a href='https://github.com/wenet-e2e/wespeaker' target='_blank'>Github: Learn more about WeSpeaker</a>" | |
| "</p>") | |
| examples = [ | |
| ['examples/BAC009S0764W0228.wav', 'examples/BAC009S0764W0328.wav', 'CN'], | |
| ['examples/BAC009S0913W0133.wav', 'examples/BAC009S0764W0228.wav', 'CN'], | |
| ['examples/00001_spk1.wav', 'examples/00003_spk2.wav', 'EN'], | |
| ['examples/00010_spk2.wav', 'examples/00024_spk1.wav', 'EN'], | |
| ['examples/00001_spk1.wav', 'examples/00024_spk1.wav', 'EN'], | |
| ['examples/00010_spk2.wav', 'examples/00003_spk2.wav', 'EN'], | |
| ] | |
| interface = gr.Interface( | |
| fn=speaker_verification, | |
| inputs=inputs, | |
| outputs=output, | |
| title="Speaker verification in WeSpeaker : 基于 WeSpeaker 的说话人确认", | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| theme="huggingface", | |
| ) | |
| interface.launch(enable_queue=True) | |