fix link to script

spk_embeddings.py

@@ -1,54 +0,0 @@
-'''
- * Software Name : spk_embeddings.py
- * SPDX-FileCopyrightText: Copyright (c) Orange SA
- * SPDX-License-Identifier: CC-BY-SA-3.0
- *
- * This software is distributed under the Creative Commons Attribution Share Alike 3.0 Unported,
- * see the "LICENSE.txt" file for more details or https://huggingface.co/Orange/w-pro/blob/main/LICENSE.txt
-'''
-
-import torch, torchaudio
-import torch.nn as nn
-from transformers.models.wavlm.modeling_wavlm import WavLMPreTrainedModel, WavLMModel
-
-class TopLayers(nn.Module):
-    def __init__(self, embd_size = 250, top_interm_size = 512):
-        super(TopLayers, self).__init__()
-        self.affine1 = nn.Conv1d(in_channels=2048, out_channels=top_interm_size, kernel_size=1)
-        self.batchnorm1 = nn.BatchNorm1d(num_features=top_interm_size, affine=False, eps=1e-03)
-        self.affine2 = nn.Conv1d(in_channels=top_interm_size, out_channels=embd_size, kernel_size=1)
-        self.batchnorm2 = nn.BatchNorm1d(num_features=embd_size, affine=False, eps=1e-03)
-        self.activation = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        out = self.batchnorm1(self.activation(self.affine1(x)))
-        out = self.batchnorm2(self.activation(self.affine2(out)))
-        return nn.functional.normalize(out[:,:,0])
-    
-class EmbeddingsModel(WavLMPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.wavlm = WavLMModel(config)
-        self.top_layers = TopLayers(config.embd_size, config.top_interm_size)
-        
-    def forward(self, input_values):
-        # MVN normalization
-        x_norm = (input_values - input_values.mean(dim=1).unsqueeze(1)) / (input_values.std(dim=1).unsqueeze(1))
-        # wavlm fwd
-        base_out = self.wavlm(input_values=x_norm, output_hidden_states=False).last_hidden_state
-        # stats pooling
-        v = base_out.var(dim=1).clamp(min=1e-10)
-        x_stats = torch.cat((base_out.mean(dim=1),v.pow(0.5)),dim=1).unsqueeze(dim=2)
-        # top layers fwd 
-        return self.top_layers(x_stats)
-    
-
-def compute_embedding(fnm, model, max_size=320000):
-    sig, sr = torchaudio.load(fnm)
-    assert sr == 16000, "please convert your audio file to a sampling rate of 16 kHz"
-    sig = sig.mean(dim=0)
-    if sig.shape[0] > max_size:
-        print(f"truncating long signal {fnm}")
-        sig = sig[:max_size]
-    embd = model(sig.unsqueeze(dim=0))
-    return embd.clone().detach()