Medix-AI/coqui-tts
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coqui-tts
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add batched speaker encoder inference

This commit is contained in:
Edresson 2021-05-27 20:01:00 -03:00
parent 825734a3a9
commit 208bb0f0ee
2 changed files with 7 additions and 6 deletions
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1
TTS/bin/compute_embeddings.py
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@ -2,7 +2,6 @@ import argparse
import glob import glob
import os import os
import numpy as np
import torch import torch
from tqdm import tqdm from tqdm import tqdm

12
TTS/speaker_encoder/models/resnet.py
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@ -174,15 +174,17 @@ class ResNetSpeakerEncoder(nn.Module):
offsets = np.linspace(0, max_len-num_frames, num=num_eval) offsets = np.linspace(0, max_len-num_frames, num=num_eval)
embeddings = [] frames_batch = []
for offset in offsets: for offset in offsets:
offset = int(offset) offset = int(offset)
end_offset = int(offset+num_frames) end_offset = int(offset+num_frames)
frames = x[:, offset:end_offset] frames = x[:, offset:end_offset]
embed = self.forward(frames, l2_norm=True) frames_batch.append(frames)
embeddings.append(embed)
frames_batch = torch.cat(frames_batch, dim=0)
embeddings = self.forward(frames_batch, l2_norm=True)
embeddings = torch.stack(embeddings)
if return_mean: if return_mean:
embeddings = torch.mean(embeddings, dim=0) embeddings = torch.mean(embeddings, dim=0, keepdim=True)
return embeddings return embeddings