coqui-tts/notebooks/GE2E-CorentinJ-ExtractSpeak...

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This is a noteboook used to generate the speaker embeddings with the CorentinJ GE2E model trained with Angular Prototypical loss for multi-speaker training.

Before running this script please DON'T FORGET:

  • to set the right paths in the cell below.

Repositories:

In [2]:
import os
import importlib
import random
import librosa
import torch

import numpy as np
from TTS.utils.io import load_config
from tqdm import tqdm
from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping

# you may need to change this depending on your system
os.environ['CUDA_VISIBLE_DEVICES']='0'
In [3]:
# Clone encoder 
!git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git
os.chdir('Real-Time-Voice-Cloning/')
Cloning into 'Real-Time-Voice-Cloning'...
remote: Enumerating objects: 5, done.
remote: Counting objects: 100% (5/5), done.
remote: Compressing objects: 100% (5/5), done.
remote: Total 2508 (delta 0), reused 3 (delta 0), pack-reused 2503
Receiving objects: 100% (2508/2508), 360.78 MiB | 17.84 MiB/s, done.
Resolving deltas: 100% (1387/1387), done.
Checking connectivity... done.
In [4]:
#Install voxceleb_trainer Requeriments
!python -m pip install umap-learn visdom webrtcvad librosa>=0.5.1 matplotlib>=2.0.2 numpy>=1.14.0  scipy>=1.0.0  tqdm sounddevice Unidecode inflect multiprocess numba
In [5]:
#Download encoder Checkpoint
!wget https://github.com/Edresson/Real-Time-Voice-Cloning/releases/download/checkpoints/pretrained.zip
!unzip pretrained.zip
--2020-08-05 06:51:05--  https://github.com/Edresson/Real-Time-Voice-Cloning/releases/download/checkpoints/pretrained.zip
Resolving github.com (github.com)... 18.231.5.6
Connecting to github.com (github.com)|18.231.5.6|:443... connected.
HTTP request sent, awaiting response... 301 Moved Permanently
Location: https://github.com/Edresson/GE2E-Speaker-Encoder/releases/download/checkpoints/pretrained.zip [following]
--2020-08-05 06:51:05--  https://github.com/Edresson/GE2E-Speaker-Encoder/releases/download/checkpoints/pretrained.zip
Reusing existing connection to github.com:443.
HTTP request sent, awaiting response... 302 Found
Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/263893598/f7f31d80-96df-11ea-8345-261fc35f9849?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200805%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200805T101614Z&X-Amz-Expires=300&X-Amz-Signature=df7724c28668ebd5dfbcc6a9b51f6afb78193c30119f3a1c3eef678188aabd1e&X-Amz-SignedHeaders=host&actor_id=0&repo_id=263893598&response-content-disposition=attachment%3B%20filename%3Dpretrained.zip&response-content-type=application%2Foctet-stream [following]
--2020-08-05 06:51:05--  https://github-production-release-asset-2e65be.s3.amazonaws.com/263893598/f7f31d80-96df-11ea-8345-261fc35f9849?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200805%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200805T101614Z&X-Amz-Expires=300&X-Amz-Signature=df7724c28668ebd5dfbcc6a9b51f6afb78193c30119f3a1c3eef678188aabd1e&X-Amz-SignedHeaders=host&actor_id=0&repo_id=263893598&response-content-disposition=attachment%3B%20filename%3Dpretrained.zip&response-content-type=application%2Foctet-stream
Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.18.24
Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.18.24|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 383640573 (366M) [application/octet-stream]
Saving to: pretrained.zip

pretrained.zip      100%[===================>] 365,87M  6,62MB/s    in 56s     

2020-08-05 06:52:03 (6,48 MB/s) - pretrained.zip saved [383640573/383640573]

Archive:  pretrained.zip
   creating: encoder/saved_models/
  inflating: encoder/saved_models/pretrained.pt  
   creating: synthesizer/saved_models/
   creating: synthesizer/saved_models/logs-pretrained/
   creating: synthesizer/saved_models/logs-pretrained/taco_pretrained/
 extracting: synthesizer/saved_models/logs-pretrained/taco_pretrained/checkpoint  
  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.data-00000-of-00001  
  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.index  
  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.meta  
   creating: vocoder/saved_models/
   creating: vocoder/saved_models/pretrained/
  inflating: vocoder/saved_models/pretrained/pretrained.pt  
In [6]:
from encoder import inference as encoder
from encoder.params_model import model_embedding_size as speaker_embedding_size
from pathlib import Path
In [7]:
print("Preparing the encoder, the synthesizer and the vocoder...")
encoder.load_model(Path('encoder/saved_models/pretrained.pt'))
print("Testing your configuration with small inputs.")
# Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's
# sampling rate, which may differ.
# If you're unfamiliar with digital audio, know that it is encoded as an array of floats 
# (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.
# The sampling rate is the number of values (samples) recorded per second, it is set to
# 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond 
# to an audio of 1 second.
print("\tTesting the encoder...")

wav = np.zeros(encoder.sampling_rate)    
embed = encoder.embed_utterance(wav)
print(embed.shape)

# Embeddings are L2-normalized (this isn't important here, but if you want to make your own 
# embeddings it will be).
#embed /= np.linalg.norm(embed) # for random embedding
Preparing the encoder, the synthesizer and the vocoder...
Loaded encoder "pretrained.pt" trained to step 1564501
Testing your configuration with small inputs.
	Testing the encoder...
(256,)
In [15]:
SAVE_PATH = '../'
In [12]:
# Set constants
DATASETS_NAME = ['vctk'] # list the datasets
DATASETS_PATH = ['../../../../../datasets/VCTK-Corpus-removed-silence/']
DATASETS_METAFILE = ['']
USE_CUDA = True
In [18]:
#Preprocess dataset
meta_data = []
for i in range(len(DATASETS_NAME)):
    preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')
    preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())
    meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])
      
meta_data= list(meta_data)

meta_data = meta_data
embeddings_dict = {}
len_meta_data= len(meta_data)
for i in tqdm(range(len_meta_data)):
    _, wave_file_path, speaker_id = meta_data[i]
    wav_file_name = os.path.basename(wave_file_path)
    # Extract Embedding
    preprocessed_wav = encoder.preprocess_wav(wave_file_path)
    file_embedding = encoder.embed_utterance(preprocessed_wav)
    embeddings_dict[wav_file_name] = [file_embedding.reshape(-1).tolist(), speaker_id]
    del file_embedding
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In [19]:
# create and export speakers.json  and aplly a L2_norm in embedding
speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0]} for i, sample in enumerate(embeddings_dict.keys())}
save_speaker_mapping(SAVE_PATH, speaker_mapping)
In [20]:
#test load integrity
speaker_mapping_load = load_speaker_mapping(SAVE_PATH)
assert speaker_mapping == speaker_mapping_load
print("The file speakers.json has been exported to ",SAVE_PATH, ' with ', len(embeddings_dict.keys()), ' samples')
The file speakers.json has been exported to  ../  with  44063  samples
In [ ]:
 
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