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app.py

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+import os
+import torch
+import librosa
+import gradio as gr
+from scipy.io.wavfile import write
+from transformers import WavLMModel
+
+import utils
+from models import SynthesizerTrn
+from mel_processing import mel_spectrogram_torch
+from speaker_encoder.voice_encoder import SpeakerEncoder
+
+'''
+def get_wavlm():
+    os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')
+    shutil.move('WavLM-Large.pt', 'wavlm')
+'''
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("Loading FreeVC...")
+hps = utils.get_hparams_from_file("configs/freevc.json")
+freevc = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc.eval()
+_ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None)
+smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')
+
+print("Loading FreeVC(24k)...")
+hps = utils.get_hparams_from_file("configs/freevc-24.json")
+freevc_24 = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc_24.eval()
+_ = utils.load_checkpoint("checkpoints/freevc-24.pth", freevc_24, None)
+
+print("Loading FreeVC-s...")
+hps = utils.get_hparams_from_file("configs/freevc-s.json")
+freevc_s = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc_s.eval()
+_ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None)
+
+print("Loading WavLM for content...")
+cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
+ 
+def convert(model, src, tgt):
+    with torch.no_grad():
+        # tgt
+        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
+        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
+        if model == "FreeVC" or model == "FreeVC (24kHz)":
+            g_tgt = smodel.embed_utterance(wav_tgt)
+            g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
+        else:
+            wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
+            mel_tgt = mel_spectrogram_torch(
+                wav_tgt, 
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax
+            )
+        # src
+        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
+        wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
+        c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
+        # infer
+        if model == "FreeVC":
+            audio = freevc.infer(c, g=g_tgt)
+        elif model == "FreeVC-s":
+            audio = freevc_s.infer(c, mel=mel_tgt)
+        else:
+            audio = freevc_24.infer(c, g=g_tgt)
+        audio = audio[0][0].data.cpu().float().numpy()
+        if model == "FreeVC" or model == "FreeVC-s":
+            write("out.wav", hps.data.sampling_rate, audio)
+        else:
+            write("out.wav", 24000, audio)
+    out = "out.wav"
+    return out
+    
+model = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC",type="value", label="Model") 
+audio1 = gr.Audio(label="Source Audio", type='filepath')
+audio2 = gr.Audio(label="Reference Audio", type='filepath')
+inputs = [model, audio1, audio2]
+outputs =  gr.Audio(label="Output Audio", type='filepath')
+
+title = "FreeVC"
+description = "Gradio Demo for FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion. To use it, simply upload your audio, or click the example to load. Read more at the links below. Note: It seems that the WavLM checkpoint in HuggingFace is a little different from the one used to train FreeVC, which may degrade the performance a bit. In addition, speaker similarity can be largely affected if there are too much silence in the reference audio, so please <strong>trim</strong> it before submitting."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2210.15418' target='_blank'>Paper</a> | <a href='https://github.com/OlaWod/FreeVC' target='_blank'>Github Repo</a></p>"
+
+examples=[["FreeVC", 'p225_001.wav', 'p226_002.wav'], ["FreeVC-s", 'p226_002.wav', 'p225_001.wav'], ["FreeVC (24kHz)", 'p225_001.wav', 'p226_002.wav']]
+
+gr.Interface(convert, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()

checkpoints/freevc-24.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b39a86fefbc9ec6e30be8d26ee2a6aa5ffe6d235f6ab15773d01cdf348e5b20
+size 472644351

checkpoints/freevc-s.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:470dbc952896db9f49859dd479b687273e19b8800f5455bf03d1dfc3f8e605fd
+size 490230998

checkpoints/freevc.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2cc2d047f63b80d1d6780e37611cec11a01d597560393b1fe6118158b3bd47f
+size 472644351

commons.py

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+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+  classname = m.__class__.__name__
+  if classname.find("Conv") != -1:
+    m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+  return int((kernel_size*dilation - dilation)/2)
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def intersperse(lst, item):
+  result = [item] * (len(lst) * 2 + 1)
+  result[1::2] = lst
+  return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+  """KL(P||Q)"""
+  kl = (logs_q - logs_p) - 0.5
+  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
+  return kl
+
+
+def rand_gumbel(shape):
+  """Sample from the Gumbel distribution, protect from overflows."""
+  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+  return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+  return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    ret[i] = x[i, :, idx_str:idx_end]
+  return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def rand_spec_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def get_timing_signal_1d(
+    length, channels, min_timescale=1.0, max_timescale=1.0e4):
+  position = torch.arange(length, dtype=torch.float)
+  num_timescales = channels // 2
+  log_timescale_increment = (
+      math.log(float(max_timescale) / float(min_timescale)) /
+      (num_timescales - 1))
+  inv_timescales = min_timescale * torch.exp(
+      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+  signal = F.pad(signal, [0, 0, 0, channels % 2])
+  signal = signal.view(1, channels, length)
+  return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+  return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+  n_channels_int = n_channels[0]
+  in_act = input_a + input_b
+  t_act = torch.tanh(in_act[:, :n_channels_int, :])
+  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+  acts = t_act * s_act
+  return acts
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def shift_1d(x):
+  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+  return x
+
+
+def sequence_mask(length, max_length=None):
+  if max_length is None:
+    max_length = length.max()
+  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+  return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+  """
+  duration: [b, 1, t_x]
+  mask: [b, 1, t_y, t_x]
+  """
+  device = duration.device
+  
+  b, _, t_y, t_x = mask.shape
+  cum_duration = torch.cumsum(duration, -1)
+  
+  cum_duration_flat = cum_duration.view(b * t_x)
+  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+  path = path.view(b, t_x, t_y)
+  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+  path = path.unsqueeze(1).transpose(2,3) * mask
+  return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm

configs/configs_freevc-24.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8640,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8008"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,6,4,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

configs/configs_freevc-s.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": false
+  }
+}

configs/configs_freevc.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

mel_processing.py

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+import math
+import os
+import random
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.utils.data
+import numpy as np
+import librosa
+import librosa.util as librosa_util
+from librosa.util import normalize, pad_center, tiny
+from scipy.signal import get_window
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec

models.py

@@ -0,0 +1,351 @@
+import copy
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class Encoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+        
+        
+class SpeakerEncoder(torch.nn.Module):
+    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
+        super(SpeakerEncoder, self).__init__()
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, mels):
+        self.lstm.flatten_parameters()
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+        
+    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
+        mel_slices = []
+        for i in range(0, total_frames-partial_frames, partial_hop):
+            mel_range = torch.arange(i, i+partial_frames)
+            mel_slices.append(mel_range)
+            
+        return mel_slices
+    
+    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
+        mel_len = mel.size(1)
+        last_mel = mel[:,-partial_frames:]
+        
+        if mel_len > partial_frames:
+            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
+            mels = list(mel[:,s] for s in mel_slices)
+            mels.append(last_mel)
+            mels = torch.stack(tuple(mels), 0).squeeze(1)
+        
+            with torch.no_grad():
+                partial_embeds = self(mels)
+            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
+            #embed = embed / torch.linalg.norm(embed, 2)
+        else:
+            with torch.no_grad():
+                embed = self(last_mel)
+        
+        return embed
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self, 
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock, 
+    resblock_kernel_sizes, 
+    resblock_dilation_sizes, 
+    upsample_rates, 
+    upsample_initial_channel, 
+    upsample_kernel_sizes,
+    gin_channels,
+    ssl_dim,
+    use_spk,
+    **kwargs):
+
+    super().__init__()
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.gin_channels = gin_channels
+    self.ssl_dim = ssl_dim
+    self.use_spk = use_spk
+
+    self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) 
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+    
+    if not self.use_spk:
+      self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
+
+  def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
+    if c_lengths == None:
+      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    if spec_lengths == None:
+      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
+      
+    if not self.use_spk:
+      g = self.enc_spk(mel.transpose(1,2))
+    g = g.unsqueeze(-1)
+      
+    _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
+    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
+    z_p = self.flow(z, spec_mask, g=g)
+
+    z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    
+    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, c, g=None, mel=None, c_lengths=None):
+    if c_lengths == None:
+      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    if not self.use_spk:
+      g = self.enc_spk.embed_utterance(mel.transpose(1,2))
+    g = g.unsqueeze(-1)
+
+    z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
+    z = self.flow(z_p, c_mask, g=g, reverse=True)
+    o = self.dec(z * c_mask, g=g)
+    
+    return o

modules.py

@@ -0,0 +1,342 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import commons
+from commons import init_weights, get_padding
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    super().__init__()
+    self.channels = channels
+    self.eps = eps
+
+    self.gamma = nn.Parameter(torch.ones(channels))
+    self.beta = nn.Parameter(torch.zeros(channels))
+
+  def forward(self, x):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+ 
+class ConvReluNorm(nn.Module):
+  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+    super().__init__()
+    self.in_channels = in_channels
+    self.hidden_channels = hidden_channels
+    self.out_channels = out_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+    assert n_layers > 1, "Number of layers should be larger than 0."
+
+    self.conv_layers = nn.ModuleList()
+    self.norm_layers = nn.ModuleList()
+    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+    self.norm_layers.append(LayerNorm(hidden_channels))
+    self.relu_drop = nn.Sequential(
+        nn.ReLU(),
+        nn.Dropout(p_dropout))
+    for _ in range(n_layers-1):
+      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+      self.norm_layers.append(LayerNorm(hidden_channels))
+    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask):
+    x_org = x
+    for i in range(self.n_layers):
+      x = self.conv_layers[i](x * x_mask)
+      x = self.norm_layers[i](x)
+      x = self.relu_drop(x)
+    x = x_org + self.proj(x)
+    return x * x_mask
+
+
+class DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    self.hidden_channels =hidden_channels
+    self.kernel_size = kernel_size,
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.p_dropout = p_dropout
+
+    self.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
+      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+    for i in range(n_layers):
+      dilation = dilation_rate ** i
+      padding = int((kernel_size * dilation - dilation) / 2)
+      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
+                                 dilation=dilation, padding=padding)
+      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+      self.in_layers.append(in_layer)
+
+      # last one is not necessary
+      if i < n_layers - 1:
+        res_skip_channels = 2 * hidden_channels
+      else:
+        res_skip_channels = hidden_channels
+
+      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+      self.res_skip_layers.append(res_skip_layer)
+
+  def forward(self, x, x_mask, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      if g is not None:
+        cond_offset = i * 2 * self.hidden_channels
+        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
+      else:
+        g_l = torch.zeros_like(x_in)
+
+      acts = commons.fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * x_mask
+        output = output + res_skip_acts[:,self.hidden_channels:,:]
+      else:
+        output = output + res_skip_acts
+    return output * x_mask
+
+  def remove_weight_norm(self):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+    
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x

requirements.txt

@@ -0,0 +1,8 @@
+altair
+httpx==0.24.1
+numpy
+scipy
+torch
+transformers
+librosa
+webrtcvad==2.0.10

speaker_encoder/ckpt/pretrained_bak_5805000.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc7ff82ef75becd495aab2ede3a8220da393a717f178ae9534df355a6173bbca
+size 17090379

speaker_encoder/data_objects/speaker_encoder_data_objects___init__.py

@@ -0,0 +1,2 @@
+from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
+from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataLoader

speaker_encoder/data_objects/speaker_encoder_data_objects_random_cycler.py

@@ -0,0 +1,37 @@
+import random
+
+class RandomCycler:
+    """
+    Creates an internal copy of a sequence and allows access to its items in a constrained random 
+    order. For a source sequence of n items and one or several consecutive queries of a total 
+    of m items, the following guarantees hold (one implies the other):
+        - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
+        - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
+    """
+    
+    def __init__(self, source):
+        if len(source) == 0:
+            raise Exception("Can't create RandomCycler from an empty collection")
+        self.all_items = list(source)
+        self.next_items = []
+    
+    def sample(self, count: int):
+        shuffle = lambda l: random.sample(l, len(l))
+        
+        out = []
+        while count > 0:
+            if count >= len(self.all_items):
+                out.extend(shuffle(list(self.all_items)))
+                count -= len(self.all_items)
+                continue
+            n = min(count, len(self.next_items))
+            out.extend(self.next_items[:n])
+            count -= n
+            self.next_items = self.next_items[n:]
+            if len(self.next_items) == 0:
+                self.next_items = shuffle(list(self.all_items))
+        return out
+    
+    def __next__(self):
+        return self.sample(1)[0]
+

speaker_encoder/data_objects/speaker_encoder_data_objects_speaker.py

@@ -0,0 +1,40 @@
+from speaker_encoder.data_objects.random_cycler import RandomCycler
+from speaker_encoder.data_objects.utterance import Utterance
+from pathlib import Path
+
+# Contains the set of utterances of a single speaker
+class Speaker:
+    def __init__(self, root: Path):
+        self.root = root
+        self.name = root.name
+        self.utterances = None
+        self.utterance_cycler = None
+        
+    def _load_utterances(self):
+        with self.root.joinpath("_sources.txt").open("r") as sources_file:
+            sources = [l.split(",") for l in sources_file]
+        sources = {frames_fname: wave_fpath for frames_fname, wave_fpath in sources}
+        self.utterances = [Utterance(self.root.joinpath(f), w) for f, w in sources.items()]
+        self.utterance_cycler = RandomCycler(self.utterances)
+               
+    def random_partial(self, count, n_frames):
+        """
+        Samples a batch of <count> unique partial utterances from the disk in a way that all 
+        utterances come up at least once every two cycles and in a random order every time.
+        
+        :param count: The number of partial utterances to sample from the set of utterances from 
+        that speaker. Utterances are guaranteed not to be repeated if <count> is not larger than 
+        the number of utterances available.
+        :param n_frames: The number of frames in the partial utterance.
+        :return: A list of tuples (utterance, frames, range) where utterance is an Utterance, 
+        frames are the frames of the partial utterances and range is the range of the partial 
+        utterance with regard to the complete utterance.
+        """
+        if self.utterances is None:
+            self._load_utterances()
+
+        utterances = self.utterance_cycler.sample(count)
+
+        a = [(u,) + u.random_partial(n_frames) for u in utterances]
+
+        return a

speaker_encoder/data_objects/speaker_encoder_data_objects_speaker_batch.py

@@ -0,0 +1,12 @@
+import numpy as np
+from typing import List
+from speaker_encoder.data_objects.speaker import Speaker
+
+class SpeakerBatch:
+    def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
+        self.speakers = speakers
+        self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
+        
+        # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
+        # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
+        self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])

speaker_encoder/data_objects/speaker_encoder_data_objects_speaker_verification_dataset.py

@@ -0,0 +1,56 @@
+from speaker_encoder.data_objects.random_cycler import RandomCycler
+from speaker_encoder.data_objects.speaker_batch import SpeakerBatch
+from speaker_encoder.data_objects.speaker import Speaker
+from speaker_encoder.params_data import partials_n_frames
+from torch.utils.data import Dataset, DataLoader
+from pathlib import Path
+
+# TODO: improve with a pool of speakers for data efficiency
+
+class SpeakerVerificationDataset(Dataset):
+    def __init__(self, datasets_root: Path):
+        self.root = datasets_root
+        speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
+        if len(speaker_dirs) == 0:
+            raise Exception("No speakers found. Make sure you are pointing to the directory "
+                            "containing all preprocessed speaker directories.")
+        self.speakers = [Speaker(speaker_dir) for speaker_dir in speaker_dirs]
+        self.speaker_cycler = RandomCycler(self.speakers)
+
+    def __len__(self):
+        return int(1e10)
+        
+    def __getitem__(self, index):
+        return next(self.speaker_cycler)
+    
+    def get_logs(self):
+        log_string = ""
+        for log_fpath in self.root.glob("*.txt"):
+            with log_fpath.open("r") as log_file:
+                log_string += "".join(log_file.readlines())
+        return log_string
+    
+    
+class SpeakerVerificationDataLoader(DataLoader):
+    def __init__(self, dataset, speakers_per_batch, utterances_per_speaker, sampler=None, 
+                 batch_sampler=None, num_workers=0, pin_memory=False, timeout=0, 
+                 worker_init_fn=None):
+        self.utterances_per_speaker = utterances_per_speaker
+
+        super().__init__(
+            dataset=dataset, 
+            batch_size=speakers_per_batch, 
+            shuffle=False, 
+            sampler=sampler, 
+            batch_sampler=batch_sampler, 
+            num_workers=num_workers,
+            collate_fn=self.collate, 
+            pin_memory=pin_memory, 
+            drop_last=False, 
+            timeout=timeout, 
+            worker_init_fn=worker_init_fn
+        )
+
+    def collate(self, speakers):
+        return SpeakerBatch(speakers, self.utterances_per_speaker, partials_n_frames) 
+    

speaker_encoder/data_objects/speaker_encoder_data_objects_utterance.py

@@ -0,0 +1,26 @@
+import numpy as np
+
+
+class Utterance:
+    def __init__(self, frames_fpath, wave_fpath):
+        self.frames_fpath = frames_fpath
+        self.wave_fpath = wave_fpath
+        
+    def get_frames(self):
+        return np.load(self.frames_fpath)
+
+    def random_partial(self, n_frames):
+        """
+        Crops the frames into a partial utterance of n_frames
+        
+        :param n_frames: The number of frames of the partial utterance
+        :return: the partial utterance frames and a tuple indicating the start and end of the 
+        partial utterance in the complete utterance.
+        """
+        frames = self.get_frames()
+        if frames.shape[0] == n_frames:
+            start = 0
+        else:
+            start = np.random.randint(0, frames.shape[0] - n_frames)
+        end = start + n_frames
+        return frames[start:end], (start, end)

speaker_encoder/speaker_encoder_audio.py

@@ -0,0 +1,107 @@
+from scipy.ndimage.morphology import binary_dilation
+from speaker_encoder.params_data import *
+from pathlib import Path
+from typing import Optional, Union
+import numpy as np
+import webrtcvad
+import librosa
+import struct
+
+int16_max = (2 ** 15) - 1
+
+
+def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
+                   source_sr: Optional[int] = None):
+    """
+    Applies the preprocessing operations used in training the Speaker Encoder to a waveform 
+    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
+
+    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 
+    just .wav), either the waveform as a numpy array of floats.
+    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 
+    preprocessing. After preprocessing, the waveform's sampling rate will match the data 
+    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 
+    this argument will be ignored.
+    """
+    # Load the wav from disk if needed
+    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
+        wav, source_sr = librosa.load(fpath_or_wav, sr=None)
+    else:
+        wav = fpath_or_wav
+    
+    # Resample the wav if needed
+    if source_sr is not None and source_sr != sampling_rate:
+        wav = librosa.resample(wav, source_sr, sampling_rate)
+        
+    # Apply the preprocessing: normalize volume and shorten long silences 
+    wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
+    wav = trim_long_silences(wav)
+    
+    return wav
+
+
+def wav_to_mel_spectrogram(wav):
+    """
+    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
+    Note: this not a log-mel spectrogram.
+    """
+    frames = librosa.feature.melspectrogram(
+        y=wav,
+        sr=sampling_rate,
+        n_fft=int(sampling_rate * mel_window_length / 1000),
+        hop_length=int(sampling_rate * mel_window_step / 1000),
+        n_mels=mel_n_channels
+    )
+    return frames.astype(np.float32).T
+
+
+def trim_long_silences(wav):
+    """
+    Ensures that segments without voice in the waveform remain no longer than a 
+    threshold determined by the VAD parameters in params.py.
+
+    :param wav: the raw waveform as a numpy array of floats 
+    :return: the same waveform with silences trimmed away (length <= original wav length)
+    """
+    # Compute the voice detection window size
+    samples_per_window = (vad_window_length * sampling_rate) // 1000
+    
+    # Trim the end of the audio to have a multiple of the window size
+    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
+    
+    # Convert the float waveform to 16-bit mono PCM
+    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
+    
+    # Perform voice activation detection
+    voice_flags = []
+    vad = webrtcvad.Vad(mode=3)
+    for window_start in range(0, len(wav), samples_per_window):
+        window_end = window_start + samples_per_window
+        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
+                                         sample_rate=sampling_rate))
+    voice_flags = np.array(voice_flags)
+    
+    # Smooth the voice detection with a moving average
+    def moving_average(array, width):
+        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
+        ret = np.cumsum(array_padded, dtype=float)
+        ret[width:] = ret[width:] - ret[:-width]
+        return ret[width - 1:] / width
+    
+    audio_mask = moving_average(voice_flags, vad_moving_average_width)
+    audio_mask = np.round(audio_mask).astype(np.bool)
+    
+    # Dilate the voiced regions
+    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
+    audio_mask = np.repeat(audio_mask, samples_per_window)
+    
+    return wav[audio_mask == True]
+
+
+def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
+    if increase_only and decrease_only:
+        raise ValueError("Both increase only and decrease only are set")
+    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
+    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
+        return wav
+    return wav * (10 ** (dBFS_change / 20))

speaker_encoder/speaker_encoder_compute_embed.py

@@ -0,0 +1,40 @@
+from speaker_encoder import inference as encoder
+from multiprocessing.pool import Pool
+from functools import partial
+from pathlib import Path
+# from utils import logmmse
+# from tqdm import tqdm
+# import numpy as np
+# import librosa
+
+
+def embed_utterance(fpaths, encoder_model_fpath):
+    if not encoder.is_loaded():
+        encoder.load_model(encoder_model_fpath)
+
+    # Compute the speaker embedding of the utterance
+    wav_fpath, embed_fpath = fpaths
+    wav = np.load(wav_fpath)
+    wav = encoder.preprocess_wav(wav)
+    embed = encoder.embed_utterance(wav)
+    np.save(embed_fpath, embed, allow_pickle=False)
+    
+ 
+def create_embeddings(outdir_root: Path, wav_dir: Path, encoder_model_fpath: Path, n_processes: int):
+
+    wav_dir = outdir_root.joinpath("audio")
+    metadata_fpath = synthesizer_root.joinpath("train.txt")
+    assert wav_dir.exists() and metadata_fpath.exists()
+    embed_dir = synthesizer_root.joinpath("embeds")
+    embed_dir.mkdir(exist_ok=True)
+    
+    # Gather the input wave filepath and the target output embed filepath
+    with metadata_fpath.open("r") as metadata_file:
+        metadata = [line.split("|") for line in metadata_file]
+        fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
+        
+    # TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
+    # Embed the utterances in separate threads
+    func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
+    job = Pool(n_processes).imap(func, fpaths)
+    list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))

speaker_encoder/speaker_encoder_config.py

@@ -0,0 +1,45 @@
+librispeech_datasets = {
+    "train": {
+        "clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"],
+        "other": ["LibriSpeech/train-other-500"]
+    },
+    "test": {
+        "clean": ["LibriSpeech/test-clean"],
+        "other": ["LibriSpeech/test-other"]
+    },
+    "dev": {
+        "clean": ["LibriSpeech/dev-clean"],
+        "other": ["LibriSpeech/dev-other"]
+    },
+}
+libritts_datasets = {
+    "train": {
+        "clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"],
+        "other": ["LibriTTS/train-other-500"]
+    },
+    "test": {
+        "clean": ["LibriTTS/test-clean"],
+        "other": ["LibriTTS/test-other"]
+    },
+    "dev": {
+        "clean": ["LibriTTS/dev-clean"],
+        "other": ["LibriTTS/dev-other"]
+    },
+}
+voxceleb_datasets = {
+    "voxceleb1" : {
+        "train": ["VoxCeleb1/wav"],
+        "test": ["VoxCeleb1/test_wav"]
+    },
+    "voxceleb2" : {
+        "train": ["VoxCeleb2/dev/aac"],
+        "test": ["VoxCeleb2/test_wav"]
+    }
+}
+
+other_datasets = [
+    "LJSpeech-1.1",
+    "VCTK-Corpus/wav48",
+]
+
+anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]

speaker_encoder/speaker_encoder_hparams.py

@@ -0,0 +1,31 @@
+## Mel-filterbank
+mel_window_length = 25  # In milliseconds
+mel_window_step = 10    # In milliseconds
+mel_n_channels = 40
+
+
+## Audio
+sampling_rate = 16000
+# Number of spectrogram frames in a partial utterance
+partials_n_frames = 160     # 1600 ms
+
+
+## Voice Activation Detection
+# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
+# This sets the granularity of the VAD. Should not need to be changed.
+vad_window_length = 30  # In milliseconds
+# Number of frames to average together when performing the moving average smoothing.
+# The larger this value, the larger the VAD variations must be to not get smoothed out. 
+vad_moving_average_width = 8
+# Maximum number of consecutive silent frames a segment can have.
+vad_max_silence_length = 6
+
+
+## Audio volume normalization
+audio_norm_target_dBFS = -30
+
+
+## Model parameters
+model_hidden_size = 256
+model_embedding_size = 256
+model_num_layers = 3

speaker_encoder/speaker_encoder_inference.py

@@ -0,0 +1,177 @@
+from speaker_encoder.params_data import *
+from speaker_encoder.model import SpeakerEncoder
+from speaker_encoder.audio import preprocess_wav   # We want to expose this function from here
+from matplotlib import cm
+from speaker_encoder import audio
+from pathlib import Path
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+_model = None # type: SpeakerEncoder
+_device = None # type: torch.device
+
+
+def load_model(weights_fpath: Path, device=None):
+    """
+    Loads the model in memory. If this function is not explicitely called, it will be run on the 
+    first call to embed_frames() with the default weights file.
+    
+    :param weights_fpath: the path to saved model weights.
+    :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The 
+    model will be loaded and will run on this device. Outputs will however always be on the cpu. 
+    If None, will default to your GPU if it"s available, otherwise your CPU.
+    """
+    # TODO: I think the slow loading of the encoder might have something to do with the device it
+    #   was saved on. Worth investigating.
+    global _model, _device
+    if device is None:
+        _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    elif isinstance(device, str):
+        _device = torch.device(device)
+    _model = SpeakerEncoder(_device, torch.device("cpu"))
+    checkpoint = torch.load(weights_fpath)
+    _model.load_state_dict(checkpoint["model_state"])
+    _model.eval()
+    print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"]))
+    
+    
+def is_loaded():
+    return _model is not None
+
+
+def embed_frames_batch(frames_batch):
+    """
+    Computes embeddings for a batch of mel spectrogram.
+    
+    :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape 
+    (batch_size, n_frames, n_channels)
+    :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size)
+    """
+    if _model is None:
+        raise Exception("Model was not loaded. Call load_model() before inference.")
+    
+    frames = torch.from_numpy(frames_batch).to(_device)
+    embed = _model.forward(frames).detach().cpu().numpy()
+    return embed
+
+
+def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames,
+                           min_pad_coverage=0.75, overlap=0.5):
+    """
+    Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain 
+    partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel 
+    spectrogram slices are returned, so as to make each partial utterance waveform correspond to 
+    its spectrogram. This function assumes that the mel spectrogram parameters used are those 
+    defined in params_data.py.
+    
+    The returned ranges may be indexing further than the length of the waveform. It is 
+    recommended that you pad the waveform with zeros up to wave_slices[-1].stop.
+    
+    :param n_samples: the number of samples in the waveform
+    :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial 
+    utterance
+    :param min_pad_coverage: when reaching the last partial utterance, it may or may not have 
+    enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present, 
+    then the last partial utterance will be considered, as if we padded the audio. Otherwise, 
+    it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial 
+    utterance, this parameter is ignored so that the function always returns at least 1 slice.
+    :param overlap: by how much the partial utterance should overlap. If set to 0, the partial 
+    utterances are entirely disjoint. 
+    :return: the waveform slices and mel spectrogram slices as lists of array slices. Index 
+    respectively the waveform and the mel spectrogram with these slices to obtain the partial 
+    utterances.
+    """
+    assert 0 <= overlap < 1
+    assert 0 < min_pad_coverage <= 1
+    
+    samples_per_frame = int((sampling_rate * mel_window_step / 1000))
+    n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
+    frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1)
+
+    # Compute the slices
+    wav_slices, mel_slices = [], []
+    steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1)
+    for i in range(0, steps, frame_step):
+        mel_range = np.array([i, i + partial_utterance_n_frames])
+        wav_range = mel_range * samples_per_frame
+        mel_slices.append(slice(*mel_range))
+        wav_slices.append(slice(*wav_range))
+        
+    # Evaluate whether extra padding is warranted or not
+    last_wav_range = wav_slices[-1]
+    coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
+    if coverage < min_pad_coverage and len(mel_slices) > 1:
+        mel_slices = mel_slices[:-1]
+        wav_slices = wav_slices[:-1]
+    
+    return wav_slices, mel_slices
+
+
+def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs):
+    """
+    Computes an embedding for a single utterance.
+    
+    # TODO: handle multiple wavs to benefit from batching on GPU
+    :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32
+    :param using_partials: if True, then the utterance is split in partial utterances of 
+    <partial_utterance_n_frames> frames and the utterance embedding is computed from their 
+    normalized average. If False, the utterance is instead computed from feeding the entire 
+    spectogram to the network.
+    :param return_partials: if True, the partial embeddings will also be returned along with the 
+    wav slices that correspond to the partial embeddings.
+    :param kwargs: additional arguments to compute_partial_splits()
+    :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If 
+    <return_partials> is True, the partial utterances as a numpy array of float32 of shape 
+    (n_partials, model_embedding_size) and the wav partials as a list of slices will also be 
+    returned. If <using_partials> is simultaneously set to False, both these values will be None 
+    instead.
+    """
+    # Process the entire utterance if not using partials
+    if not using_partials:
+        frames = audio.wav_to_mel_spectrogram(wav)
+        embed = embed_frames_batch(frames[None, ...])[0]
+        if return_partials:
+            return embed, None, None
+        return embed
+    
+    # Compute where to split the utterance into partials and pad if necessary
+    wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs)
+    max_wave_length = wave_slices[-1].stop
+    if max_wave_length >= len(wav):
+        wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
+    
+    # Split the utterance into partials
+    frames = audio.wav_to_mel_spectrogram(wav)
+    frames_batch = np.array([frames[s] for s in mel_slices])
+    partial_embeds = embed_frames_batch(frames_batch)
+    
+    # Compute the utterance embedding from the partial embeddings
+    raw_embed = np.mean(partial_embeds, axis=0)
+    embed = raw_embed / np.linalg.norm(raw_embed, 2)
+    
+    if return_partials:
+        return embed, partial_embeds, wave_slices
+    return embed
+
+
+def embed_speaker(wavs, **kwargs):
+    raise NotImplemented()
+
+
+def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
+    if ax is None:
+        ax = plt.gca()
+    
+    if shape is None:
+        height = int(np.sqrt(len(embed)))
+        shape = (height, -1)
+    embed = embed.reshape(shape)
+    
+    cmap = cm.get_cmap()
+    mappable = ax.imshow(embed, cmap=cmap)
+    cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04)
+    cbar.set_clim(*color_range)
+    
+    ax.set_xticks([]), ax.set_yticks([])
+    ax.set_title(title)

speaker_encoder/speaker_encoder_model.py

@@ -0,0 +1,135 @@
+from speaker_encoder.params_model import *
+from speaker_encoder.params_data import *
+from scipy.interpolate import interp1d
+from sklearn.metrics import roc_curve
+from torch.nn.utils import clip_grad_norm_
+from scipy.optimize import brentq
+from torch import nn
+import numpy as np
+import torch
+
+
+class SpeakerEncoder(nn.Module):
+    def __init__(self, device, loss_device):
+        super().__init__()
+        self.loss_device = loss_device
+        
+        # Network defition
+        self.lstm = nn.LSTM(input_size=mel_n_channels,     # 40
+                            hidden_size=model_hidden_size, # 256 
+                            num_layers=model_num_layers,   # 3 
+                            batch_first=True).to(device)
+        self.linear = nn.Linear(in_features=model_hidden_size, 
+                                out_features=model_embedding_size).to(device)
+        self.relu = torch.nn.ReLU().to(device)
+        
+        # Cosine similarity scaling (with fixed initial parameter values)
+        self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device)
+        self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device)
+
+        # Loss
+        self.loss_fn = nn.CrossEntropyLoss().to(loss_device)
+        
+    def do_gradient_ops(self):
+        # Gradient scale
+        self.similarity_weight.grad *= 0.01
+        self.similarity_bias.grad *= 0.01
+            
+        # Gradient clipping
+        clip_grad_norm_(self.parameters(), 3, norm_type=2)
+    
+    def forward(self, utterances, hidden_init=None):
+        """
+        Computes the embeddings of a batch of utterance spectrograms.
+        
+        :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape 
+        (batch_size, n_frames, n_channels) 
+        :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, 
+        batch_size, hidden_size). Will default to a tensor of zeros if None.
+        :return: the embeddings as a tensor of shape (batch_size, embedding_size)
+        """
+        # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state
+        # and the final cell state.
+        out, (hidden, cell) = self.lstm(utterances, hidden_init)
+        
+        # We take only the hidden state of the last layer
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        
+        # L2-normalize it
+        embeds = embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+        
+        return embeds
+    
+    def similarity_matrix(self, embeds):
+        """
+        Computes the similarity matrix according the section 2.1 of GE2E.
+
+        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
+        utterances_per_speaker, embedding_size)
+        :return: the similarity matrix as a tensor of shape (speakers_per_batch,
+        utterances_per_speaker, speakers_per_batch)
+        """
+        speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
+        
+        # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation
+        centroids_incl = torch.mean(embeds, dim=1, keepdim=True)
+        centroids_incl = centroids_incl.clone() / torch.norm(centroids_incl, dim=2, keepdim=True)
+
+        # Exclusive centroids (1 per utterance)
+        centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds)
+        centroids_excl /= (utterances_per_speaker - 1)
+        centroids_excl = centroids_excl.clone() / torch.norm(centroids_excl, dim=2, keepdim=True)
+
+        # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot
+        # product of these vectors (which is just an element-wise multiplication reduced by a sum).
+        # We vectorize the computation for efficiency.
+        sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker,
+                                 speakers_per_batch).to(self.loss_device)
+        mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int)
+        for j in range(speakers_per_batch):
+            mask = np.where(mask_matrix[j])[0]
+            sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2)
+            sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1)
+        
+        ## Even more vectorized version (slower maybe because of transpose)
+        # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker
+        #                           ).to(self.loss_device)
+        # eye = np.eye(speakers_per_batch, dtype=np.int)
+        # mask = np.where(1 - eye)
+        # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2)
+        # mask = np.where(eye)
+        # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2)
+        # sim_matrix2 = sim_matrix2.transpose(1, 2)
+        
+        sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias
+        return sim_matrix
+    
+    def loss(self, embeds):
+        """
+        Computes the softmax loss according the section 2.1 of GE2E.
+        
+        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
+        utterances_per_speaker, embedding_size)
+        :return: the loss and the EER for this batch of embeddings.
+        """
+        speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
+        
+        # Loss
+        sim_matrix = self.similarity_matrix(embeds)
+        sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker, 
+                                         speakers_per_batch))
+        ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker)
+        target = torch.from_numpy(ground_truth).long().to(self.loss_device)
+        loss = self.loss_fn(sim_matrix, target)
+        
+        # EER (not backpropagated)
+        with torch.no_grad():
+            inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0]
+            labels = np.array([inv_argmax(i) for i in ground_truth])
+            preds = sim_matrix.detach().cpu().numpy()
+
+            # Snippet from https://yangcha.github.io/EER-ROC/
+            fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten())           
+            eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
+            
+        return loss, eer

speaker_encoder/speaker_encoder_params_data.py

@@ -0,0 +1,29 @@
+
+## Mel-filterbank
+mel_window_length = 25  # In milliseconds
+mel_window_step = 10    # In milliseconds
+mel_n_channels = 40
+
+
+## Audio
+sampling_rate = 16000
+# Number of spectrogram frames in a partial utterance
+partials_n_frames = 160     # 1600 ms
+# Number of spectrogram frames at inference
+inference_n_frames = 80     #  800 ms
+
+
+## Voice Activation Detection
+# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
+# This sets the granularity of the VAD. Should not need to be changed.
+vad_window_length = 30  # In milliseconds
+# Number of frames to average together when performing the moving average smoothing.
+# The larger this value, the larger the VAD variations must be to not get smoothed out. 
+vad_moving_average_width = 8
+# Maximum number of consecutive silent frames a segment can have.
+vad_max_silence_length = 6
+
+
+## Audio volume normalization
+audio_norm_target_dBFS = -30
+

speaker_encoder/speaker_encoder_params_model.py

@@ -0,0 +1,11 @@
+
+## Model parameters
+model_hidden_size = 256
+model_embedding_size = 256
+model_num_layers = 3
+
+
+## Training parameters
+learning_rate_init = 1e-4
+speakers_per_batch = 64
+utterances_per_speaker = 10

speaker_encoder/speaker_encoder_preprocess.py

@@ -0,0 +1,285 @@
+from multiprocess.pool import ThreadPool
+from speaker_encoder.params_data import *
+from speaker_encoder.config import librispeech_datasets, anglophone_nationalites
+from datetime import datetime
+from speaker_encoder import audio
+from pathlib import Path
+from tqdm import tqdm
+import numpy as np
+
+
+class DatasetLog:
+    """
+    Registers metadata about the dataset in a text file.
+    """
+    def __init__(self, root, name):
+        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
+        self.sample_data = dict()
+        
+        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
+        self.write_line("Creating dataset %s on %s" % (name, start_time))
+        self.write_line("-----")
+        self._log_params()
+        
+    def _log_params(self):
+        from speaker_encoder import params_data
+        self.write_line("Parameter values:")
+        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
+            value = getattr(params_data, param_name)
+            self.write_line("\t%s: %s" % (param_name, value))
+        self.write_line("-----")
+    
+    def write_line(self, line):
+        self.text_file.write("%s\n" % line)
+        
+    def add_sample(self, **kwargs):
+        for param_name, value in kwargs.items():
+            if not param_name in self.sample_data:
+                self.sample_data[param_name] = []
+            self.sample_data[param_name].append(value)
+            
+    def finalize(self):
+        self.write_line("Statistics:")
+        for param_name, values in self.sample_data.items():
+            self.write_line("\t%s:" % param_name)
+            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
+            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
+        self.write_line("-----")
+        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
+        self.write_line("Finished on %s" % end_time)
+        self.text_file.close()
+       
+        
+def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
+    dataset_root = datasets_root.joinpath(dataset_name)
+    if not dataset_root.exists():
+        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
+        return None, None
+    return dataset_root, DatasetLog(out_dir, dataset_name)
+
+
+def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
+                             skip_existing, logger):
+    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
+    
+    # Function to preprocess utterances for one speaker
+    def preprocess_speaker(speaker_dir: Path):
+        # Give a name to the speaker that includes its dataset
+        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
+        
+        # Create an output directory with that name, as well as a txt file containing a 
+        # reference to each source file.
+        speaker_out_dir = out_dir.joinpath(speaker_name)
+        speaker_out_dir.mkdir(exist_ok=True)
+        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
+        
+        # There's a possibility that the preprocessing was interrupted earlier, check if 
+        # there already is a sources file.
+        if sources_fpath.exists():
+            try:
+                with sources_fpath.open("r") as sources_file:
+                    existing_fnames = {line.split(",")[0] for line in sources_file}
+            except:
+                existing_fnames = {}
+        else:
+            existing_fnames = {}
+        
+        # Gather all audio files for that speaker recursively
+        sources_file = sources_fpath.open("a" if skip_existing else "w")
+        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
+            # Check if the target output file already exists
+            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
+            out_fname = out_fname.replace(".%s" % extension, ".npy")
+            if skip_existing and out_fname in existing_fnames:
+                continue
+                
+            # Load and preprocess the waveform
+            wav = audio.preprocess_wav(in_fpath)
+            if len(wav) == 0:
+                continue
+            
+            # Create the mel spectrogram, discard those that are too short
+            frames = audio.wav_to_mel_spectrogram(wav)
+            if len(frames) < partials_n_frames:
+                continue
+            
+            out_fpath = speaker_out_dir.joinpath(out_fname)
+            np.save(out_fpath, frames)
+            logger.add_sample(duration=len(wav) / sampling_rate)
+            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
+        
+        sources_file.close()
+    
+    # Process the utterances for each speaker
+    with ThreadPool(8) as pool:
+        list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
+                  unit="speakers"))
+    logger.finalize()
+    print("Done preprocessing %s.\n" % dataset_name)
+
+
+# Function to preprocess utterances for one speaker
+def __preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, extension: str, skip_existing: bool):
+        # Give a name to the speaker that includes its dataset
+        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
+        
+        # Create an output directory with that name, as well as a txt file containing a 
+        # reference to each source file.
+        speaker_out_dir = out_dir.joinpath(speaker_name)
+        speaker_out_dir.mkdir(exist_ok=True)
+        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
+        
+        # There's a possibility that the preprocessing was interrupted earlier, check if 
+        # there already is a sources file.
+        # if sources_fpath.exists():
+        #     try:
+        #         with sources_fpath.open("r") as sources_file:
+        #             existing_fnames = {line.split(",")[0] for line in sources_file}
+        #     except:
+        #         existing_fnames = {}
+        # else:
+        #     existing_fnames = {}
+        existing_fnames = {}
+        # Gather all audio files for that speaker recursively
+        sources_file = sources_fpath.open("a" if skip_existing else "w")
+
+        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
+            # Check if the target output file already exists
+            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
+            out_fname = out_fname.replace(".%s" % extension, ".npy")
+            if skip_existing and out_fname in existing_fnames:
+                continue
+                
+            # Load and preprocess the waveform
+            wav = audio.preprocess_wav(in_fpath)
+            if len(wav) == 0:
+                continue
+            
+            # Create the mel spectrogram, discard those that are too short
+            frames = audio.wav_to_mel_spectrogram(wav)
+            if len(frames) < partials_n_frames:
+                continue
+            
+            out_fpath = speaker_out_dir.joinpath(out_fname)
+            np.save(out_fpath, frames)
+            # logger.add_sample(duration=len(wav) / sampling_rate)
+            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
+        
+        sources_file.close()
+        return len(wav)
+
+def _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
+                             skip_existing, logger):
+    # from multiprocessing import Pool, cpu_count
+    from pathos.multiprocessing import ProcessingPool as Pool
+    # Function to preprocess utterances for one speaker
+    def __preprocess_speaker(speaker_dir: Path):
+        # Give a name to the speaker that includes its dataset
+        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
+        
+        # Create an output directory with that name, as well as a txt file containing a 
+        # reference to each source file.
+        speaker_out_dir = out_dir.joinpath(speaker_name)
+        speaker_out_dir.mkdir(exist_ok=True)
+        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
+        
+        existing_fnames = {}
+        # Gather all audio files for that speaker recursively
+        sources_file = sources_fpath.open("a" if skip_existing else "w")
+        wav_lens = []
+        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
+            # Check if the target output file already exists
+            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
+            out_fname = out_fname.replace(".%s" % extension, ".npy")
+            if skip_existing and out_fname in existing_fnames:
+                continue
+                
+            # Load and preprocess the waveform
+            wav = audio.preprocess_wav(in_fpath)
+            if len(wav) == 0:
+                continue
+            
+            # Create the mel spectrogram, discard those that are too short
+            frames = audio.wav_to_mel_spectrogram(wav)
+            if len(frames) < partials_n_frames:
+                continue
+            
+            out_fpath = speaker_out_dir.joinpath(out_fname)
+            np.save(out_fpath, frames)
+            # logger.add_sample(duration=len(wav) / sampling_rate)
+            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
+            wav_lens.append(len(wav))
+        sources_file.close()
+        return wav_lens
+
+    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
+    # Process the utterances for each speaker
+    # with ThreadPool(8) as pool:
+    #     list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
+    #               unit="speakers"))
+    pool = Pool(processes=20)
+    for i, wav_lens in enumerate(pool.map(__preprocess_speaker, speaker_dirs), 1):
+        for wav_len in wav_lens:
+            logger.add_sample(duration=wav_len / sampling_rate)
+        print(f'{i}/{len(speaker_dirs)} \r')
+
+    logger.finalize()
+    print("Done preprocessing %s.\n" % dataset_name)
+
+
+def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
+    for dataset_name in librispeech_datasets["train"]["other"]:
+        # Initialize the preprocessing
+        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
+        if not dataset_root:
+            return 
+        
+        # Preprocess all speakers
+        speaker_dirs = list(dataset_root.glob("*"))
+        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
+                                 skip_existing, logger)
+
+
+def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
+    # Initialize the preprocessing
+    dataset_name = "VoxCeleb1"
+    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
+    if not dataset_root:
+        return
+
+    # Get the contents of the meta file
+    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
+        metadata = [line.split("\t") for line in metafile][1:]
+    
+    # Select the ID and the nationality, filter out non-anglophone speakers
+    nationalities = {line[0]: line[3] for line in metadata}
+    # keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if 
+    #                     nationality.lower() in anglophone_nationalites]
+    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items()]                        
+    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % 
+          (len(keep_speaker_ids), len(nationalities)))
+    
+    # Get the speaker directories for anglophone speakers only
+    speaker_dirs = dataset_root.joinpath("wav").glob("*")
+    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
+                    speaker_dir.name in keep_speaker_ids]
+    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % 
+          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
+
+    # Preprocess all speakers
+    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
+                             skip_existing, logger)
+
+
+def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
+    # Initialize the preprocessing
+    dataset_name = "VoxCeleb2"
+    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
+    if not dataset_root:
+        return
+    
+    # Get the speaker directories
+    # Preprocess all speakers
+    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
+    _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
+                             skip_existing, logger)

speaker_encoder/speaker_encoder_train.py

@@ -0,0 +1,125 @@
+from speaker_encoder.visualizations import Visualizations
+from speaker_encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset
+from speaker_encoder.params_model import *
+from speaker_encoder.model import SpeakerEncoder
+from utils.profiler import Profiler
+from pathlib import Path
+import torch
+
+def sync(device: torch.device):
+    # FIXME
+    return 
+    # For correct profiling (cuda operations are async)
+    if device.type == "cuda":
+        torch.cuda.synchronize(device)
+
+def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int,
+          backup_every: int, vis_every: int, force_restart: bool, visdom_server: str,
+          no_visdom: bool):
+    # Create a dataset and a dataloader
+    dataset = SpeakerVerificationDataset(clean_data_root)
+    loader = SpeakerVerificationDataLoader(
+        dataset,
+        speakers_per_batch,       # 64
+        utterances_per_speaker,   # 10
+        num_workers=8,
+    )
+    
+    # Setup the device on which to run the forward pass and the loss. These can be different, 
+    # because the forward pass is faster on the GPU whereas the loss is often (depending on your
+    # hyperparameters) faster on the CPU.
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    # FIXME: currently, the gradient is None if loss_device is cuda
+    loss_device = torch.device("cpu")
+    
+    # Create the model and the optimizer
+    model = SpeakerEncoder(device, loss_device)
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init)
+    init_step = 1
+    
+    # Configure file path for the model
+    state_fpath = models_dir.joinpath(run_id + ".pt")
+    backup_dir = models_dir.joinpath(run_id + "_backups")
+
+    # Load any existing model
+    if not force_restart:
+        if state_fpath.exists():
+            print("Found existing model \"%s\", loading it and resuming training." % run_id)
+            checkpoint = torch.load(state_fpath)
+            init_step = checkpoint["step"]
+            model.load_state_dict(checkpoint["model_state"])
+            optimizer.load_state_dict(checkpoint["optimizer_state"])
+            optimizer.param_groups[0]["lr"] = learning_rate_init
+        else:
+            print("No model \"%s\" found, starting training from scratch." % run_id)
+    else:
+        print("Starting the training from scratch.")
+    model.train()
+    
+    # Initialize the visualization environment
+    vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom)
+    vis.log_dataset(dataset)
+    vis.log_params()
+    device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU")
+    vis.log_implementation({"Device": device_name})
+    
+    # Training loop
+    profiler = Profiler(summarize_every=10, disabled=False)
+    for step, speaker_batch in enumerate(loader, init_step):
+        profiler.tick("Blocking, waiting for batch (threaded)")
+        
+        # Forward pass
+        inputs = torch.from_numpy(speaker_batch.data).to(device)
+        sync(device)
+        profiler.tick("Data to %s" % device)
+        embeds = model(inputs)
+        sync(device)
+        profiler.tick("Forward pass")
+        embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device)
+        loss, eer = model.loss(embeds_loss)
+        sync(loss_device)
+        profiler.tick("Loss")
+
+        # Backward pass
+        model.zero_grad()
+        loss.backward()
+        profiler.tick("Backward pass")
+        model.do_gradient_ops()
+        optimizer.step()
+        profiler.tick("Parameter update")
+        
+        # Update visualizations
+        # learning_rate = optimizer.param_groups[0]["lr"]
+        vis.update(loss.item(), eer, step)
+        
+        # Draw projections and save them to the backup folder
+        if umap_every != 0 and step % umap_every == 0:
+            print("Drawing and saving projections (step %d)" % step)
+            backup_dir.mkdir(exist_ok=True)
+            projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step))
+            embeds = embeds.detach().cpu().numpy()
+            vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath)
+            vis.save()
+
+        # Overwrite the latest version of the model
+        if save_every != 0 and step % save_every == 0:
+            print("Saving the model (step %d)" % step)
+            torch.save({
+                "step": step + 1,
+                "model_state": model.state_dict(),
+                "optimizer_state": optimizer.state_dict(),
+            }, state_fpath)
+            
+        # Make a backup
+        if backup_every != 0 and step % backup_every == 0:
+            print("Making a backup (step %d)" % step)
+            backup_dir.mkdir(exist_ok=True)
+            backup_fpath = backup_dir.joinpath("%s_bak_%06d.pt" % (run_id, step))
+            torch.save({
+                "step": step + 1,
+                "model_state": model.state_dict(),
+                "optimizer_state": optimizer.state_dict(),
+            }, backup_fpath)
+            
+        profiler.tick("Extras (visualizations, saving)")
+        

speaker_encoder/speaker_encoder_visualizations.py

@@ -0,0 +1,178 @@
+from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
+from datetime import datetime
+from time import perf_counter as timer
+import matplotlib.pyplot as plt
+import numpy as np
+# import webbrowser
+import visdom
+import umap
+
+colormap = np.array([
+    [76, 255, 0],
+    [0, 127, 70],
+    [255, 0, 0],
+    [255, 217, 38],
+    [0, 135, 255],
+    [165, 0, 165],
+    [255, 167, 255],
+    [0, 255, 255],
+    [255, 96, 38],
+    [142, 76, 0],
+    [33, 0, 127],
+    [0, 0, 0],
+    [183, 183, 183],
+], dtype=np.float) / 255 
+
+
+class Visualizations:
+    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
+        # Tracking data
+        self.last_update_timestamp = timer()
+        self.update_every = update_every
+        self.step_times = []
+        self.losses = []
+        self.eers = []
+        print("Updating the visualizations every %d steps." % update_every)
+        
+        # If visdom is disabled TODO: use a better paradigm for that
+        self.disabled = disabled    
+        if self.disabled:
+            return 
+        
+        # Set the environment name
+        now = str(datetime.now().strftime("%d-%m %Hh%M"))
+        if env_name is None:
+            self.env_name = now
+        else:
+            self.env_name = "%s (%s)" % (env_name, now)
+        
+        # Connect to visdom and open the corresponding window in the browser
+        try:
+            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
+        except ConnectionError:
+            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
+                            "start it.")
+        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
+        
+        # Create the windows
+        self.loss_win = None
+        self.eer_win = None
+        # self.lr_win = None
+        self.implementation_win = None
+        self.projection_win = None
+        self.implementation_string = ""
+        
+    def log_params(self):
+        if self.disabled:
+            return 
+        from speaker_encoder import params_data
+        from speaker_encoder import params_model
+        param_string = "<b>Model parameters</b>:<br>"
+        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
+            value = getattr(params_model, param_name)
+            param_string += "\t%s: %s<br>" % (param_name, value)
+        param_string += "<b>Data parameters</b>:<br>"
+        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
+            value = getattr(params_data, param_name)
+            param_string += "\t%s: %s<br>" % (param_name, value)
+        self.vis.text(param_string, opts={"title": "Parameters"})
+        
+    def log_dataset(self, dataset: SpeakerVerificationDataset):
+        if self.disabled:
+            return 
+        dataset_string = ""
+        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
+        dataset_string += "\n" + dataset.get_logs()
+        dataset_string = dataset_string.replace("\n", "<br>")
+        self.vis.text(dataset_string, opts={"title": "Dataset"})
+        
+    def log_implementation(self, params):
+        if self.disabled:
+            return 
+        implementation_string = ""
+        for param, value in params.items():
+            implementation_string += "<b>%s</b>: %s\n" % (param, value)
+            implementation_string = implementation_string.replace("\n", "<br>")
+        self.implementation_string = implementation_string
+        self.implementation_win = self.vis.text(
+            implementation_string, 
+            opts={"title": "Training implementation"}
+        )
+
+    def update(self, loss, eer, step):
+        # Update the tracking data
+        now = timer()
+        self.step_times.append(1000 * (now - self.last_update_timestamp))
+        self.last_update_timestamp = now
+        self.losses.append(loss)
+        self.eers.append(eer)
+        print(".", end="")
+        
+        # Update the plots every <update_every> steps
+        if step % self.update_every != 0:
+            return
+        time_string = "Step time:  mean: %5dms  std: %5dms" % \
+                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
+        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
+              (step, np.mean(self.losses), np.mean(self.eers), time_string))
+        if not self.disabled:
+            self.loss_win = self.vis.line(
+                [np.mean(self.losses)],
+                [step],
+                win=self.loss_win,
+                update="append" if self.loss_win else None,
+                opts=dict(
+                    legend=["Avg. loss"],
+                    xlabel="Step",
+                    ylabel="Loss",
+                    title="Loss",
+                )
+            )
+            self.eer_win = self.vis.line(
+                [np.mean(self.eers)],
+                [step],
+                win=self.eer_win,
+                update="append" if self.eer_win else None,
+                opts=dict(
+                    legend=["Avg. EER"],
+                    xlabel="Step",
+                    ylabel="EER",
+                    title="Equal error rate"
+                )
+            )
+            if self.implementation_win is not None:
+                self.vis.text(
+                    self.implementation_string + ("<b>%s</b>" % time_string), 
+                    win=self.implementation_win,
+                    opts={"title": "Training implementation"},
+                )
+
+        # Reset the tracking
+        self.losses.clear()
+        self.eers.clear()
+        self.step_times.clear()
+        
+    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
+                         max_speakers=10):
+        max_speakers = min(max_speakers, len(colormap))
+        embeds = embeds[:max_speakers * utterances_per_speaker]
+        
+        n_speakers = len(embeds) // utterances_per_speaker
+        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
+        colors = [colormap[i] for i in ground_truth]
+        
+        reducer = umap.UMAP()
+        projected = reducer.fit_transform(embeds)
+        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
+        plt.gca().set_aspect("equal", "datalim")
+        plt.title("UMAP projection (step %d)" % step)
+        if not self.disabled:
+            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
+        if out_fpath is not None:
+            plt.savefig(out_fpath)
+        plt.clf()
+        
+    def save(self):
+        if not self.disabled:
+            self.vis.save([self.env_name])
+        

speaker_encoder/speaker_encoder_voice_encoder.py

@@ -0,0 +1,173 @@
+from speaker_encoder.hparams import *
+from speaker_encoder import audio
+from pathlib import Path
+from typing import Union, List
+from torch import nn
+from time import perf_counter as timer
+import numpy as np
+import torch
+
+
+class SpeakerEncoder(nn.Module):
+    def __init__(self, weights_fpath, device: Union[str, torch.device]=None, verbose=True):
+        """
+        :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). 
+        If None, defaults to cuda if it is available on your machine, otherwise the model will 
+        run on cpu. Outputs are always returned on the cpu, as numpy arrays.
+        """
+        super().__init__()
+        
+        # Define the network
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+        
+        # Get the target device
+        if device is None:
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        elif isinstance(device, str):
+            device = torch.device(device)
+        self.device = device
+            
+        # Load the pretrained model'speaker weights
+        # weights_fpath = Path(__file__).resolve().parent.joinpath("pretrained.pt")
+        # if not weights_fpath.exists():
+        #     raise Exception("Couldn't find the voice encoder pretrained model at %s." % 
+        #                     weights_fpath)
+
+        start = timer()
+        checkpoint = torch.load(weights_fpath, map_location="cpu")
+
+        self.load_state_dict(checkpoint["model_state"], strict=False)
+        self.to(device)
+        
+        if verbose:
+            print("Loaded the voice encoder model on %s in %.2f seconds." % 
+                  (device.type, timer() - start))
+
+    def forward(self, mels: torch.FloatTensor):
+        """
+        Computes the embeddings of a batch of utterance spectrograms.
+        :param mels: a batch of mel spectrograms of same duration as a float32 tensor of shape 
+        (batch_size, n_frames, n_channels) 
+        :return: the embeddings as a float 32 tensor of shape (batch_size, embedding_size). 
+        Embeddings are positive and L2-normed, thus they lay in the range [0, 1].
+        """
+        # Pass the input through the LSTM layers and retrieve the final hidden state of the last 
+        # layer. Apply a cutoff to 0 for negative values and L2 normalize the embeddings.
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+    
+    @staticmethod
+    def compute_partial_slices(n_samples: int, rate, min_coverage):
+        """
+        Computes where to split an utterance waveform and its corresponding mel spectrogram to 
+        obtain partial utterances of <partials_n_frames> each. Both the waveform and the 
+        mel spectrogram slices are returned, so as to make each partial utterance waveform 
+        correspond to its spectrogram.
+    
+        The returned ranges may be indexing further than the length of the waveform. It is 
+        recommended that you pad the waveform with zeros up to wav_slices[-1].stop.
+    
+        :param n_samples: the number of samples in the waveform
+        :param rate: how many partial utterances should occur per second. Partial utterances must 
+        cover the span of the entire utterance, thus the rate should not be lower than the inverse 
+        of the duration of a partial utterance. By default, partial utterances are 1.6s long and 
+        the minimum rate is thus 0.625.
+        :param min_coverage: when reaching the last partial utterance, it may or may not have 
+        enough frames. If at least <min_pad_coverage> of <partials_n_frames> are present, 
+        then the last partial utterance will be considered by zero-padding the audio. Otherwise, 
+        it will be discarded. If there aren't enough frames for one partial utterance, 
+        this parameter is ignored so that the function always returns at least one slice.
+        :return: the waveform slices and mel spectrogram slices as lists of array slices. Index 
+        respectively the waveform and the mel spectrogram with these slices to obtain the partial 
+        utterances.
+        """
+        assert 0 < min_coverage <= 1
+        
+        # Compute how many frames separate two partial utterances
+        samples_per_frame = int((sampling_rate * mel_window_step / 1000))
+        n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
+        frame_step = int(np.round((sampling_rate / rate) / samples_per_frame))
+        assert 0 < frame_step, "The rate is too high"
+        assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % \
+            (sampling_rate / (samples_per_frame * partials_n_frames))
+        
+        # Compute the slices
+        wav_slices, mel_slices = [], []
+        steps = max(1, n_frames - partials_n_frames + frame_step + 1)
+        for i in range(0, steps, frame_step):
+            mel_range = np.array([i, i + partials_n_frames])
+            wav_range = mel_range * samples_per_frame
+            mel_slices.append(slice(*mel_range))
+            wav_slices.append(slice(*wav_range))
+        
+        # Evaluate whether extra padding is warranted or not
+        last_wav_range = wav_slices[-1]
+        coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
+        if coverage < min_coverage and len(mel_slices) > 1:
+            mel_slices = mel_slices[:-1]
+            wav_slices = wav_slices[:-1]
+        
+        return wav_slices, mel_slices
+    
+    def embed_utterance(self, wav: np.ndarray, return_partials=False, rate=1.3, min_coverage=0.75):
+        """
+        Computes an embedding for a single utterance. The utterance is divided in partial 
+        utterances and an embedding is computed for each. The complete utterance embedding is the 
+        L2-normed average embedding of the partial utterances.
+        
+        TODO: independent batched version of this function
+    
+        :param wav: a preprocessed utterance waveform as a numpy array of float32
+        :param return_partials: if True, the partial embeddings will also be returned along with 
+        the wav slices corresponding to each partial utterance.
+        :param rate: how many partial utterances should occur per second. Partial utterances must 
+        cover the span of the entire utterance, thus the rate should not be lower than the inverse 
+        of the duration of a partial utterance. By default, partial utterances are 1.6s long and 
+        the minimum rate is thus 0.625.
+        :param min_coverage: when reaching the last partial utterance, it may or may not have 
+        enough frames. If at least <min_pad_coverage> of <partials_n_frames> are present, 
+        then the last partial utterance will be considered by zero-padding the audio. Otherwise, 
+        it will be discarded. If there aren't enough frames for one partial utterance, 
+        this parameter is ignored so that the function always returns at least one slice.
+        :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If 
+        <return_partials> is True, the partial utterances as a numpy array of float32 of shape 
+        (n_partials, model_embedding_size) and the wav partials as a list of slices will also be 
+        returned.
+        """
+        # Compute where to split the utterance into partials and pad the waveform with zeros if 
+        # the partial utterances cover a larger range. 
+        wav_slices, mel_slices = self.compute_partial_slices(len(wav), rate, min_coverage)
+        max_wave_length = wav_slices[-1].stop
+        if max_wave_length >= len(wav):
+            wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
+        
+        # Split the utterance into partials and forward them through the model
+        mel = audio.wav_to_mel_spectrogram(wav)
+        mels = np.array([mel[s] for s in mel_slices])
+        with torch.no_grad():
+            mels = torch.from_numpy(mels).to(self.device)
+            partial_embeds = self(mels).cpu().numpy()
+        
+        # Compute the utterance embedding from the partial embeddings
+        raw_embed = np.mean(partial_embeds, axis=0)
+        embed = raw_embed / np.linalg.norm(raw_embed, 2)
+        
+        if return_partials:
+            return embed, partial_embeds, wav_slices
+        return embed
+    
+    def embed_speaker(self, wavs: List[np.ndarray], **kwargs):
+        """
+        Compute the embedding of a collection of wavs (presumably from the same speaker) by 
+        averaging their embedding and L2-normalizing it.
+        
+        :param wavs: list of wavs a numpy arrays of float32.
+        :param kwargs: extra arguments to embed_utterance()
+        :return: the embedding as a numpy array of float32 of shape (model_embedding_size,).
+        """
+        raw_embed = np.mean([self.embed_utterance(wav, return_partials=False, **kwargs) \
+                             for wav in wavs], axis=0)
+        return raw_embed / np.linalg.norm(raw_embed, 2)

utils.py

@@ -0,0 +1,305 @@
+import os
+import sys
+import argparse
+import logging
+import json
+import subprocess
+import numpy as np
+from scipy.io.wavfile import read
+import torch
+from torch.nn import functional as F
+from commons import sequence_mask
+
+MATPLOTLIB_FLAG = False
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logger = logging
+
+
+def get_cmodel(rank):
+    checkpoint = torch.load('wavlm/WavLM-Large.pt')
+    cfg = WavLMConfig(checkpoint['cfg'])
+    cmodel = WavLM(cfg).cuda(rank)
+    cmodel.load_state_dict(checkpoint['model'])
+    cmodel.eval()
+    return cmodel
+    
+    
+def get_content(cmodel, y):
+    with torch.no_grad():
+        c = cmodel.extract_features(y.squeeze(1))[0]
+    c = c.transpose(1, 2)
+    return c
+
+
+def get_vocoder(rank):
+    with open("hifigan/config.json", "r") as f:
+        config = json.load(f)
+    config = hifigan.AttrDict(config)
+    vocoder = hifigan.Generator(config)
+    ckpt = torch.load("hifigan/generator_v1")
+    vocoder.load_state_dict(ckpt["generator"])
+    vocoder.eval()
+    vocoder.remove_weight_norm()
+    vocoder.cuda(rank)
+    return vocoder
+    
+    
+def transform(mel, height): # 68-92
+    #r = np.random.random()
+    #rate = r * 0.3 + 0.85 # 0.85-1.15
+    #height = int(mel.size(-2) * rate)
+    tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
+    if height >= mel.size(-2):
+        return tgt[:, :mel.size(-2), :]
+    else:
+        silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1) 
+        silence += torch.randn_like(silence) / 10
+        return torch.cat((tgt, silence), 1)
+        
+        
+def stretch(mel, width): # 0.5-2
+    return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None):
+  assert os.path.isfile(checkpoint_path)
+  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+  iteration = checkpoint_dict['iteration']
+  learning_rate = checkpoint_dict['learning_rate']
+  if optimizer is not None:
+    optimizer.load_state_dict(checkpoint_dict['optimizer'])
+  saved_state_dict = checkpoint_dict['model']
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  new_state_dict= {}
+  for k, v in state_dict.items():
+    try:
+      new_state_dict[k] = saved_state_dict[k]
+    except:
+      logger.info("%s is not in the checkpoint" % k)
+      new_state_dict[k] = v
+  if hasattr(model, 'module'):
+    model.module.load_state_dict(new_state_dict)
+  else:
+    model.load_state_dict(new_state_dict)
+  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
+    checkpoint_path, iteration))
+  return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+  logger.info("Saving model and optimizer state at iteration {} to {}".format(
+    iteration, checkpoint_path))
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  torch.save({'model': state_dict,
+              'iteration': iteration,
+              'optimizer': optimizer.state_dict(),
+              'learning_rate': learning_rate}, checkpoint_path)
+
+
+def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
+  for k, v in scalars.items():
+    writer.add_scalar(k, v, global_step)
+  for k, v in histograms.items():
+    writer.add_histogram(k, v, global_step)
+  for k, v in images.items():
+    writer.add_image(k, v, global_step, dataformats='HWC')
+  for k, v in audios.items():
+    writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+  f_list = glob.glob(os.path.join(dir_path, regex))
+  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+  x = f_list[-1]
+  print(x)
+  return x
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+  
+  fig, ax = plt.subplots(figsize=(10,2))
+  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                  interpolation='none')
+  plt.colorbar(im, ax=ax)
+  plt.xlabel("Frames")
+  plt.ylabel("Channels")
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+
+  fig, ax = plt.subplots(figsize=(6, 4))
+  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+                  interpolation='none')
+  fig.colorbar(im, ax=ax)
+  xlabel = 'Decoder timestep'
+  if info is not None:
+      xlabel += '\n\n' + info
+  plt.xlabel(xlabel)
+  plt.ylabel('Encoder timestep')
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def load_wav_to_torch(full_path):
+  sampling_rate, data = read(full_path)
+  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(filename, split="|"):
+  with open(filename, encoding='utf-8') as f:
+    filepaths_and_text = [line.strip().split(split) for line in f]
+  return filepaths_and_text
+
+
+def get_hparams(init=True):
+  parser = argparse.ArgumentParser()
+  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
+                      help='JSON file for configuration')
+  parser.add_argument('-m', '--model', type=str, required=True,
+                      help='Model name')
+  
+  args = parser.parse_args()
+  model_dir = os.path.join("./logs", args.model)
+
+  if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+
+  config_path = args.config
+  config_save_path = os.path.join(model_dir, "config.json")
+  if init:
+    with open(config_path, "r") as f:
+      data = f.read()
+    with open(config_save_path, "w") as f:
+      f.write(data)
+  else:
+    with open(config_save_path, "r") as f:
+      data = f.read()
+  config = json.loads(data)
+  
+  hparams = HParams(**config)
+  hparams.model_dir = model_dir
+  return hparams
+
+
+def get_hparams_from_dir(model_dir):
+  config_save_path = os.path.join(model_dir, "config.json")
+  with open(config_save_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  hparams.model_dir = model_dir
+  return hparams
+
+
+def get_hparams_from_file(config_path):
+  with open(config_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  return hparams
+
+
+def check_git_hash(model_dir):
+  source_dir = os.path.dirname(os.path.realpath(__file__))
+  if not os.path.exists(os.path.join(source_dir, ".git")):
+    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
+      source_dir
+    ))
+    return
+
+  cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+  path = os.path.join(model_dir, "githash")
+  if os.path.exists(path):
+    saved_hash = open(path).read()
+    if saved_hash != cur_hash:
+      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
+        saved_hash[:8], cur_hash[:8]))
+  else:
+    open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+  global logger
+  logger = logging.getLogger(os.path.basename(model_dir))
+  logger.setLevel(logging.DEBUG)
+  
+  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+  if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+  h = logging.FileHandler(os.path.join(model_dir, filename))
+  h.setLevel(logging.DEBUG)
+  h.setFormatter(formatter)
+  logger.addHandler(h)
+  return logger
+
+
+class HParams():
+  def __init__(self, **kwargs):
+    for k, v in kwargs.items():
+      if type(v) == dict:
+        v = HParams(**v)
+      self[k] = v
+    
+  def keys(self):
+    return self.__dict__.keys()
+
+  def items(self):
+    return self.__dict__.items()
+
+  def values(self):
+    return self.__dict__.values()
+
+  def __len__(self):
+    return len(self.__dict__)
+
+  def __getitem__(self, key):
+    return getattr(self, key)
+
+  def __setitem__(self, key, value):
+    return setattr(self, key, value)
+
+  def __contains__(self, key):
+    return key in self.__dict__
+
+  def __repr__(self):
+    return self.__dict__.__repr__()