actually do speaker verification
This commit is contained in:
parent
c2e17e287b
commit
c2c6d912ac
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@ -136,7 +136,7 @@ def main():
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parser.add_argument("--comparison", type=str, default=None)
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parser.add_argument("--transcription-model", type=str, default="openai/whisper-base")
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parser.add_argument("--speaker-similarity-model", type=str, default="microsoft/wavlm-base-sv")
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parser.add_argument("--speaker-similarity-model", type=str, default="microsoft/wavlm-large")
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args = parser.parse_args()
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@ -28,6 +28,8 @@ from ..utils.io import json_read, json_write
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from .g2p import encode as phonemize
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from .qnt import encode as quantize, trim, convert_audio
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from ..models import download_model
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from ..webui import init_tts
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def load_audio( path, target_sr=None ):
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@ -46,20 +48,40 @@ tts = None
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# this is for computing SIM-O, but can probably technically be used for scoring similar utterances
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@cache
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def _load_sim_model(device="cuda", dtype="float16", model_name='microsoft/wavlm-base-sv'):
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def _load_sim_model(device="cuda", dtype="float16", model_name='microsoft/wavlm-large'):
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from ..utils.ext.ecapa_tdnn import ECAPA_TDNN_SMALL
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model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type='wavlm_large')
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finetune_path = Path("./data/models/wavlm_large_finetune.pth")
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if not finetune_path.exists():
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download_model(finetune_path)
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state_dict = torch.load( finetune_path )
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state_dict = state_dict['model']
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del state_dict['loss_calculator.projection.weight']
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model.load_state_dict( state_dict )
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model = model.to(device=device, dtype=coerce_dtype(dtype))
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model = model.eval()
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return model, None
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"""
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logging.getLogger('s3prl').setLevel(logging.DEBUG)
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logging.getLogger('speechbrain').setLevel(logging.DEBUG)
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#from ..utils.ext.ecapa_tdnn import ECAPA_TDNN_SMALL
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from transformers import Wav2Vec2FeatureExtractor, WavLMForXVector
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model = WavLMForXVector.from_pretrained(model_name)
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finetune_path = Path("./data/models/wavlm_large_finetune.pth")
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if finetune_path.exists():
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state_dict = torch.load( finetune_path )
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model.load_state_dict( state_dict['model'] )
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model = model.to(device=device, dtype=coerce_dtype(dtype))
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model = model.eval()
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
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return model, feature_extractor
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"""
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@torch.no_grad()
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def speaker_similarity_embedding(
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@ -75,12 +97,15 @@ def speaker_similarity_embedding(
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audio = load_audio(audio, 16000)
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audio, sr = audio
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embeddings = model(audio.to(device=device, dtype=coerce_dtype(dtype)))
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"""
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features = feature_extractor(audio, return_tensors="pt", sampling_rate=sr)
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features = features.input_values.squeeze(0).to(dtype=coerce_dtype(dtype), device=device)
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output = model(input_values=features)
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embeddings = output.embeddings
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embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
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"""
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return embeddings
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def batch_similar_utterances(
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@ -13,6 +13,7 @@ DEFAULT_MODEL_DIR = Path(__file__).parent.parent.parent / 'data/models'
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DEFAULT_MODEL_PATH = DEFAULT_MODEL_DIR / "ar+nar-len-llama-8.sft"
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DEFAULT_MODEL_URLS = {
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'ar+nar-len-llama-8.sft': 'https://huggingface.co/ecker/vall-e/resolve/main/models/ckpt/ar%2Bnar-len-llama-8/ckpt/fp32.sft',
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'wavlm_large_finetune.pth': 'https://huggingface.co/Dongchao/UniAudio/resolve/main/wavlm_large_finetune.pth',
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}
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if not DEFAULT_MODEL_PATH.exists() and Path("./data/models/ar+nar-len-llama-8.sft").exists():
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467
vall_e/utils/ext/ecapa_tdnn.py
Normal file
467
vall_e/utils/ext/ecapa_tdnn.py
Normal file
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@ -0,0 +1,467 @@
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# borrowed with love from "https://github.com/keonlee9420/evaluate-zero-shot-tts/blob/master/src/evaluate_zero_shot_tts/utils/speaker_verification/models/ecapa_tdnn.py"
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# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio.transforms as trans
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#from .utils import UpstreamExpert
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""" Res2Conv1d + BatchNorm1d + ReLU
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"""
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class Res2Conv1dReluBn(nn.Module):
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"""
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in_channels == out_channels == channels
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"""
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def __init__(
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self,
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channels,
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kernel_size=1,
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stride=1,
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padding=0,
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dilation=1,
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bias=True,
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scale=4,
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):
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super().__init__()
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assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
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self.scale = scale
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self.width = channels // scale
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self.nums = scale if scale == 1 else scale - 1
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self.convs = []
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self.bns = []
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for i in range(self.nums):
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self.convs.append(
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nn.Conv1d(
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self.width,
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self.width,
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kernel_size,
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stride,
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padding,
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dilation,
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bias=bias,
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)
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)
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self.bns.append(nn.BatchNorm1d(self.width))
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self.convs = nn.ModuleList(self.convs)
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self.bns = nn.ModuleList(self.bns)
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def forward(self, x):
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out = []
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spx = torch.split(x, self.width, 1)
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for i in range(self.nums):
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if i == 0:
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sp = spx[i]
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else:
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sp = sp + spx[i]
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# Order: conv -> relu -> bn
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sp = self.convs[i](sp)
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sp = self.bns[i](F.relu(sp))
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out.append(sp)
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if self.scale != 1:
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out.append(spx[self.nums])
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out = torch.cat(out, dim=1)
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return out
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""" Conv1d + BatchNorm1d + ReLU
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"""
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class Conv1dReluBn(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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dilation=1,
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bias=True,
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):
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super().__init__()
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self.conv = nn.Conv1d(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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bias=bias,
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)
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self.bn = nn.BatchNorm1d(out_channels)
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def forward(self, x):
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return self.bn(F.relu(self.conv(x)))
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""" The SE connection of 1D case.
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"""
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class SE_Connect(nn.Module):
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def __init__(self, channels, se_bottleneck_dim=128):
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super().__init__()
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self.linear1 = nn.Linear(channels, se_bottleneck_dim)
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self.linear2 = nn.Linear(se_bottleneck_dim, channels)
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def forward(self, x):
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out = x.mean(dim=2)
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out = F.relu(self.linear1(out))
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out = torch.sigmoid(self.linear2(out))
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out = x * out.unsqueeze(2)
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return out
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""" SE-Res2Block of the ECAPA-TDNN architecture.
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"""
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# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
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# return nn.Sequential(
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# Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
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# Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
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# Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
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# SE_Connect(channels)
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# )
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class SE_Res2Block(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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scale,
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se_bottleneck_dim,
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):
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super().__init__()
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self.Conv1dReluBn1 = Conv1dReluBn(
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in_channels, out_channels, kernel_size=1, stride=1, padding=0
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)
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self.Res2Conv1dReluBn = Res2Conv1dReluBn(
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out_channels, kernel_size, stride, padding, dilation, scale=scale
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)
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self.Conv1dReluBn2 = Conv1dReluBn(
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out_channels, out_channels, kernel_size=1, stride=1, padding=0
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)
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self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
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self.shortcut = None
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if in_channels != out_channels:
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self.shortcut = nn.Conv1d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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)
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def forward(self, x):
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residual = x
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if self.shortcut:
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residual = self.shortcut(x)
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x = self.Conv1dReluBn1(x)
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x = self.Res2Conv1dReluBn(x)
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x = self.Conv1dReluBn2(x)
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x = self.SE_Connect(x)
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return x + residual
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""" Attentive weighted mean and standard deviation pooling.
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"""
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class AttentiveStatsPool(nn.Module):
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def __init__(
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self, in_dim, attention_channels=128, global_context_att=False
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):
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super().__init__()
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self.global_context_att = global_context_att
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# Use Conv1d with stride == 1 rather than Linear, then we don't need to transpose inputs.
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if global_context_att:
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self.linear1 = nn.Conv1d(
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in_dim * 3, attention_channels, kernel_size=1
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) # equals W and b in the paper
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else:
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self.linear1 = nn.Conv1d(
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in_dim, attention_channels, kernel_size=1
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) # equals W and b in the paper
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self.linear2 = nn.Conv1d(
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attention_channels, in_dim, kernel_size=1
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) # equals V and k in the paper
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def forward(self, x):
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if self.global_context_att:
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context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
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context_std = torch.sqrt(
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torch.var(x, dim=-1, keepdim=True) + 1e-10
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).expand_as(x)
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x_in = torch.cat((x, context_mean, context_std), dim=1)
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else:
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x_in = x
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# DON'T use ReLU here! In experiments, I find ReLU hard to converge.
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alpha = torch.tanh(self.linear1(x_in))
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# alpha = F.relu(self.linear1(x_in))
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alpha = torch.softmax(self.linear2(alpha), dim=2)
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mean = torch.sum(alpha * x, dim=2)
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residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
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std = torch.sqrt(residuals.clamp(min=1e-9))
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return torch.cat([mean, std], dim=1)
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class ECAPA_TDNN(nn.Module):
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def __init__(
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self,
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feat_dim=80,
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channels=512,
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emb_dim=192,
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global_context_att=False,
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feat_type="fbank",
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sr=16000,
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feature_selection="hidden_states",
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update_extract=False,
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config_path=None,
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):
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super().__init__()
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self.feat_type = feat_type
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self.feature_selection = feature_selection
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self.update_extract = update_extract
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self.sr = sr
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if feat_type == "fbank" or feat_type == "mfcc":
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self.update_extract = False
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win_len = int(sr * 0.025)
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hop_len = int(sr * 0.01)
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if feat_type == "fbank":
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self.feature_extract = trans.MelSpectrogram(
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sample_rate=sr,
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n_fft=512,
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win_length=win_len,
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hop_length=hop_len,
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f_min=0.0,
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f_max=sr // 2,
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pad=0,
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n_mels=feat_dim,
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)
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elif feat_type == "mfcc":
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melkwargs = {
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"n_fft": 512,
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"win_length": win_len,
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"hop_length": hop_len,
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"f_min": 0.0,
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"f_max": sr // 2,
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"pad": 0,
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}
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self.feature_extract = trans.MFCC(
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sample_rate=sr,
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n_mfcc=feat_dim,
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log_mels=False,
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melkwargs=melkwargs,
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)
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else:
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"""
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if config_path is None:
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self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
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else:
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self.feature_extract = UpstreamExpert(config_path)
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"""
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self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
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if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
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self.feature_extract.model.encoder.layers[23].self_attn,
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"fp32_attention",
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):
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self.feature_extract.model.encoder.layers[
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23
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].self_attn.fp32_attention = False
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if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
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self.feature_extract.model.encoder.layers[11].self_attn,
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"fp32_attention",
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):
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self.feature_extract.model.encoder.layers[
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11
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].self_attn.fp32_attention = False
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self.feat_num = self.get_feat_num()
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self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
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if feat_type != "fbank" and feat_type != "mfcc":
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freeze_list = [
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"final_proj",
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"label_embs_concat",
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"mask_emb",
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"project_q",
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"quantizer",
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]
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for name, param in self.feature_extract.named_parameters():
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for freeze_val in freeze_list:
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if freeze_val in name:
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param.requires_grad = False
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break
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if not self.update_extract:
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for param in self.feature_extract.parameters():
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param.requires_grad = False
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self.instance_norm = nn.InstanceNorm1d(feat_dim)
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# self.channels = [channels] * 4 + [channels * 3]
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self.channels = [channels] * 4 + [1536]
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self.layer1 = Conv1dReluBn(
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feat_dim, self.channels[0], kernel_size=5, padding=2
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)
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self.layer2 = SE_Res2Block(
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self.channels[0],
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self.channels[1],
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kernel_size=3,
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stride=1,
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padding=2,
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dilation=2,
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scale=8,
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se_bottleneck_dim=128,
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)
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self.layer3 = SE_Res2Block(
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self.channels[1],
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self.channels[2],
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kernel_size=3,
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stride=1,
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padding=3,
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dilation=3,
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scale=8,
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se_bottleneck_dim=128,
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)
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self.layer4 = SE_Res2Block(
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self.channels[2],
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self.channels[3],
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kernel_size=3,
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stride=1,
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padding=4,
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dilation=4,
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scale=8,
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se_bottleneck_dim=128,
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)
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# self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
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cat_channels = channels * 3
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self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
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self.pooling = AttentiveStatsPool(
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self.channels[-1],
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attention_channels=128,
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global_context_att=global_context_att,
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)
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self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
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self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
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def get_feat_num(self):
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self.feature_extract.eval()
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wav = [
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torch.randn(self.sr).to(
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next(self.feature_extract.parameters()).device
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)
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]
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with torch.no_grad():
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features = self.feature_extract(wav)
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select_feature = features[self.feature_selection]
|
||||
if isinstance(select_feature, (list, tuple)):
|
||||
return len(select_feature)
|
||||
else:
|
||||
return 1
|
||||
|
||||
def get_feat(self, x):
|
||||
if self.update_extract:
|
||||
x = self.feature_extract([sample for sample in x])
|
||||
else:
|
||||
with torch.no_grad():
|
||||
if self.feat_type == "fbank" or self.feat_type == "mfcc":
|
||||
x = (
|
||||
self.feature_extract(x) + 1e-6
|
||||
) # B x feat_dim x time_len
|
||||
else:
|
||||
x = self.feature_extract([sample for sample in x])
|
||||
|
||||
if self.feat_type == "fbank":
|
||||
x = x.log()
|
||||
|
||||
if self.feat_type != "fbank" and self.feat_type != "mfcc":
|
||||
x = x[self.feature_selection]
|
||||
if isinstance(x, (list, tuple)):
|
||||
x = torch.stack(x, dim=0)
|
||||
else:
|
||||
x = x.unsqueeze(0)
|
||||
norm_weights = (
|
||||
F.softmax(self.feature_weight, dim=-1)
|
||||
.unsqueeze(-1)
|
||||
.unsqueeze(-1)
|
||||
.unsqueeze(-1)
|
||||
)
|
||||
x = (norm_weights * x).sum(dim=0)
|
||||
x = torch.transpose(x, 1, 2) + 1e-6
|
||||
|
||||
x = self.instance_norm(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.get_feat(x)
|
||||
|
||||
out1 = self.layer1(x)
|
||||
out2 = self.layer2(out1)
|
||||
out3 = self.layer3(out2)
|
||||
out4 = self.layer4(out3)
|
||||
|
||||
out = torch.cat([out2, out3, out4], dim=1)
|
||||
out = F.relu(self.conv(out))
|
||||
out = self.bn(self.pooling(out))
|
||||
out = self.linear(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def ECAPA_TDNN_SMALL(
|
||||
feat_dim,
|
||||
emb_dim=256,
|
||||
feat_type="fbank",
|
||||
sr=16000,
|
||||
feature_selection="hidden_states",
|
||||
update_extract=False,
|
||||
config_path=None,
|
||||
):
|
||||
return ECAPA_TDNN(
|
||||
feat_dim=feat_dim,
|
||||
channels=512,
|
||||
emb_dim=emb_dim,
|
||||
feat_type=feat_type,
|
||||
sr=sr,
|
||||
feature_selection=feature_selection,
|
||||
update_extract=update_extract,
|
||||
config_path=config_path,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = torch.zeros(2, 32000)
|
||||
model = ECAPA_TDNN_SMALL(
|
||||
feat_dim=768,
|
||||
emb_dim=256,
|
||||
feat_type="hubert_base",
|
||||
feature_selection="hidden_states",
|
||||
update_extract=False,
|
||||
)
|
||||
|
||||
out = model(x)
|
||||
# print(model)
|
||||
print(out.shape)
|
||||
Loading…
Reference in New Issue
Block a user