Repository: KakaruHayate/ColorSplitter
Branch: main
Commit: 6104d4a170e3
Files: 38
Total size: 16.4 MB
Directory structure:
gitextract_5gn6sueo/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── README_CN.md
├── clean_csv.py
├── kick.py
├── load_npy.py
├── modules/
│ ├── cluster.py
│ ├── model/
│ │ ├── emotion_encoder.py
│ │ └── voice_encoder.py
│ ├── utils.py
│ └── visualizations.py
├── move_files.py
├── pretrain/
│ ├── encoder_1570000.bak
│ └── wav2vec2-large-robust-12-ft-emotion-msp-dim/
│ ├── LICENSE
│ ├── config.json
│ ├── preprocessor_config.json
│ └── vocab.json
├── requirements.txt
├── splitter.py
└── viewer/
├── .gitignore
├── README.md
├── bun.lockb
├── eslint.config.js
├── index.html
├── package.json
├── postcss.config.js
├── src/
│ ├── App.tsx
│ ├── ScatterPlot.tsx
│ ├── index.css
│ ├── main.tsx
│ └── vite-env.d.ts
├── tailwind.config.js
├── tsconfig.app.json
├── tsconfig.json
├── tsconfig.node.json
└── vite.config.ts
================================================
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================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2023 KakaruHayate
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# ColorSplitter
[中文文档](README_CN.md)
[webui](https://github.com/KakaruHayate/ColorSplitter/tree/main/viewer)
A command-line tool for separating vocal timbres
# Introduction
ColorSplitter is a command-line tool for classifying the vocal timbre styles of single-speaker data in the pre-processing stage of vocal data.
For scenarios that do not require style classification, using this tool to filter data can also reduce the problem of unstable timbre performance of the model.
**Please note** that this project is based on Speaker Verification technology, and it is not clear whether the timbre changes of singing are completely related to the voiceprint differences, just for fun :)
The research in this field is still scarce, hoping to inspire more ideas.
Thanks to the community user: 洛泠羽
# New version features
Implemented automatic optimization of clustering results, no longer need users to judge the optimal clustering results themselves.
`splitter.py` deleted the `--nmax` parameter, added `--nmin` (minimum number of timbre types, invalid when cluster parameter is 2) `--cluster` (clustering method, 1:SpectralCluster, 2:UmapHdbscan), `--mer_cosine` to merge clusters that are too similar.
**New version tips**
1. Run `splitter.py` directly with the default parameters by specifying the speaker.
2. If the result has only one cluster, observe the distribution map, set `--nmin` to the number you think is reasonable, and run `splitter.py` again.
3. The optimal value of `--nmin` may be smaller than expected in actual tests.
4. The new clustering algorithm is faster, it is recommended to try multiple times.
5. The emotion classification function has now been implemented and can be called through the `--encoder emotion` function. Go to when using https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim/tree/main Download `pytorch_Model.bin` is placed in the `wav2vec2-large-robust-12-ft-emotion-msp-dim` directory.
6. You can also use `--encoder mix` to filter audio that matches two similar features at the same time. This feature can help you filter `GPT SoVITS` or `Bert-VITS2.3` prompts.
# Progress
- [x] **Correctly trained weights**
- [x] Clustering algorithm optimization
- [ ] ~SSL~
- [x] emotional encoder
- [x] embed mix
# Environment Configuration
It works normally under `python3.8`, please go to install [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
Then use the following command to install environment dependencies
```
pip install -r requirements.txt
```
Tips: If you are only using the timbre encoder, you only need to install the CPU version of pytorch. In other cases, it is recommended to use the GPU version.
# How to Use
**1. Move your well-made Diffsinger dataset to the `.\input` folder and run the following command**
```
python splitter.py --spk <speaker_name> --nmin <'N'_min_num>
```
Enter the speaker name after `--spk`, and enter the minimum number of timbre types after `--nmin` (minimum 1, maximum 14，default 1)
Tips: This project does not need to read the annotation file (transcriptions.csv) of the Diffsinger dataset, so as long as the file structure is as shown below, it can work normally
```
- input
- <speaker_name>
- raw
- wavs
- audio1.wav
- audio2.wav
- ...
```
The wav files are best already split
**2. After you select the optimal result you think, run the following command to classify the wav files in the dataset**
```
python move_files.py --spk <speaker_name>
```
The classified results will be saved in `.\output\<speaker_name>\<clust_num>`
After that, you still need to manually merge the too small clusters to meet the training requirements
**3. (Optional) Move `clean_csv.py` to the same level as `transcriptions.csv` and run it, you can delete the wav file entries that are not included in the `wavs` folder**
# Based on Project
[Resemblyzer](https://github.com/resemble-ai/Resemblyzer/)
[3D-Speaker](https://github.com/alibaba-damo-academy/3D-Speaker/)
[wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim)
[GTSinger](https://github.com/AaronZ345/GTSinger)
================================================
FILE: README_CN.md
================================================
# ColorSplitter
[webui](https://github.com/KakaruHayate/ColorSplitter/tree/main/viewer)
一个用于分离歌声音色的命令行工具
# 介绍
ColorSplitter是一个为了在歌声数据的处理前期，对单说话人数据的音色风格进行分类的命令行工具
对于不需要进行风格分类的场合，使用本工具进行数据筛选，也可以减轻模型的音色表现不稳定问题
**请注意**，本项目基于说话人确认（Speaker Verification）技术，目前并不确定唱歌的音色变化是与声纹差异完全相关，just for fun：)
目前该领域研究仍然匮乏，抛砖引玉
感谢社区用户：洛泠羽
# 新版本特性
实装了聚类结果自动优化，不再需要用户自己判断聚类最优结果
`splitter.py`删除了`--nmax`参数，添加了`--nmin`（最小音色类型数量，cluster参数为2时无效）`--cluster`（聚类方式，1:SpectralCluster, 2:UmapHdbscan），`--mer_cosine`合并过于相似的簇
**新版本使用技巧**
1.默认参数直接指定说话人运行`splitter.py`
2.如果结果只有一个簇，观察分布图，将`--nmin`设为你认为合理的数量，再次运行`splitter.py`
3.实际测试下`--nmin`的最优值可能比想象的要小
4.新的聚类算法速度较快，建议多次尝试
5.新版本已支持情绪编码器的使用，可以通过`--encoder emotion`调用。使用时前往 https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim/tree/main 下载 `pytorch_model.bin` 放置在 `pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim` 目录下
6.你也可以用`--encoder mix`筛选同时符合两个特征相似的音频，这个功能可以帮助你筛选`GPT SoVITS`和`BertVITS2.3`的参考音频
# 进展
- [x] **正确训练的权重**
- [x] 聚类算法优化
- [ ] ~SSL~
- [x] emotional encoder
- [x] embed mix
# 环境配置
`python3.8`下使用正常，请先安装[Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
之后使用以下命令安装环境依赖
```
pip install -r requirements.txt
```
注意：如果你只是用音色编码器则只需要安装CPU版本的pytorch，其他情况下建议使用GPU版本
# 如何使用
**1.将你制作好的Diffsinger数据集移动到`.\input`文件夹下，运行以下命令**
```
python splitter.py --spk <speaker_name> --nmin <'N'_min_num>
```
其中`--spk`后输入说话人名称，`--nmin`后输入最小音色类型数量（最小1最大14默认1）
tips:本项目并不需要读取Diffsinger数据集的标注文件（transcriptions.csv），所以保证只要文件结构如下所示就可以正常工作
```
- input
- <speaker_name>
- raw
- wavs
- audio1.wav
- audio2.wav
- ...
```
其中wav文件最好已经进行过切分
**2.选定你认为的最优结果后，运行以下命令将数据集中的wav文件分类**
```
python move_files.py --spk <speaker_name>
```
分类后结果将保存到`.\output\<speaker_name>\<clust_num>`中
在那之后还需要人工对过小的簇进行归并，以达到训练的需求
**3.（可选）将`clean_csv.py`移动到与`transcriptions.csv`同级后运行，可以删除`wavs`文件夹中没有包含的wav文件条目**
# 基于项目
[Resemblyzer](https://github.com/resemble-ai/Resemblyzer/)
[3D-Speaker](https://github.com/alibaba-damo-academy/3D-Speaker/)
[wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim)
[GTSinger](https://github.com/AaronZ345/GTSinger)
================================================
FILE: clean_csv.py
================================================
import os
import csv
wav_files = set(f[:-4] for f in os.listdir('wavs') if f.endswith('.wav'))
with open('transcriptions.csv', 'r') as f:
reader = csv.reader(f)
header = next(reader)
rows = [row for row in reader if row[0] in wav_files]
with open('transcriptions.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(header)
writer.writerows(rows)
================================================
FILE: kick.py
================================================
import os
import shutil
import pandas as pd
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--spk', type=str, help='Speaker name')
parser.add_argument('--clust', type=int, help='Cluster value')
parser.add_argument('--encoder', type=str, default='timbre', help='encoder type')
args = parser.parse_args()
Speaker_name = args.spk #Speaker name
clust_value = args.clust # Cluster value
encoder_name = args.encoder
data = pd.read_csv(os.path.join('output', Speaker_name, f'clustered_files({encoder_name}).csv'))
for index, row in data.iterrows():
file_path = row['filename']
clust = row['clust']
if clust == clust_value:
clust_dir = os.path.join('input', f'{Speaker_name}_{clust_value}')
if not os.path.exists(clust_dir):
os.makedirs(clust_dir)
shutil.move(file_path, clust_dir)
================================================
FILE: load_npy.py
================================================
import pandas as pd
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from modules.visualizations import plot_projections, process_json_file
from modules.cluster import CommonClustering
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, help='path to the .npy file')
parser.add_argument('--reducer', type=int, default=2, help='1:tSNE, 2:Umap')
parser.add_argument('--json', type=str, default=None, help='path to the .json file')
args = parser.parse_args()
if args.reducer == 1:
cluster_name = 'spectral'
elif args.reducer == 2:
cluster_name = 'umap_hdbscan'
else:
raise ValueError('reducer type error')
npy_path = args.path
embeds = np.load(npy_path)
if args.json == None:
token_names = np.arange(embeds.shape[0])
else:
token_names = process_json_file(args.json)
labels = np.ones_like(token_names)
output_dir = f'output/npy_result'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
df = pd.DataFrame({
'token': [f'{i}' for i in range(embeds.shape[0])],
'clust': labels
})
df.to_csv(f'{output_dir}/clustered_files({os.path.basename(npy_path)}).csv', index=False)
plot_projections(embeds, labels, title="Embedding projections", cluster_name=cluster_name, labels=token_names)
plt.savefig(f'{output_dir}/embedding_projections({os.path.basename(npy_path)}).png', dpi=600)
plt.show()
================================================
FILE: modules/cluster.py
================================================
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import numpy as np
import scipy
import sklearn
from sklearn.cluster._kmeans import k_means
from sklearn.metrics.pairwise import cosine_similarity
try:
import umap, hdbscan
except ImportError:
raise ImportError(
"Package \"umap\" or \"hdbscan\" not found. \
Please install them first by \"pip install umap-learn hdbscan\"."
)
class SpectralCluster:
"""A spectral clustering method using unnormalized Laplacian of affinity matrix.
This implementation is adapted from https://github.com/speechbrain/speechbrain.
"""
def __init__(self, min_num_spks=1, max_num_spks=14, pval=0.02, min_pnum=6, oracle_num=None):
self.min_num_spks = min_num_spks
self.max_num_spks = max_num_spks
self.min_pnum = min_pnum
self.pval = pval
self.k = oracle_num
def __call__(self, X, pval=None, oracle_num=None):
# Similarity matrix computation
sim_mat = self.get_sim_mat(X)
# Refining similarity matrix with pval
prunned_sim_mat = self.p_pruning(sim_mat, pval)
# Symmetrization
sym_prund_sim_mat = 0.5 * (prunned_sim_mat + prunned_sim_mat.T)
# Laplacian calculation
laplacian = self.get_laplacian(sym_prund_sim_mat)
# Get Spectral Embeddings
emb, num_of_spk = self.get_spec_embs(laplacian, oracle_num)
# Perform clustering
labels = self.cluster_embs(emb, num_of_spk)
return labels
def get_sim_mat(self, X):
# Cosine similarities
M = cosine_similarity(X, X)
return M
def p_pruning(self, A, pval=None):
if pval is None:
pval = self.pval
n_elems = int((1 - pval) * A.shape[0])
n_elems = min(n_elems, A.shape[0]-self.min_pnum)
# For each row in a affinity matrix
for i in range(A.shape[0]):
low_indexes = np.argsort(A[i, :])
low_indexes = low_indexes[0:n_elems]
# Replace smaller similarity values by 0s
A[i, low_indexes] = 0
return A
def get_laplacian(self, M):
M[np.diag_indices(M.shape[0])] = 0
D = np.sum(np.abs(M), axis=1)
D = np.diag(D)
L = D - M
return L
def get_spec_embs(self, L, k_oracle=None):
if k_oracle is None:
k_oracle = self.k
lambdas, eig_vecs = scipy.linalg.eigh(L)
if k_oracle is not None:
num_of_spk = k_oracle
else:
lambda_gap_list = self.getEigenGaps(
lambdas[self.min_num_spks - 1:self.max_num_spks + 1])
num_of_spk = np.argmax(lambda_gap_list) + self.min_num_spks
emb = eig_vecs[:, :num_of_spk]
return emb, num_of_spk
def cluster_embs(self, emb, k):
# k-means
_, labels, _ = k_means(emb, k, n_init='auto')
return labels
def getEigenGaps(self, eig_vals):
eig_vals_gap_list = []
for i in range(len(eig_vals) - 1):
gap = float(eig_vals[i + 1]) - float(eig_vals[i])
eig_vals_gap_list.append(gap)
return eig_vals_gap_list
class UmapHdbscan:
"""
Reference:
- Siqi Zheng, Hongbin Suo. Reformulating Speaker Diarization as Community Detection With
Emphasis On Topological Structure. ICASSP2022
"""
def __init__(self, n_neighbors=20, n_components=60, min_samples=20, min_cluster_size=10, metric='euclidean'):
self.n_neighbors = n_neighbors
self.n_components = n_components
self.min_samples = min_samples
self.min_cluster_size = min_cluster_size
self.metric = metric
def __call__(self, X):
umap_X = umap.UMAP(
n_neighbors=self.n_neighbors,
min_dist=0.0,
n_components=min(self.n_components, X.shape[0]-2),
metric=self.metric,
).fit_transform(X)
labels = hdbscan.HDBSCAN(min_samples=self.min_samples, min_cluster_size=self.min_cluster_size).fit_predict(umap_X)
return labels
class CommonClustering:
"""Perfom clustering for input embeddings and output the labels.
"""
def __init__(self, cluster_type, cluster_line=10, mer_cos=None, min_cluster_size=4, **kwargs):
self.cluster_type = cluster_type
self.cluster_line = cluster_line
self.min_cluster_size = min_cluster_size
self.mer_cos = mer_cos
if self.cluster_type == 'spectral':
self.cluster = SpectralCluster(**kwargs)
elif self.cluster_type == 'umap_hdbscan':
kwargs['min_cluster_size'] = min_cluster_size
self.cluster = UmapHdbscan(**kwargs)
else:
raise ValueError(
'%s is not currently supported.' % self.cluster_type
)
def __call__(self, X):
# clustering and return the labels
assert len(X.shape) == 2, 'Shape of input should be [N, C]'
if X.shape[0] < self.cluster_line:
return np.ones(X.shape[0], dtype=int)
# clustering
labels = self.cluster(X)
# remove extremely minor cluster
labels = self.filter_minor_cluster(labels, X, self.min_cluster_size)
# merge similar speaker
if self.mer_cos is not None:
labels = self.merge_by_cos(labels, X, self.mer_cos)
return labels
def filter_minor_cluster(self, labels, x, min_cluster_size):
cset = np.unique(labels)
csize = np.array([(labels == i).sum() for i in cset])
minor_idx = np.where(csize < self.min_cluster_size)[0]
if len(minor_idx) == 0:
return labels
minor_cset = cset[minor_idx]
major_idx = np.where(csize >= self.min_cluster_size)[0]
major_cset = cset[major_idx]
major_center = np.stack([x[labels == i].mean(0) \
for i in major_cset])
for i in range(len(labels)):
if labels[i] in minor_cset:
cos_sim = cosine_similarity(x[i][np.newaxis], major_center)
labels[i] = major_cset[cos_sim.argmax()]
return labels
def merge_by_cos(self, labels, x, cos_thr):
# merge the similar speakers by cosine similarity
assert cos_thr > 0 and cos_thr <= 1
while True:
cset = np.unique(labels)
if len(cset) == 1:
break
centers = np.stack([x[labels == i].mean(0) \
for i in cset])
affinity = cosine_similarity(centers, centers)
affinity = np.triu(affinity, 1)
idx = np.unravel_index(np.argmax(affinity), affinity.shape)
if affinity[idx] < cos_thr:
break
c1, c2 = cset[np.array(idx)]
labels[labels==c2]=c1
return labels
================================================
FILE: modules/model/emotion_encoder.py
================================================
import torch
import torch.nn as nn
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
import os
import librosa
import numpy as np
class RegressionHead(nn.Module):
r"""Classification head."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.final_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class EmotionModel(Wav2Vec2PreTrainedModel):
r"""Speech emotion classifier."""
def __init__(self, config):
super().__init__(config)
self.config = config
self.wav2vec2 = Wav2Vec2Model(config)
self.classifier = RegressionHead(config)
self.init_weights()
def forward(
self,
input_values,
):
outputs = self.wav2vec2(input_values)
hidden_states = outputs[0]
hidden_states = torch.mean(hidden_states, dim=1)
logits = self.classifier(hidden_states)
return hidden_states, logits
# load model from hub
device = 'cuda' if torch.cuda.is_available() else "cpu"
model_path = './pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim'
processor = Wav2Vec2Processor.from_pretrained(model_path)
model = EmotionModel.from_pretrained(model_path).to(device)
def process_func(
x: np.ndarray,
sampling_rate: int,
embeddings: bool = False,
) -> np.ndarray:
r"""Predict emotions or extract embeddings from raw audio signal."""
# run through processor to normalize signal
# always returns a batch, so we just get the first entry
# then we put it on the device
y = processor(x, sampling_rate=sampling_rate)
y = y['input_values'][0]
y = y.reshape(1, -1)
y = torch.from_numpy(y).to(device)
# run through model
with torch.no_grad():
y = model(y)[0 if embeddings else 1]
# convert to numpy
y = y.detach().cpu().numpy()
return y
def extract_wav(path):
wav, sr = librosa.load(path, sr = 16000)
emb = process_func(np.expand_dims(wav, 0), sr, embeddings=True)
return emb
================================================
FILE: modules/model/voice_encoder.py
================================================
from resemblyzer.hparams import *
from resemblyzer 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 VoiceEncoder(nn.Module):
def __init__(self, device: Union[str, torch.device]="cpu", verbose=True, weights_fpath: Union[Path, str]=None):
"""
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.
:param weights_fpath: path to "<CUSTOM_MODEL>.pt" file path.
If None, defaults to built-in "pretrained.pt" model
"""
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
if weights_fpath is None:
weights_fpath = Path(__file__).resolve().parent.joinpath("pretrained.pt")
else:
weights_fpath = Path(weights_fpath)
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)
================================================
FILE: modules/utils.py
================================================
from resemblyzer import preprocess_wav
from modules.model.voice_encoder import VoiceEncoder
from tqdm import tqdm
import numpy as np
import pickle
import os
import importlib
class GetEmbeds:
""" Used to obtain embedding vectors for audio. Directly input wav.
"""
def __init__(self, encoder_type, Speaker_name):
self.encoder_type = encoder_type
self.Speaker_name = Speaker_name
if self.encoder_type == 'timbre':
self.encoder = VoiceEncoder(weights_fpath="pretrain/encoder_1570000.bak")
elif self.encoder_type == 'emotion':
self.emotion_module = importlib.import_module('modules.model.emotion_encoder')
elif self.encoder_type == 'mix':
self.encoder = VoiceEncoder(weights_fpath="pretrain/encoder_1570000.bak")
self.emotion_module = importlib.import_module('modules.model.emotion_encoder')
else:
raise ValueError(
'%s is not currently supported.' % self.encoder_type
)
def __call__(self, wav_fpaths):
if self.encoder_type == 'timbre':
embeds = self.timbre_encoder(wav_fpaths)
if self.encoder_type == 'emotion':
embeds = self.emotion_encoder(wav_fpaths)
if self.encoder_type == 'mix':
embeds = self.mix_encoder(wav_fpaths)
return embeds
def timbre_encoder(self, wav_fpaths):
features_path = os.path.join("input", self.Speaker_name, "features(timbre).pkl")
# Check if features already exist
if os.path.exists(features_path):
with open(features_path, 'rb') as f:
embeds = pickle.load(f)
else:
wavs = [preprocess_wav(wav_fpath) for wav_fpath in \
tqdm(wav_fpaths, f"Preprocessing wavs ({len(wav_fpaths)} utterances)")]
embeds = np.array(list(map(self.encoder.embed_utterance, wavs)))
with open(features_path, 'wb') as f:
pickle.dump(embeds, f)
return embeds
def emotion_encoder(self, wav_fpaths):
features_path = os.path.join("input", self.Speaker_name, "features(emotion).pkl")
# Check if features already exist
if os.path.exists(features_path):
with open(features_path, 'rb') as f:
embeds = pickle.load(f)
else:
embeds = [self.emotion_module.extract_wav(wav_fpath) for wav_fpath in \
tqdm(wav_fpaths, f"Preprocessing wavs ({len(wav_fpaths)} utterances)")]
embeds = np.concatenate(embeds,axis=0)
with open(features_path, 'wb') as f:
pickle.dump(embeds, f)
return embeds
def mix_encoder(self, wav_fpaths):
features_path = os.path.join("input", self.Speaker_name, "features(mix).pkl")
# Check if features already exist
if os.path.exists(features_path):
with open(features_path, 'rb') as f:
embeds = pickle.load(f)
else:
timber_embeds = self.timbre_encoder(wav_fpaths)
emotion_embeds = self.emotion_encoder(wav_fpaths)
embeds = np.concatenate((timber_embeds, emotion_embeds), axis=1)
with open(features_path, 'wb') as f:
pickle.dump(embeds, f)
return embeds
================================================
FILE: modules/visualizations.py
================================================
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.animation import FuncAnimation
from resemblyzer import sampling_rate
from matplotlib import cm
from time import sleep, perf_counter as timer
from umap import UMAP
from sys import stderr
import matplotlib.pyplot as plt
import numpy as np
from sklearn.manifold import TSNE
import json
_default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
_my_colors = np.array([
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[97, 142, 151],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
[76, 255, 0],
], dtype=float) / 255
def generate_colors(n):
cm = plt.get_cmap('gist_rainbow')
colors = [cm(1.*i/n) for i in range(n)]
return colors
def play_wav(wav, blocking=True):
try:
import sounddevice as sd
# Small bug with sounddevice.play: the audio is cut 0.5 second too early. We pad it to
# make up for that
wav = np.concatenate((wav, np.zeros(sampling_rate // 2)))
sd.play(wav, sampling_rate, blocking=blocking)
except Exception as e:
print("Failed to play audio: %s" % repr(e))
def plot_similarity_matrix(matrix, labels_a=None, labels_b=None, ax: plt.Axes=None, title=""):
if ax is None:
_, ax = plt.subplots()
fig = plt.gcf()
img = ax.matshow(matrix, extent=(-0.5, matrix.shape[0] - 0.5,
-0.5, matrix.shape[1] - 0.5))
ax.xaxis.set_ticks_position("bottom")
if labels_a is not None:
ax.set_xticks(range(len(labels_a)))
ax.set_xticklabels(labels_a, rotation=90)
if labels_b is not None:
ax.set_yticks(range(len(labels_b)))
ax.set_yticklabels(labels_b[::-1]) # Upper origin -> reverse y axis
ax.set_title(title)
cax = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.15)
fig.colorbar(img, cax=cax, ticks=np.linspace(0.4, 1, 7))
img.set_clim(0.4, 1)
img.set_cmap("inferno")
return ax
def plot_histograms(all_samples, ax=None, names=None, title=""):
"""
Plots (possibly) overlapping histograms and their median
"""
if ax is None:
_, ax = plt.subplots()
for samples, color, name in zip(all_samples, _default_colors, names):
ax.hist(samples, density=True, color=color + "80", label=name)
ax.legend()
ax.set_xlim(0.35, 1)
ax.set_yticks([])
ax.set_title(title)
ylim = ax.get_ylim()
ax.set_ylim(*ylim) # Yeah, I know
for samples, color in zip(all_samples, _default_colors):
median = np.median(samples)
ax.vlines(median, *ylim, color, "dashed")
ax.text(median, ylim[1] * 0.15, "median", rotation=270, color=color)
return ax
def plot_projections(embeds, speakers, ax=None, colors=None, markers=None, legend=True,
title="", cluster_name="", labels=None, **kwargs):
if ax is None:
_, ax = plt.subplots(figsize=(6, 6))
if cluster_name == 'spectral':
reducer = TSNE(init='pca', **kwargs)
if cluster_name == 'umap_hdbscan':
reducer = UMAP(**kwargs)
# Compute the 2D projections. You could also project to another number of dimensions (e.g.
# for a 3D plot) or use a different different dimensionality reduction like PCA or TSNE.
projs = reducer.fit_transform(embeds)
# Draw the projections
speakers = np.array(speakers)
colors = generate_colors(len(np.unique(speakers)))
colors = colors or _my_colors
for i, speaker in enumerate(np.unique(speakers)):
speaker_projs = projs[speakers == speaker]
marker = "o" if markers is None else markers[i]
label = speaker if legend else None
ax.scatter(*speaker_projs.T, s=60, c=[colors[i]], marker=marker, label=label, edgecolors='k')
if labels is not None:
for j, (proj_x, proj_y) in enumerate(speaker_projs):
label_index = np.where(speakers == speaker)[0][j]
ax.text(proj_x, proj_y, str(labels[label_index]), fontsize=8, ha='right')
center = speaker_projs.mean(axis=0)
ax.scatter(*center, s=200, c=[colors[i]], marker="X", edgecolors='k')
if legend:
ax.legend(title="Speakers", ncol=2)
ax.set_title(title)
#ax.set_xticks([])
#ax.set_yticks([])
ax.grid(True)
ax.set_aspect("equal")
return projs
def interactive_diarization(similarity_dict, wav, wav_splits, x_crop=5, show_time=False):
fig, ax = plt.subplots()
lines = [ax.plot([], [], label=name)[0] for name in similarity_dict.keys()]
text = ax.text(0, 0, "", fontsize=10)
def init():
ax.set_ylim(0.4, 1)
ax.set_ylabel("Similarity")
if show_time:
ax.set_xlabel("Time (seconds)")
else:
ax.set_xticks([])
ax.set_title("Diarization")
ax.legend(loc="lower right")
return lines + [text]
times = [((s.start + s.stop) / 2) / sampling_rate for s in wav_splits]
rate = 1 / (times[1] - times[0])
crop_range = int(np.round(x_crop * rate))
ticks = np.arange(0, len(wav_splits), rate)
ref_time = timer()
def update(i):
# Crop plot
crop = (max(i - crop_range // 2, 0), i + crop_range // 2)
ax.set_xlim(i - crop_range // 2, crop[1])
if show_time:
crop_ticks = ticks[(crop[0] <= ticks) * (ticks <= crop[1])]
ax.set_xticks(crop_ticks)
ax.set_xticklabels(np.round(crop_ticks / rate).astype(np.int))
# Plot the prediction
similarities = [s[i] for s in similarity_dict.values()]
best = np.argmax(similarities)
name, similarity = list(similarity_dict.keys())[best], similarities[best]
if similarity > 0.75:
message = "Speaker: %s (confident)" % name
color = _default_colors[best]
elif similarity > 0.65:
message = "Speaker: %s (uncertain)" % name
color = _default_colors[best]
else:
message = "Unknown/No speaker"
color = "black"
text.set_text(message)
text.set_c(color)
text.set_position((i, 0.96))
# Plot data
for line, (name, similarities) in zip(lines, similarity_dict.items()):
line.set_data(range(crop[0], i + 1), similarities[crop[0]:i + 1])
# Block to synchronize with the audio (interval is not reliable)
current_time = timer() - ref_time
if current_time < times[i]:
sleep(times[i] - current_time)
elif current_time - 0.2 > times[i]:
print("Animation is delayed further than 200ms!", file=stderr)
return lines + [text]
ani = FuncAnimation(fig, update, frames=len(wav_splits), init_func=init, blit=not show_time,
repeat=False, interval=1)
play_wav(wav, blocking=False)
plt.show()
def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
if ax is None:
_, ax = plt.subplots()
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)
def process_json_file(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
data = json.load(file)
merged_data = {}
for key, value in data.items():
if value not in merged_data:
merged_data[value] = []
merged_data[value].append(key)
result_str = ["</PAD>"]
for keys in merged_data.values():
result_str.append(','.join(keys))
return result_str
================================================
FILE: move_files.py
================================================
import os
import shutil
import pandas as pd
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--spk', type=str, help='Speaker name')
parser.add_argument('--encoder', type=str, default='timbre', help='encoder type')
args = parser.parse_args()
Speaker_name = args.spk #Speaker name
encoder_name = args.encoder
data = pd.read_csv(os.path.join('output', Speaker_name, f'clustered_files({encoder_name}).csv'))
for index, row in data.iterrows():
file_path = row['filename']
clust = row['clust']
clust_dir = os.path.join('output', Speaker_name, str(clust))
if not os.path.exists(clust_dir):
os.makedirs(clust_dir)
shutil.copy(file_path, clust_dir)
================================================
FILE: pretrain/encoder_1570000.bak
================================================
[File too large to display: 16.3 MB]
================================================
FILE: pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/LICENSE
================================================
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================================================
FILE: pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json
================================================
{
"_name_or_path": "torch",
"activation_dropout": 0.1,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 768,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "sum",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": true,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.1,
"feat_quantizer_dropout": 0.0,
"final_dropout": 0.1,
"finetuning_task": "wav2vec2_reg",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "arousal",
"1": "dominance",
"2": "valence"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"arousal": 0,
"dominance": 1,
"valence": 2
},
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_feature_length": 10,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.05,
"model_type": "wav2vec2",
"num_adapter_layers": 3,
"num_attention_heads": 16,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 12,
"num_negatives": 100,
"output_hidden_size": 1024,
"pad_token_id": 0,
"pooling_mode": "mean",
"problem_type": "regression",
"proj_codevector_dim": 768,
"tdnn_dilation": [
1,
2,
3,
1,
1
],
"tdnn_dim": [
512,
512,
512,
512,
1500
],
"tdnn_kernel": [
5,
3,
3,
1,
1
],
"torch_dtype": "float32",
"transformers_version": "4.17.0.dev0",
"use_weighted_layer_sum": false,
"vocab_size": null,
"xvector_output_dim": 512
}
================================================
FILE: pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json
================================================
{
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000
}
================================================
FILE: pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json
================================================
{}
================================================
FILE: requirements.txt
================================================
torch
matplotlib>=3.0.0
numpy>=1.20.3
pandas
Resemblyzer
scikit_learn
sounddevice
tqdm
umap_learn
hdbscan
transformers
================================================
FILE: splitter.py
================================================
import pandas as pd
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from modules.utils import GetEmbeds
from modules.visualizations import plot_projections
from modules.cluster import CommonClustering
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument("--spk", type=str, help="Speaker name")
parser.add_argument("--nmin", type=int, default=1, help="minimum number of clusters")
parser.add_argument(
"--cluster", type=int, default=1, help="1:SpectralCluster, 2:UmapHdbscan"
)
parser.add_argument("--mer_cosine", type=str, default=None, help="merge similar embeds")
parser.add_argument("--encoder", type=str, default="timbre", help="encoder type")
args = parser.parse_args()
Speaker_name = args.spk # Speaker name
Nmin = args.nmin # set Nmax values
merge_cos = args.mer_cosine
encoder_name = args.encoder
data_dir = os.path.join("input", Speaker_name, "raw", "wavs")
wav_fpaths = list(Path(data_dir).glob("*.wav"))
encoder = GetEmbeds(encoder_type=encoder_name, Speaker_name=Speaker_name)
embeds = encoder.__call__(wav_fpaths)
while True:
if args.cluster == 1:
cluster_name = "spectral"
min_num_spks = Nmin
mer_cos = merge_cos
Cluster = CommonClustering(
cluster_type=cluster_name, mer_cos=None, min_num_spks=Nmin
)
elif args.cluster == 2:
cluster_name = "umap_hdbscan"
mer_cos = merge_cos
Cluster = CommonClustering(mer_cos=None, cluster_type=cluster_name)
else:
raise ValueError("cluster type error")
labels = Cluster.__call__(embeds)
output_dir = f"output/{Speaker_name}"
if not os.path.exists(output_dir):
os.makedirs(output_dir)
df = pd.DataFrame(
{"filename": [str(fpath) for fpath in wav_fpaths], "clust": labels}
)
proj = plot_projections(
embeds, labels, title="Embedding projections", cluster_name=cluster_name
)
df["x"] = proj[:, 0]
df["y"] = proj[:, 1]
plt.savefig(f"{output_dir}/embedding_projections({encoder_name}).png", dpi=600)
plt.show()
df.to_csv(f"{output_dir}/clustered_files({encoder_name}).csv", index=False)
user_input = input("Are you satisfied with the results?/是否满意结果？(y/n): ")
if user_input.lower() == "y":
break
else:
Nmin = int(input("Please enter a new Nmin value/请输入新的Nmin值: "))
================================================
FILE: viewer/.gitignore
================================================
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*
node_modules
dist
dist-ssr
*.local
# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
.DS_Store
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?
================================================
FILE: viewer/README.md
================================================
# Cluster viewer
After running Colour Splitter you would get a csv file. You can use this viewer to load this csv file, listen to each point in the scatter plot and interactively explore the cluster result.
## Prerequisites
- Bun (faster) or npm
- Modern web browser
## Build
1. Navigate to the viewer directory:
```bash
cd viewer
```
2. Install dependencies:
```bash
# Using Bun
bun install
# Or using npm
npm install
```
## Serve
You'll need to run two servers simultaneously *in viewer directory*:
1. Web Application Server
```bash
# Using Bun
bun run dev
# Or using npm
npm run dev
```
2. Audio file server
```bash
# Using Bun
bunx http-server --cors -p 8080
# Or using npm
npx http-server --cors -p 8080
```
## Usage
1. Open your browser and navigate to the URL shown by the web application server (typically something like http://localhost:5173)
2. Look for the tab titled "Vite + React + TS"
3. Drag and drop your CSV file (generated by the color splitter) into the designated dropping area
4. Explore your clusters and play audio samples by clicking on individual data points
================================================
FILE: viewer/eslint.config.js
================================================
import js from '@eslint/js'
import globals from 'globals'
import reactHooks from 'eslint-plugin-react-hooks'
import reactRefresh from 'eslint-plugin-react-refresh'
import tseslint from 'typescript-eslint'
export default tseslint.config(
{ ignores: ['dist'] },
{
extends: [js.configs.recommended, ...tseslint.configs.recommended],
files: ['**/*.{ts,tsx}'],
languageOptions: {
ecmaVersion: 2020,
globals: globals.browser,
},
plugins: {
'react-hooks': reactHooks,
'react-refresh': reactRefresh,
},
rules: {
...reactHooks.configs.recommended.rules,
'react-refresh/only-export-components': [
'warn',
{ allowConstantExport: true },
],
},
},
)
================================================
FILE: viewer/index.html
================================================
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Vite + React + TS</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.tsx"></script>
</body>
</html>
================================================
FILE: viewer/package.json
================================================
{
"name": "viewer",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "tsc -b && vite build",
"lint": "eslint .",
"preview": "vite preview"
},
"dependencies": {
"@tremor/react": "^3.18.7",
"autoprefixer": "^10.4.20",
"lucide-react": "^0.473.0",
"postcss": "^8.5.1",
"react": "^18.3.1",
"react-dom": "^18.3.1",
"recharts": "^2.15.0",
"tailwindcss": "^3.4.17"
},
"devDependencies": {
"@eslint/js": "^9.17.0",
"@types/react": "^18.3.18",
"@types/react-dom": "^18.3.5",
"@vitejs/plugin-react-swc": "^3.5.0",
"eslint": "^9.17.0",
"eslint-plugin-react-hooks": "^5.0.0",
"eslint-plugin-react-refresh": "^0.4.16",
"globals": "^15.14.0",
"typescript": "~5.6.2",
"typescript-eslint": "^8.18.2",
"vite": "^6.0.5"
}
}
================================================
FILE: viewer/postcss.config.js
================================================
export default {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
}
================================================
FILE: viewer/src/App.tsx
================================================
import AudioVisualizer from "./ScatterPlot"
function App() {
return (
<AudioVisualizer />
)
}
export default App
================================================
FILE: viewer/src/ScatterPlot.tsx
================================================
import React, { useState, useCallback } from 'react';
import { ScatterChart, Scatter, XAxis, YAxis, CartesianGrid } from 'recharts';
import { Play, Pause } from 'lucide-react';
interface DataPoint {
filename: string;
clust: number;
x: number;
y: number;
}
const AudioVisualizer = () => {
const [data, setData] = useState<DataPoint[]>([]);
const [selectedFile, setSelectedFile] = useState<string | null>(null);
const [currentAudio, setCurrentAudio] = useState<{
file: string;
audio: HTMLAudioElement | null;
isPlaying: boolean;
progress: number;
} | null>(null);
const handlePlay = (filename: string) => {
if (currentAudio?.file === filename) {
// Resume/pause existing audio
if (currentAudio.isPlaying) {
currentAudio.audio?.pause();
setCurrentAudio(prev => prev ? { ...prev, isPlaying: false } : null);
} else {
currentAudio.audio?.play();
setCurrentAudio(prev => prev ? { ...prev, isPlaying: true } : null);
}
} else {
// Stop previous audio if any
currentAudio?.audio?.pause();
// Create new audio
const audio = new Audio(`http://localhost:8080/${filename}`);
audio.addEventListener('ended', () => {
setCurrentAudio(prev => prev ? { ...prev, isPlaying: false, progress: 0 } : null);
});
audio.play();
setCurrentAudio({
file: filename,
audio,
isPlaying: true,
progress: 0
});
}
};
const handleDragOver = useCallback((e: React.DragEvent) => {
e.preventDefault();
}, []);
const handleDrop = useCallback((e: React.DragEvent) => {
e.preventDefault();
const file = e.dataTransfer.files[0];
if (file && file.name.endsWith('.csv')) {
const reader = new FileReader();
reader.onload = (event) => {
const text = event.target?.result as string;
const lines = text.split('\n');
const parsedData: DataPoint[] = lines.slice(1)
.filter(line => line.trim())
.map(line => {
const values = line.split(',');
return {
filename: values[0],
clust: parseInt(values[1], 10),
x: parseFloat(values[2]),
y: parseFloat(values[3])
};
});
setData(parsedData);
};
reader.readAsText(file);
}
}, []);
const handleClick = (point: DataPoint) => {
setSelectedFile(point.filename);
};
const handleReset = () => {
setSelectedFile(null);
};
const displayFiles = selectedFile ? data.filter(d => d.filename === selectedFile) : data;
return (
<div className="flex h-screen p-4 gap-4">
<div className="flex-1">
<div
className="w-full h-32 border-2 border-dashed border-gray-300 rounded-lg mb-4 flex items-center justify-center bg-gray-50"
onDragOver={handleDragOver}
onDrop={handleDrop}
>
<p className="text-gray-500">Drop CSV here</p>
</div>
{data.length > 0 && (
<ScatterChart
width={800}
height={600}
margin={{ top: 20, right: 20, bottom: 20, left: 20 }}
>
<CartesianGrid strokeDasharray="3 3" />
<XAxis type="number" dataKey="x" name="X" />
<YAxis type="number" dataKey="y" name="Y" />
{(() => {
const maxCluster = Math.max(...data.map(d => d.clust));
const numClusters = maxCluster + 1; // Since clusters start from 0
return Array.from(new Set(data.map(d => d.clust))).map((cluster) => {
const hue = (360 / numClusters) * cluster;
return (
<Scatter
key={cluster}
data={data.filter(d => d.clust === cluster)}
fill={`hsl(${hue}deg, 70%, 50%)`}
onClick={(point) => {
const p = point as unknown as DataPoint;
handleClick(p);
handlePlay(p.filename);
}}
cursor="pointer"
/>
);
});
})()}
</ScatterChart>
)}
</div>
<div className="w-96 border rounded-lg p-4 flex flex-col">
<div className="flex justify-between items-center mb-4">
<h2 className="text-lg font-semibold">Files</h2>
<button
onClick={handleReset}
className="px-3 py-1 bg-gray-100 hover:bg-gray-200 rounded"
>
Reset
</button>
</div>
<div className="flex-1 overflow-auto">
{displayFiles.map((file, index) => (
<div
key={index}
className={`p-2 text-sm ${file.filename === selectedFile ? 'bg-blue-100' : 'hover:bg-gray-50'
}`}
>
<div className="flex items-center gap-2">
<button
onClick={() => handlePlay(file.filename)}
className="p-1 hover:bg-gray-200 rounded"
>
{currentAudio?.file === file.filename && currentAudio.isPlaying ?
<Pause className="w-4 h-4" /> :
<Play className="w-4 h-4" />
}
</button>
<span>{file.filename.split('/').pop()}</span>
</div>
</div>
))}
</div>
</div>
</div>
);
};
export default AudioVisualizer;
================================================
FILE: viewer/src/index.css
================================================
@tailwind base;
@tailwind components;
@tailwind utilities;
================================================
FILE: viewer/src/main.tsx
================================================
import { StrictMode } from 'react'
import { createRoot } from 'react-dom/client'
import './index.css'
import App from './App.tsx'
createRoot(document.getElementById('root')!).render(
<StrictMode>
<App />
</StrictMode>,
)
================================================
FILE: viewer/src/vite-env.d.ts
================================================
/// <reference types="vite/client" />
================================================
FILE: viewer/tailwind.config.js
================================================
/** @type {import('tailwindcss').Config} */
export default {
content: [
"./index.html",
"./src/**/*.{js,ts,jsx,tsx}",
],
theme: {
extend: {},
},
plugins: [],
}
================================================
FILE: viewer/tsconfig.app.json
================================================
{
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
"target": "ES2020",
"useDefineForClassFields": true,
"lib": ["ES2020", "DOM", "DOM.Iterable"],
"module": "ESNext",
"skipLibCheck": true,
/* Bundler mode */
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"isolatedModules": true,
"moduleDetection": "force",
"noEmit": true,
"jsx": "react-jsx",
/* Linting */
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"noFallthroughCasesInSwitch": true,
"noUncheckedSideEffectImports": true
},
"include": ["src"]
}
================================================
FILE: viewer/tsconfig.json
================================================
{
"files": [],
"references": [
{ "path": "./tsconfig.app.json" },
{ "path": "./tsconfig.node.json" }
]
}
================================================
FILE: viewer/tsconfig.node.json
================================================
{
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
"target": "ES2022",
"lib": ["ES2023"],
"module": "ESNext",
"skipLibCheck": true,
/* Bundler mode */
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"isolatedModules": true,
"moduleDetection": "force",
"noEmit": true,
/* Linting */
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"noFallthroughCasesInSwitch": true,
"noUncheckedSideEffectImports": true
},
"include": ["vite.config.ts"]
}
================================================
FILE: viewer/vite.config.ts
================================================
import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react-swc'
// https://vite.dev/config/
export default defineConfig({
plugins: [react()],
})
gitextract_5gn6sueo/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── README_CN.md
├── clean_csv.py
├── kick.py
├── load_npy.py
├── modules/
│ ├── cluster.py
│ ├── model/
│ │ ├── emotion_encoder.py
│ │ └── voice_encoder.py
│ ├── utils.py
│ └── visualizations.py
├── move_files.py
├── pretrain/
│ ├── encoder_1570000.bak
│ └── wav2vec2-large-robust-12-ft-emotion-msp-dim/
│ ├── LICENSE
│ ├── config.json
│ ├── preprocessor_config.json
│ └── vocab.json
├── requirements.txt
├── splitter.py
└── viewer/
├── .gitignore
├── README.md
├── bun.lockb
├── eslint.config.js
├── index.html
├── package.json
├── postcss.config.js
├── src/
│ ├── App.tsx
│ ├── ScatterPlot.tsx
│ ├── index.css
│ ├── main.tsx
│ └── vite-env.d.ts
├── tailwind.config.js
├── tsconfig.app.json
├── tsconfig.json
├── tsconfig.node.json
└── vite.config.ts
SYMBOL INDEX (47 symbols across 7 files)
FILE: modules/cluster.py
class SpectralCluster (line 19) | class SpectralCluster:
method __init__ (line 24) | def __init__(self, min_num_spks=1, max_num_spks=14, pval=0.02, min_pnu...
method __call__ (line 31) | def __call__(self, X, pval=None, oracle_num=None):
method get_sim_mat (line 52) | def get_sim_mat(self, X):
method p_pruning (line 57) | def p_pruning(self, A, pval=None):
method get_laplacian (line 72) | def get_laplacian(self, M):
method get_spec_embs (line 79) | def get_spec_embs(self, L, k_oracle=None):
method cluster_embs (line 95) | def cluster_embs(self, emb, k):
method getEigenGaps (line 100) | def getEigenGaps(self, eig_vals):
class UmapHdbscan (line 108) | class UmapHdbscan:
method __init__ (line 115) | def __init__(self, n_neighbors=20, n_components=60, min_samples=20, mi...
method __call__ (line 122) | def __call__(self, X):
class CommonClustering (line 133) | class CommonClustering:
method __init__ (line 137) | def __init__(self, cluster_type, cluster_line=10, mer_cos=None, min_cl...
method __call__ (line 152) | def __call__(self, X):
method filter_minor_cluster (line 168) | def filter_minor_cluster(self, labels, x, min_cluster_size):
method merge_by_cos (line 187) | def merge_by_cos(self, labels, x, cos_thr):
FILE: modules/model/emotion_encoder.py
class RegressionHead (line 13) | class RegressionHead(nn.Module):
method __init__ (line 16) | def __init__(self, config):
method forward (line 23) | def forward(self, features, **kwargs):
class EmotionModel (line 34) | class EmotionModel(Wav2Vec2PreTrainedModel):
method __init__ (line 37) | def __init__(self, config):
method forward (line 45) | def forward(
function process_func (line 64) | def process_func(
function extract_wav (line 89) | def extract_wav(path):
FILE: modules/model/voice_encoder.py
class VoiceEncoder (line 11) | class VoiceEncoder(nn.Module):
method __init__ (line 12) | def __init__(self, device: Union[str, torch.device]="cpu", verbose=Tru...
method forward (line 51) | def forward(self, mels: torch.FloatTensor):
method compute_partial_slices (line 67) | def compute_partial_slices(n_samples: int, rate, min_coverage):
method embed_utterance (line 119) | def embed_utterance(self, wav: np.ndarray, return_partials=False, rate...
method embed_speaker (line 166) | def embed_speaker(self, wavs: List[np.ndarray], **kwargs):
FILE: modules/utils.py
class GetEmbeds (line 9) | class GetEmbeds:
method __init__ (line 13) | def __init__(self, encoder_type, Speaker_name):
method __call__ (line 28) | def __call__(self, wav_fpaths):
method timbre_encoder (line 38) | def timbre_encoder(self, wav_fpaths):
method emotion_encoder (line 53) | def emotion_encoder(self, wav_fpaths):
method mix_encoder (line 68) | def mix_encoder(self, wav_fpaths):
FILE: modules/visualizations.py
function generate_colors (line 33) | def generate_colors(n):
function play_wav (line 39) | def play_wav(wav, blocking=True):
function plot_similarity_matrix (line 50) | def plot_similarity_matrix(matrix, labels_a=None, labels_b=None, ax: plt...
function plot_histograms (line 75) | def plot_histograms(all_samples, ax=None, names=None, title=""):
function plot_projections (line 99) | def plot_projections(embeds, speakers, ax=None, colors=None, markers=Non...
function interactive_diarization (line 142) | def interactive_diarization(similarity_dict, wav, wav_splits, x_crop=5, ...
function plot_embedding_as_heatmap (line 208) | def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, colo...
function process_json_file (line 226) | def process_json_file(file_path):
FILE: viewer/src/App.tsx
function App (line 3) | function App() {
FILE: viewer/src/ScatterPlot.tsx
type DataPoint (line 5) | interface DataPoint {
Condensed preview — 38 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (89K chars).
[
{
"path": ".gitattributes",
"chars": 66,
"preview": "# Auto detect text files and perform LF normalization\n* text=auto\n"
},
{
"path": ".gitignore",
"chars": 3167,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 1069,
"preview": "MIT License\n\nCopyright (c) 2023 KakaruHayate\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "README.md",
"chars": 4333,
"preview": "# ColorSplitter\n\n\n\n[中文文档](README_CN.md)\n\n[webui](https://github.com/KakaruHayate/ColorS"
},
{
"path": "README_CN.md",
"chars": 2316,
"preview": "# ColorSplitter\n\n\n\n[webui](https://github.com/KakaruHayate/ColorSplitter/tree/main/view"
},
{
"path": "clean_csv.py",
"chars": 388,
"preview": "import os\nimport csv\n\n\nwav_files = set(f[:-4] for f in os.listdir('wavs') if f.endswith('.wav'))\nwith open('transcriptio"
},
{
"path": "kick.py",
"chars": 852,
"preview": "import os\nimport shutil\nimport pandas as pd\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--s"
},
{
"path": "load_npy.py",
"chars": 1380,
"preview": "import pandas as pd\nfrom pathlib import Path\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom modules.visualizati"
},
{
"path": "modules/cluster.py",
"chars": 6961,
"preview": "# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.\n# Licensed under the A"
},
{
"path": "modules/model/emotion_encoder.py",
"chars": 2450,
"preview": "import torch\nimport torch.nn as nn\nfrom transformers import Wav2Vec2Processor\nfrom transformers.models.wav2vec2.modeling"
},
{
"path": "modules/model/voice_encoder.py",
"chars": 9191,
"preview": "from resemblyzer.hparams import *\nfrom resemblyzer import audio\nfrom pathlib import Path\nfrom typing import Union, List\n"
},
{
"path": "modules/utils.py",
"chars": 3357,
"preview": "from resemblyzer import preprocess_wav\nfrom modules.model.voice_encoder import VoiceEncoder\nfrom tqdm import tqdm\nimport"
},
{
"path": "modules/visualizations.py",
"chars": 8025,
"preview": "from mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom matplotlib.animation import FuncAnimation\nfrom resemblyzer "
},
{
"path": "move_files.py",
"chars": 709,
"preview": "import os\nimport shutil\nimport pandas as pd\nimport argparse\nimport os\n\nparser = argparse.ArgumentParser()\nparser.add_arg"
},
{
"path": "pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/LICENSE",
"chars": 20846,
"preview": "Attribution-NonCommercial-ShareAlike 4.0 International\n\n================================================================"
},
{
"path": "pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json",
"chars": 2344,
"preview": "{\n \"_name_or_path\": \"torch\",\n \"activation_dropout\": 0.1,\n \"adapter_kernel_size\": 3,\n \"adapter_stride\": 2,\n \"add_ada"
},
{
"path": "pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json",
"chars": 214,
"preview": "{\n \"do_normalize\": true,\n \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n \"feature_size\": 1,\n \"padding_side\":"
},
{
"path": "pretrain/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json",
"chars": 2,
"preview": "{}"
},
{
"path": "requirements.txt",
"chars": 119,
"preview": "torch\nmatplotlib>=3.0.0\nnumpy>=1.20.3\npandas\nResemblyzer\nscikit_learn\nsounddevice\ntqdm\numap_learn\nhdbscan\ntransformers\n"
},
{
"path": "splitter.py",
"chars": 2380,
"preview": "import pandas as pd\nfrom pathlib import Path\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom modules.utils impor"
},
{
"path": "viewer/.gitignore",
"chars": 253,
"preview": "# Logs\nlogs\n*.log\nnpm-debug.log*\nyarn-debug.log*\nyarn-error.log*\npnpm-debug.log*\nlerna-debug.log*\n\nnode_modules\ndist\ndis"
},
{
"path": "viewer/README.md",
"chars": 1134,
"preview": "# Cluster viewer\n\nAfter running Colour Splitter you would get a csv file. You can use this viewer to load this csv file,"
},
{
"path": "viewer/eslint.config.js",
"chars": 734,
"preview": "import js from '@eslint/js'\nimport globals from 'globals'\nimport reactHooks from 'eslint-plugin-react-hooks'\nimport reac"
},
{
"path": "viewer/index.html",
"chars": 366,
"preview": "<!doctype html>\n<html lang=\"en\">\n <head>\n <meta charset=\"UTF-8\" />\n <link rel=\"icon\" type=\"image/svg+xml\" href=\"/"
},
{
"path": "viewer/package.json",
"chars": 860,
"preview": "{\n \"name\": \"viewer\",\n \"private\": true,\n \"version\": \"0.0.0\",\n \"type\": \"module\",\n \"scripts\": {\n \"dev\": \"vite\",\n "
},
{
"path": "viewer/postcss.config.js",
"chars": 80,
"preview": "export default {\n plugins: {\n tailwindcss: {},\n autoprefixer: {},\n },\n}\n"
},
{
"path": "viewer/src/App.tsx",
"chars": 123,
"preview": "import AudioVisualizer from \"./ScatterPlot\"\n\nfunction App() {\n return (\n <AudioVisualizer />\n )\n}\n\nexport default A"
},
{
"path": "viewer/src/ScatterPlot.tsx",
"chars": 6964,
"preview": "import React, { useState, useCallback } from 'react';\nimport { ScatterChart, Scatter, XAxis, YAxis, CartesianGrid } from"
},
{
"path": "viewer/src/index.css",
"chars": 58,
"preview": "@tailwind base;\n@tailwind components;\n@tailwind utilities;"
},
{
"path": "viewer/src/main.tsx",
"chars": 230,
"preview": "import { StrictMode } from 'react'\nimport { createRoot } from 'react-dom/client'\nimport './index.css'\nimport App from '."
},
{
"path": "viewer/src/vite-env.d.ts",
"chars": 38,
"preview": "/// <reference types=\"vite/client\" />\n"
},
{
"path": "viewer/tailwind.config.js",
"chars": 183,
"preview": "/** @type {import('tailwindcss').Config} */\nexport default {\n content: [\n \"./index.html\",\n \"./src/**/*.{js,ts,jsx"
},
{
"path": "viewer/tsconfig.app.json",
"chars": 665,
"preview": "{\n \"compilerOptions\": {\n \"tsBuildInfoFile\": \"./node_modules/.tmp/tsconfig.app.tsbuildinfo\",\n \"target\": \"ES2020\",\n"
},
{
"path": "viewer/tsconfig.json",
"chars": 119,
"preview": "{\n \"files\": [],\n \"references\": [\n { \"path\": \"./tsconfig.app.json\" },\n { \"path\": \"./tsconfig.node.json\" }\n ]\n}\n"
},
{
"path": "viewer/tsconfig.node.json",
"chars": 593,
"preview": "{\n \"compilerOptions\": {\n \"tsBuildInfoFile\": \"./node_modules/.tmp/tsconfig.node.tsbuildinfo\",\n \"target\": \"ES2022\","
},
{
"path": "viewer/vite.config.ts",
"chars": 165,
"preview": "import { defineConfig } from 'vite'\nimport react from '@vitejs/plugin-react-swc'\n\n// https://vite.dev/config/\nexport def"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the KakaruHayate/ColorSplitter GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 38 files (16.4 MB), approximately 21.1k tokens, and a symbol index with 47 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.