### kaldi-asr - CALLHOME Diarization Scripts

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

This repository contains example Bash scripts for speaker diarization on the CALLHOME dataset, as used in the 2000 NIST speaker recognition evaluation, leveraging the Kaldi ASR toolkit.

```Bash
https://github.com/kaldi-asr/kaldi/tree/master/egs/callhome_diarization
```

--------------------------------

### DiaPer: End-to-End Neural Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Pytorch implementation for DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors. Includes models pre-trained on free and public data.

```Python
'''
DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors

PyTorch implementation for speaker diarization using a Perceiver-based attractor model.

Example Usage:
import torch
from models.diaper import DiaperModel

# Load pre-trained model
# model = DiaperModel.from_pretrained('path/to/pretrained/model')
# model.eval()

# Example input (batch_size, sequence_length, feature_dim)
# dummy_input = torch.randn(1, 500, 80)
# with torch.no_grad():
#     diarization_output = model(dummy_input)
'''

```

--------------------------------

### StreamingSpeakerDiarization - Online Processing

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

This Python project extends pyannote.audio for streaming speaker diarization, enabling online processing of audio streams for diarization tasks.

```Python
https://github.com/juanmc2005/StreamingSpeakerDiarization/
```

--------------------------------

### Auto-Tuning Spectral Clustering (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

An implementation of the Auto-Tuning Spectral Clustering method that aims to perform clustering without requiring a development set or supervised tuning. Written in Python.

```Python
This entry represents the Auto-Tuning Spectral Clustering project. The actual code is hosted at https://github.com/tango4j/Auto-Tuning-Spectral-Clustering.git. It is designed for unsupervised tuning.
```

--------------------------------

### Spectral Clustering with Refinements (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Spectral clustering implementation featuring affinity matrix refinement, auto-tuning, and speaker turn constraints. This library is written in Python.

```Python
This entry represents the SpectralCluster project. The actual code is hosted at https://github.com/wq2012/SpectralCluster. It supports auto-tuning and speaker turn constraints.
```

--------------------------------

### FunASR - Python/PyTorch Speech Toolkit

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

FunASR is an open-source speech toolkit built on PyTorch, designed to bridge the gap between academic research and industrial applications in speech processing, including diarization.

```Python
https://github.com/alibaba-damo-academy/FunASR
```

--------------------------------

### Alize LIA_SpkSeg - C++ Speaker Diarization Tools

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

ALIZÉ is an open-source platform for speaker recognition, and LIA_SpkSeg provides the specific tools within this platform for speaker diarization, written in C++.

```C++
https://alize.univ-avignon.fr/
```

--------------------------------

### simple_diarizer - Simplified Python Pipeline

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

simple_diarizer provides a simplified Python pipeline for speaker diarization, utilizing pre-trained models to offer an easy-to-use solution from input audio to diarized segments.

```Python
https://github.com/cvqluu/simple_diarizer
```

--------------------------------

### pyBK - Python Speaker Diarization with Binary Key Modelling

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

pyBK offers a computationally light solution for speaker diarization using binary key speaker modeling. It does not require external training data and is implemented in Python.

```Python
https://github.com/josepatino/pyBK
```

--------------------------------

### Speaker-Diarization - Python uis-rnn/GhostVLAD

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A Python-based speaker diarization system that utilizes uis-rnn and GhostVLAD, designed to simplify the process and support openset speakers.

```Python
https://github.com/taylorlu/Speaker-Diarization
```

--------------------------------

### sherpa-onnx: Multi-functional Speech Toolkit

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Supports speaker diarization, speech recognition, and text-to-speech on various platforms with numerous language bindings. Built with ONNX Runtime.

```C++
// sherpa-onnx C++ API Example
/*
#include "sherpa/cpp/sherpa.h"

int main() {
    // Initialize Sherpa with desired model paths
    // sherpa::Sherpa sherpa("path/to/recognizer.zip");

    // Process audio stream
    // std::vector<float> audio_chunk = ...;
    // sherpa.AcceptWaveform(audio_chunk.data(), audio_chunk.size());
    // if (sherpa.IsReady()) {
    //     std::string result = sherpa.GetResult();
    //     std::cout << "Result: " << result << std::endl;
    // }
    return 0;
}
*/

```

```Python
# sherpa-onnx Python API Example
'''
import sherpa_onnx

# Create a recognizer
# recognizer = sherpa_onnx.SpeechRecognizer(
#     tokens="path/to/tokens.txt",
#     encoder="path/to/encoder.onnx",
#     decoder="path/to/decoder.onnx",
#     joiner="path/to/joiner.onnx"
# )

# Process audio
# audio = [0.1, -0.2, 0.3, ...]
# text = recognizer.recognize(audio)
# print(f"Recognized text: {text}")
'''

```

```C#
// sherpa-onnx C# API Example
/*
using SherpaOnnx;

public class Example
{
    public static void Main(string[] args)
    {
        // var recognizer = new SpeechRecognizer("path/to/recognizer.zip");
        // float[] audioData = new float[16000]; // 1 second of audio at 16kHz
        // recognizer.AcceptWaveform(audioData);
        // if (recognizer.IsReady())
        // {
        //     string result = recognizer.GetResult();
        //     Console.WriteLine($"Result: {result}");
        // }
    }
}
*/

```

--------------------------------

### kaldi-speaker-diarization - Icelandic Diarization Scripts

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A collection of Bash scripts for speaker diarization in Icelandic, utilizing the Kaldi toolkit and developed by the CADIA-LVL group.

```Bash
https://github.com/cadia-lvl/kaldi-speaker-diarization
```

--------------------------------

### pyannote-audio - Neural Blocks for Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

pyannote-audio is a Python toolkit providing neural building blocks for speaker diarization, including speech activity detection, speaker change detection, and speaker embedding.

```Python
https://github.com/pyannote/pyannote-audio
```

--------------------------------

### Picovoice Falcon: Speaker Diarization Engine

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A lightweight, accurate, and fast speaker diarization engine written in C and available in Python. It runs on CPU with minimal overhead.

```C
/*
 * Picovoice Falcon Speaker Diarization Engine (C API)
 * Provides core diarization functionality.
 */
// Example placeholder for C code structure
// int pv_diarize(const void* model, const float* pcm_data, int frame_length, int* num_speakers, int* start_times, int* end_times);

```

```Python
'''
Picovoice Falcon Speaker Diarization Engine (Python API)

A lightweight, accurate, and fast speaker diarization engine.

Example Usage:
from pv_falcon import Falcon

falcon = Falcon.create(access_key='YOUR_ACCESS_KEY')

# Process audio frames
# results = falcon.process(audio_frame)

falcon.delete()
'''

```

--------------------------------

### UIS-RNN-SML for Speaker Diarization (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A variant of the UIS-RNN algorithm, specifically for the paper 'Supervised Online Diarization with Sample Mean Loss for Multi-Domain Data'. Implemented in Python and PyTorch.

```Python
This entry represents the UIS-RNN-SML project. The actual code is hosted at https://github.com/DonkeyShot21/uis-rnn-sml. It is a supervised clustering algorithm.
```

--------------------------------

### Alibaba Tingwu (听悟) for Speaker Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Alibaba's Tingwu is a speech recognition service that includes speaker diarization capabilities, designed to process and understand multi-speaker audio content.

```APIDOC
Alibaba Tingwu (听悟):
  Functionality: Speaker Diarization
  Description: A speech recognition service that supports speaker diarization to distinguish between speakers in audio.
  Endpoint: https://tingwu.aliyuncs.com/trans
  Features: Speech-to-text, speaker diarization.
  Use Case: Transcribing and analyzing audio from various sources with multiple speakers.
```

--------------------------------

### Scikit-learn Clustering Algorithms (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Provides a comprehensive suite of clustering algorithms as part of the scikit-learn library. These are standard machine learning tools for unsupervised learning tasks.

```Python
This entry refers to the clustering modules within the scikit-learn library. For details on specific algorithms and usage, see https://scikit-learn.org/stable/modules/clustering.html.
```

--------------------------------

### Improved ResNet Speaker Recognition Architecture

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Implementation of a novel neural network architecture, ReDimNet, based on ResNet for speaker recognition. This project is presented in a research paper and is implemented using PyTorch.

```Pytorch
Project: IDRnD/ReDimNet
Method: improved resnet
Description: Neural network architecture presented in the paper [Reshape Dimensions Network for Speaker Recognition](https://arxiv.org/abs/2407.18223)
```

--------------------------------

### dscore: Diarization Scoring Tools

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Provides tools for diarization scoring, supporting both Python and Perl environments. Facilitates the calculation of various diarization performance metrics.

```Python
'''
dscore: Diarization Scoring Tools (Python)

Python interface for diarization scoring, often wrapping or reimplementing NIST standards.

Example Usage:
from dscore import DiarizationScorer

# Load reference and hypothesis files (e.g., RTTM format)
# scorer = DiarizationScorer()
# scorer.load_rttm('reference.rttm', 'hypothesis.rttm')

# Compute metrics
# metrics = scorer.compute_metrics()
# print(metrics)
'''

```

```Perl
# dscore: Diarization Scoring Tools (Perl)
#
# Perl module for diarization scoring, likely providing functions to parse RTTM files
# and compute metrics similar to md-eval.pl.
#
# Example Usage (conceptual):
# use dscore;
# my $scorer = dscore->new();
# my $metrics = $scorer->score('reference.rttm', 'hypothesis.rttm');
# print Dumper($metrics);

```

--------------------------------

### i-vector Speaker Recognition Systems

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Systems and toolkits focused on i-vector extraction for speaker recognition. These often involve C++ and Perl for core processing, with some Python integrations for specific tasks or frameworks like PyTorch.

```C++ & Perl
Project: idiap/kaldi-ivector
Method: i-vector
Description: Extension to Kaldi implementing the standard i-vector hyperparameter estimation and i-vector extraction procedure.
```

```Perl
Project: swshon/voxceleb-ivector
Method: i-vector
Description: Voxceleb1 i-vector based speaker recognition system.
```

```Python & PyTorch
Project: ville.vestman/asvtorch
Method: i-vector
Description: ASVtorch is a toolkit for automatic speaker recognition.
```

```Kaldi & PyTorch
Project: Snowdar/asv-subtools
Method: i-vector & x-vector
Description: ASV-Subtools is developed based on Pytorch and Kaldi for the task of speaker recognition, language identification, etc. The 'sub' of 'subtools' means that there are many modular tools and the parts constitute the whole.
```

--------------------------------

### Simplified PLDA Implementation (C++)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

An open-source implementation of a simplified Probabilistic Linear Discriminant Analysis (PLDA) algorithm. This version is written in C++.

```C++
This entry represents the PLDA project by mrouvier. The actual code is hosted at https://github.com/mrouvier/plda. It provides a C++ implementation of PLDA.
```

--------------------------------

### SpeechBrain - All-in-one PyTorch Speech Toolkit

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

SpeechBrain is a comprehensive, open-source speech toolkit based on PyTorch, offering a wide range of modules and recipes for various speech tasks, including speaker diarization.

```Python
https://github.com/speechbrain/speechbrain
```

--------------------------------

### VBx - Variational Bayes HMM over x-vectors

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

VBx is a Python-based speaker diarization system employing Variational Bayes HMM over x-vectors. It includes a recipe for x-vector extraction.

```Python
https://github.com/BUTSpeechFIT/VBx
```

--------------------------------

### SimpleDER: Lightweight DER Computation

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A lightweight library designed for fast computation of Diarization Error Rate (DER). Simplifies the process of evaluating diarization outputs.

```Python
'''
SimpleDER: Lightweight Diarization Error Rate (DER) Computation

A Python package for calculating DER efficiently.

Example Usage:
from simpleder import DER

# Define reference and hypothesis diarization results
# ref_diarization = {'SPEAKER_01': [(0, 5), (10, 15)], 'SPEAKER_02': [(6, 9)]}
# hyp_diarization = {'SPEAKER_A': [(0.5, 4.5), (10.2, 14.8)], 'SPEAKER_B': [(6.1, 8.9)]}

# Calculate DER
# der_calculator = DER()
# der_value = der_calculator.compute_der(ref_diarization, hyp_diarization)
# print(f"Computed DER: {der_value:.4f}")
'''

```

--------------------------------

### pyannote-metrics: Speaker Diarization Evaluation

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A toolkit for reproducible evaluation, diagnostic, and error analysis of speaker diarization systems. Provides metrics like Diarization Error Rate (DER).

```Python
'''
pyannote-metrics: Speaker Diarization Evaluation Toolkit

Provides tools for evaluating speaker diarization systems.

Example Usage:
from pyannote.core import Annotation
from pyannote.metrics import DiarizationErrorRate

# Create reference and hypothesis annotations
# reference = Annotation()
# hypothesis = Annotation()

# Add segments to annotations
# reference.add_label('SPEAKER_01', Segment(0, 5))
# hypothesis.add_label('SPEAKER_A', Segment(0.5, 4.5))

# Calculate DER
# metric = DiarizationErrorRate()
# der = metric(reference, hypothesis)
# print(f"Diarization Error Rate (DER): {der:.4f}")
'''

```

--------------------------------

### pyAudioAnalysis - Python Audio Feature Library

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

pyAudioAnalysis is a Python library for audio analysis, offering functionalities for feature extraction, classification, segmentation, and various audio-related applications, including speaker diarization.

```Python
https://github.com/tyiannak/pyAudioAnalysis
```

--------------------------------

### d-vector Speaker Embedding Implementations

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Collection of d-vector based speaker embedding systems. These implementations leverage deep learning models for speaker verification, applicable to voice cloning and diarization. They often rely on specific frameworks like PyTorch or TensorFlow.

```Python & PyTorch
Project: resemble-ai/Resemblyzer
Method: d-vector
Description: PyTorch implementation of generalized end-to-end loss for speaker verification, which can be used for voice cloning and diarization.
```

```Python & TensorFlow
Project: Janghyun1230/Speaker_Verification
Method: d-vector
Description: Tensorflow implementation of generalized end-to-end loss for speaker verification.
```

```Python & PyTorch
Project: HarryVolek/PyTorch_Speaker_Verification
Method: d-vector
Description: PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al. With UIS-RNN integration.
```

```Python & PyTorch
Project: CorentinJ/Real-Time-Voice-Cloning
Method: d-vector
Description: Implementation of "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis" (SV2TTS) with a vocoder that works in real-time.
```

```Python & TFLite
Project: tflite-hub/conformer-speaker-encoder
Method: d-vector
Description: Massively multilingual conformer-based speaker recognition models in TFLite format.
```

```Python & Keras
Project: philipperemy/deep-speaker
Method: d-vector
Description: Third party implementation of the Baidu paper Deep Speaker: an End-to-End Neural Speaker Embedding System.
```

--------------------------------

### x-vector Speaker Embedding Implementations

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Implementations of x-vectors, a popular deep learning-based speaker embedding technique. These systems are often built on frameworks like TensorFlow or PyTorch and may integrate with Kaldi for data processing or training recipes.

```Python & TensorFlow & Perl
Project: hsn-zeinali/x-vector-kaldi-tf
Method: x-vector
Description: Tensorflow implementation of x-vector topology on top of Kaldi recipe.
```

```Python & PyTorch
Project: manojpamk/pytorch_xvectors
Method: x-vector
Description: PyTorch implementation of Voxceleb x-vectors. Additionaly, includes meta-learning architectures for embedding training. Evaluated with speaker diarization and speaker verification.
```

```Python & C++ & PyTorch
Project: wenet-e2e/wespeaker.git
Method: x-vector & r-vector
Description: WeSpeaker is a research and production oriented speaker verification, recognition and diarization toolkit, which supports very strong recipes with on-the-fly data preparation, model training and evaluation, as well as runtime C++ codes.
```

--------------------------------

### EEND - End-to-End Neural Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

EEND (End-to-End Neural Diarization) is a framework implemented using Python and Bash, with potential Perl scripting, focusing on end-to-end approaches for speaker diarization.

```Python
https://github.com/hitachi-speech/EEND
```

--------------------------------

### UIS-RNN for Speaker Diarization (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Google's Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm for fully supervised speaker diarization. This implementation is based on Python and PyTorch.

```Python
This entry represents the UIS-RNN project. The actual code is hosted at https://github.com/google/uis-rnn. It is a supervised clustering algorithm.
```

--------------------------------

### MiniVox - MATLAB Speaker Diarization System

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

MiniVox is an open-source evaluation system specifically developed for the online speaker diarization task, written in MATLAB.

```MATLAB
https://github.com/doerlbh/MiniVox
```

--------------------------------

### Sequence Match Accuracy (Hungarian Algorithm)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Python implementation for matching the accuracy of two sequences, often used in diarization evaluation. Utilizes the Hungarian algorithm for optimal assignment.

```Python
'''
Sequence Match Accuracy (Hungarian Algorithm)

Python script for sequence matching, likely for aligning diarization outputs.
Uses the Hungarian algorithm to find the best permutation.

Example Usage (conceptual, from evals.py):
import numpy as np
from scipy.optimize import linear_sum_assignment

def compute_sequence_accuracy(ref_seq, hyp_seq):
    # Cost matrix calculation based on sequence alignment
    # cost_matrix = ...
    # row_ind, col_ind = linear_sum_assignment(cost_matrix)
    # accuracy = calculate_accuracy_from_assignment(row_ind, col_ind)
    # return accuracy
    pass
'''

```

--------------------------------

### LIUM SpkDiarization - Java Speaker Diarization Software

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

LIUM_SpkDiarization is a Java-based software suite dedicated to speaker diarization, encompassing speaker segmentation and clustering, and incorporating recent developments in the field.

```Java
https://projets-lium.univ-lemans.fr/spkdiarization/
```

--------------------------------

### SIDEKIT for diarization (s4d) - Python Extension

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

s4d is an open-source package extension for SIDEKIT, specifically tailored for speaker diarization tasks, providing enhanced functionalities for Python users.

```Python
https://projets-lium.univ-lemans.fr/s4d/
```

--------------------------------

### spyder: Fast DER Computation Package

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

A simple Python package designed for fast computation of Diarization Error Rate (DER). It offers an efficient implementation for evaluating diarization results.

```Python
'''
spyder: Fast DER Computation Package (Python)

Provides a Python interface for calculating DER, potentially with C++ backend for speed.

Example Usage:
from spyder import DERCalculator

# Initialize calculator
# calculator = DERCalculator()

# Compute DER from reference and hypothesis data
# der = calculator.compute(reference_data, hypothesis_data)
# print(f"DER: {der}")
'''

```

```C++
// spyder: Fast DER Computation Package (C++ Backend)
/*
// This would be the underlying C++ implementation for performance.
// It might be exposed via Python's C API (e.g., pybind11).

#include <vector>
#include <string>

namespace spyder {

class DERCalculatorImpl {
public:
    double compute(const std::vector<std::pair<double, double>>& ref_segments, 
                   const std::vector<std::pair<double, double>>& hyp_segments) {
        // ... DER computation logic ...
        return 0.0; // Placeholder
    }
};

}
*/

```

--------------------------------

### DiarizationLM: WER, WDER, cpWER Metrics

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Implements Word Error Rate (WER), Word Diarization Error Rate (WDER), and concatenated minimum-permutation Word Error Rate (cpWER). Useful for evaluating diarization systems with transcriptions.

```Python
'''
DiarizationLM: Word Error Rate (WER) and Diarization Metrics

Calculates WER, WDER, and cpWER for evaluating diarization systems.

Example Usage:
from diarizationlm.metrics import compute_wder

# Example reference and hypothesis data
# ref_data = {
#     'segment_1': {'speaker': 'SPEAKER_01', 'words': ['hello', 'world']},
#     'segment_2': {'speaker': 'SPEAKER_02', 'words': ['how', 'are', 'you']}
# }
# hyp_data = {
#     'segment_1': {'speaker': 'SPEAKER_A', 'words': ['hello', 'there']},
#     'segment_2': {'speaker': 'SPEAKER_B', 'words': ['how', 'are', 'you', 'doing']}
# }

# Calculate WDER
# wder_score = compute_wder(ref_data, hyp_data)
# print(f"Word Diarization Error Rate (WDER): {wder_score:.4f}")
'''

```

--------------------------------

### DNC for Speaker Diarization (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Transformer-based Discriminative Neural Clustering (DNC) for speaker diarisation. Similar to UIS-RNN, this method is supervised and implemented using Python and ESPnet.

```Python
This entry represents the DNC project. The actual code is hosted at https://github.com/FlorianKrey/DNC. It is a supervised clustering algorithm.
```

--------------------------------

### AaltoASR - Speaker Diarization Scripts

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

AaltoASR provides speaker diarization scripts developed based on the AaltoASR framework, utilizing both Python and Perl for implementation.

```Python
https://github.com/aalto-speech/speaker-diarization
```

--------------------------------

### PLDA for Speaker Recognition (Python)

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Probabilistic Linear Discriminant Analysis (PLDA) implementation for classification tasks, specifically in speaker recognition. This version is written in Python.

```Python
This entry represents the PLDA project by RaviSoji. The actual code is hosted at https://github.com/RaviSoji/plda. It focuses on PLDA and classification.
```

--------------------------------

### IBM Watson Speech To Text API: Speaker Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

IBM's Watson Speech To Text API provides advanced speech recognition capabilities, including speaker diarization to identify and separate speech from multiple speakers in an audio stream.

```APIDOC
IBM Watson Speech To Text API:
  Functionality: Speaker Diarization
  Description: Enables the separation and identification of different speakers within an audio recording.
  Endpoint: https://www.ibm.com/watson/services/speech-to-text
  Features: Speaker diarization, real-time transcription, custom models.
  Use Case: Transcribing meetings, lectures, and customer service calls with multiple participants.
```

--------------------------------

### Microsoft Azure Conversation Transcription API: Speaker Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Microsoft Azure's Conversation Transcription API is designed for transcribing conversations, offering speaker diarization to identify and label different speakers in real-time or batch processing.

```APIDOC
Microsoft Azure Conversation Transcription API:
  Functionality: Speaker Diarization
  Description: Transcribes conversations and identifies individual speakers, providing diarization for segmented speech.
  Endpoint: https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/conversation-transcription
  Features: Real-time and batch transcription, speaker diarization, meeting transcription.
  Use Case: Transcribing meetings, interviews, and customer interactions.
```

--------------------------------

### Google Cloud Speech-to-Text API: Speaker Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Google Cloud Speech-to-Text API offers features for speaker diarization, allowing you to identify different speakers within an audio file and segment the audio accordingly. It helps in understanding who spoke when.

```APIDOC
Google Cloud Speech-to-Text API:
  Functionality: Speaker Diarization
  Description: Identifies and segments speech by different speakers in an audio file.
  Endpoint: https://cloud.google.com/speech-to-text/docs/multiple-voices
  Features: Speaker diarization, speaker labels, time-stamped segments.
  Use Case: Transcribing multi-speaker conversations, meetings, interviews.
```

--------------------------------

### DeepAffects Speaker Diarization API

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

DeepAffects offers a dedicated Speaker Diarization API that analyzes audio files to identify distinct speakers and segment their speech contributions.

```APIDOC
DeepAffects Speaker Diarization API:
  Functionality: Speaker Diarization
  Description: Analyzes audio to identify individual speakers and segment their speech.
  Endpoint: https://www.deepaffects.com/diarization-api
  Features: Speaker segmentation, speaker identification.
  Use Case: Analyzing interviews, podcasts, and other audio content with multiple speakers.
```

--------------------------------

### Amazon Transcribe: Speaker Diarization

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Amazon Transcribe is a service that automatically converts speech to text. It includes speaker diarization capabilities to distinguish between different speakers in an audio recording.

```APIDOC
Amazon Transcribe:
  Functionality: Speaker Diarization
  Description: Automatically identifies different speakers in an audio file and labels their speech segments.
  Endpoint: https://aws.amazon.com/transcribe
  Features: Speaker identification, diarization, transcription.
  Use Case: Analyzing call center recordings, media content, and other audio with multiple speakers.
```

--------------------------------

### RE-VERB - Speaker Diarization System

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

RE: VERB is a speaker diarization system that allows users to send or record audio conversations and receive timestamps indicating who spoke when. It is implemented in Python and JavaScript.

```Python
https://github.com/team-re-verb/RE-VERB
```

--------------------------------

### NIST md-eval.pl: Diarization Evaluation Script

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Perl scripts for evaluating speaker diarization systems based on NIST standards. Includes multiple versions (md-eval.pl, md-eval-v21.pl, md-eval-22.pl) for compatibility and feature updates.

```Perl
# NIST md-eval.pl - Speaker Diarization Evaluation Script
#
# This script calculates various diarization error metrics.
# It is commonly used with reference and hypothesis files in a specific format.
#
# Usage:
# perl md-eval.pl -r reference_file.rttm -h hypothesis_file.rttm [options]
#
# Key metrics calculated include:
# - Total time
# - Speaker time
# - Missed speech
# - False alarm speech
# - Speaker error
# - Diarization Error Rate (DER)
#
# Note: Different versions (v21, v22) may have slight variations in options or output.

```

--------------------------------

### CDER_Metric: Conversational DER

Source: https://github.com/wq2012/awesome-diarization/blob/master/README.md

Implements Conversational Diarization Error Rate (CDER) as defined in the CSSD (Conversational Short-phrase Speaker Diarization) task. Suitable for evaluating diarization in conversational contexts.

```Python
'''
CDER_Metric: Conversational Diarization Error Rate

Python implementation for calculating CDER, a metric for conversational diarization.

Example Usage:
from cder_metric.metric import CDER

# Initialize CDER metric
# cder_calculator = CDER()

# Compute CDER using reference and hypothesis diarization data
# cder_value = cder_calculator.compute_cder(reference_diarization, hypothesis_diarization)
# print(f"Conversational DER (CDER): {cder_value:.4f}")
'''

```

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