Pyannote
Models by this creator
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speaker-diarization
659
The speaker-diarization model is an open-source pipeline created by pyannote, a company that provides AI consulting services. The model is used for speaker diarization, which is the process of partitioning an audio recording into homogeneous segments according to the speaker identity. This is useful for applications like meeting transcription, where it's important to know which speaker said what. The model relies on the pyannote.audio library, which provides a set of neural network-based building blocks for speaker diarization. The pipeline comes pre-trained and can be used off-the-shelf without the need for further fine-tuning. Model inputs and outputs Inputs Audio file**: The audio file to be processed for speaker diarization. Outputs Diarization**: The output of the speaker diarization process, which includes information about the start and end times of each speaker's turn, as well as the speaker labels. The output can be saved in the RTTM (Rich Transcription Time Marked) format. Capabilities The speaker-diarization model is a fully automatic pipeline that doesn't require any manual intervention, such as manual voice activity detection or manual specification of the number of speakers. It is benchmarked on a growing collection of datasets and achieves high accuracy, with low diarization error rates even in the presence of overlapped speech. What can I use it for? The speaker-diarization model can be used in various applications that involve audio processing, such as meeting transcription, audio indexing, and speaker attribution in podcasts or interviews. By automatically separating the audio into speaker turns, the model can greatly simplify the process of transcribing and analyzing audio recordings. Things to try One interesting aspect of the speaker-diarization model is its ability to handle a variable number of speakers. If the number of speakers is known in advance, you can provide this information to the model using the num_speakers option. Alternatively, you can specify a range for the number of speakers using the min_speakers and max_speakers options. Another feature to explore is the model's real-time performance. The pipeline is benchmarked to have a real-time factor of around 2.5%, meaning it can process a one-hour conversation in approximately 1.5 minutes. This makes the model suitable for near-real-time applications, where fast processing is essential.
Updated 4/28/2024
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segmentation
418
The segmentation model from PyAnnote is an open-source model for speaker segmentation. It can perform tasks like voice activity detection, overlapped speech detection, and resegmentation. The model was trained using the techniques described in the End-to-end speaker segmentation for overlap-aware resegmentation paper. Similar models from PyAnnote include the speaker-diarization pipeline, which can perform full speaker diarization. Model inputs and outputs The segmentation model takes audio samples as input and outputs speaker segmentation information. This can include the start and end times of speech regions, as well as indications of overlapping speech. Inputs Audio samples**: The model accepts raw audio data as input, which can be loaded using tools like torchaudio or librosa. Outputs Speech regions**: The model outputs a pyannote.core.Annotation instance containing the start and end times of detected speech regions. Overlapped speech regions**: The model can also output a pyannote.core.Annotation instance containing regions of overlapping speech. Raw segmentation scores**: The model can provide the raw segmentation scores as a pyannote.core.SlidingWindowFeature instance, which can be useful for further analysis. Capabilities The segmentation model from PyAnnote can perform a variety of speaker-related tasks beyond just basic voice activity detection. It can identify overlapping speech, which is useful for more accurate diarization, and can also be used for resegmentation of existing diarization output. What can I use it for? The segmentation model could be used in a variety of applications that require speaker-level information from audio, such as: Automatic transcription and captioning tools Audio-based analytics and customer service applications Podcast and meeting processing pipelines Enhancing existing speaker diarization systems PyAnnote also offers consulting services to help users make the most of their open-source models in production. Things to try One interesting aspect of the segmentation model is its ability to output raw segmentation scores, which can be useful for further analysis and experimentation. For example, you could try visualizing the segmentation scores over time to better understand the model's decision-making process. Additionally, the model's overlap detection capabilities could be leveraged to improve downstream tasks like speaker diarization or meeting summarization. By being aware of regions with overlapping speech, the model can help create more accurate speaker profiles and transcripts.
Updated 4/28/2024
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speaker-diarization-3.1
198
The speaker-diarization-3.1 model is a pipeline developed by the pyannote team that performs speaker diarization on audio data. It is an updated version of the speaker-diarization-3.0 model, removing the problematic use of onnxruntime and running the speaker segmentation and embedding entirely in PyTorch. This should ease deployment and potentially speed up inference. The model takes in mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance. It can handle stereo or multi-channel audio by automatically downmixing to mono, and it can resample audio files to 16kHz upon loading. Compared to the previous speaker-diarization-3.0 model, this updated version should provide a smoother and more efficient experience for users integrating the model into their applications. Model inputs and outputs Inputs Mono audio sampled at 16kHz**: The pipeline accepts a single-channel audio file sampled at 16kHz. It can automatically handle stereo or multi-channel audio by downmixing to mono. Outputs Speaker diarization**: The pipeline outputs a pyannote.core.Annotation instance containing the speaker diarization for the input audio. Capabilities The speaker-diarization-3.1 model is capable of accurately segmenting and labeling different speakers within an audio recording. It can handle challenging scenarios like overlapping speech and varying numbers of speakers. The model has been benchmarked on a wide range of datasets, including AISHELL-4, AliMeeting, AMI, AVA-AVD, DIHARD 3, MSDWild, REPERE, and VoxConverse, demonstrating robust performance across diverse audio scenarios. What can I use it for? The speaker-diarization-3.1 model can be valuable for a variety of audio-based applications that require identifying and separating different speakers. Some potential use cases include: Meeting transcription and analysis**: Automatically segmenting and labeling speakers in audio recordings of meetings, conferences, or interviews to facilitate post-processing and analysis. Audio forensics and investigation**: Separating and identifying speakers in audio evidence to aid in investigations and legal proceedings. Podcast and audio content production**: Streamlining the editing and post-production process for podcasts, audio books, and other multimedia content by automating speaker segmentation. Conversational AI and voice assistants**: Improving the ability of voice-based systems to track and respond to multiple speakers in real-time conversations. Things to try One interesting aspect of the speaker-diarization-3.1 model is its ability to control the number of speakers expected in the audio. By using the num_speakers, min_speakers, and max_speakers options, you can fine-tune the model's behavior to better suit your specific use case. For example, if you know the audio you're processing will have a fixed number of speakers, you can set num_speakers to that value to potentially improve the model's accuracy. Additionally, the model provides hooks for monitoring the progress of the pipeline, which can be useful for long-running or batch processing tasks. By using the ProgressHook, you can gain visibility into the model's performance and troubleshoot any issues that may arise.
Updated 4/29/2024
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speaker-diarization-3.0
142
The speaker-diarization-3.0 model is an open-source pipeline for speaker diarization, trained by Sverin Baroudi using the pyannote.audio library version 3.0.0. It takes in mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance, which can be used to identify who is speaking when in the audio. The pipeline was trained on a combination of several popular speech datasets, including AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. The model is similar to the speaker-diarization model, which uses an earlier version of the pyannote.audio library. Both models aim to perform the task of speaker diarization, identifying who is speaking when in an audio recording. Model inputs and outputs Inputs Mono audio sampled at 16kHz Outputs An Annotation instance containing the speaker diarization information, which can be used to identify when each speaker is talking. Capabilities The speaker-diarization-3.0 model can effectively identify speakers and when they are talking in a given audio recording. It can handle stereo or multi-channel audio by automatically downmixing to mono, and can also resample audio files to 16kHz if needed. The model achieves strong performance, with a diarization error rate (DER) of around 14% on the AISHELL-4 dataset. What can I use it for? The speaker-diarization-3.0 model can be useful for a variety of applications that require identifying speakers in audio, such as: Transcription and captioning for meetings or interviews Speaker tracking in security or surveillance applications Audience analysis for podcasts or other audio content Improving speech recognition systems by leveraging speaker information The maintainers of the model also offer consulting services for organizations looking to use this pipeline in production. Things to try One interesting aspect of the speaker-diarization-3.0 model is its ability to process audio on GPU, which can significantly improve the inference speed. The model achieves a real-time factor of around 2.5% when running on a single Nvidia Tesla V100 SXM2 GPU, meaning it can process a one-hour conversation in about 1.5 minutes. Developers can also experiment with running the model directly from memory, which may provide further performance improvements. The pipeline also offers hooks to monitor the progress of the diarization process, which can be useful for debugging and understanding the model's behavior.
Updated 4/28/2024
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segmentation-3.0
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The segmentation-3.0 model from pyannote is an open-source speaker segmentation model that can identify up to 3 speakers in a 10-second audio clip. It was trained on a combination of datasets including AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. This model builds on the speaker segmentation and speaker diarization models previously released by pyannote. Model inputs and outputs Inputs 10 seconds of mono audio sampled at 16kHz Outputs A (num_frames, num_classes) matrix where the 7 classes are non-speech, speaker #1, speaker #2, speaker #3, speakers #1 and #2, speakers #1 and #3, and speakers #2 and #3. Capabilities The segmentation-3.0 model can identify up to 3 speakers in a 10-second audio clip, including cases where multiple speakers are present. This makes it useful for various speech processing tasks such as voice activity detection, overlapped speech detection, and resegmentation. What can I use it for? The segmentation-3.0 model can be used as a building block in speech and audio processing pipelines, such as the speaker diarization pipeline also provided by pyannote. By integrating this model, you can create more robust and accurate speaker diarization systems that can handle overlapping speech. Things to try One interesting thing to try with the segmentation-3.0 model is to fine-tune it on your own data using the companion repository provided by Alexis Plaquet. This can help adapt the model to your specific use case and potentially improve its performance on your data.
Updated 4/28/2024
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voice-activity-detection
132
The voice-activity-detection model from the pyannote project is a powerful tool for identifying speech regions in audio. This model builds upon the pyannote.audio library, which provides a range of open-source speech processing tools. The maintainer, Hervé Niderb, offers paid consulting services to companies looking to leverage these tools in their own applications. Similar models provided by pyannote include segmentation, which performs speaker segmentation, and speaker-diarization, which identifies individual speakers within an audio recording. These models share the same underlying architecture and can be used in conjunction to provide a comprehensive speech processing pipeline. Model inputs and outputs Inputs Audio file**: The voice-activity-detection model takes a mono audio file sampled at 16kHz as input. Outputs Speech regions**: The model outputs an Annotation instance, which contains information about the start and end times of detected speech regions in the input audio. Capabilities The voice-activity-detection model is highly capable at identifying speech within audio recordings, even in the presence of background noise or overlapping speakers. By leveraging the pyannote.audio library, this model can be easily integrated into a wide range of speech processing applications, such as transcription, speaker diarization, and audio indexing. What can I use it for? The voice-activity-detection model can be a valuable tool for companies looking to extract meaningful insights from audio data. For example, it could be used to automatically generate transcripts of meetings or podcasts, or to identify relevant audio segments for further processing, such as speaker diarization or emotion analysis. Things to try One interesting application of the voice-activity-detection model could be to use it as a preprocessing step for other speech-related tasks. By first identifying the speech regions in an audio file, you can then focus your subsequent processing on these relevant portions, potentially improving the overall performance and efficiency of your system.
Updated 4/29/2024
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embedding
74
The embedding model from pyannote is a speaker embedding model that uses the canonical x-vector TDNN-based architecture, but with filter banks replaced by trainable SincNet features. This model reaches 2.8% equal error rate (EER) on the VoxCeleb 1 test set without any additional processing like voice activity detection (VAD) or probabilistic linear discriminant analysis (PLDA). Compared to similar models like the segmentation and speaker-diarization models from pyannote, the embedding model focuses specifically on extracting speaker embeddings from audio. Model inputs and outputs The embedding model takes in an audio file and outputs a numpy array representing the speaker embedding for the entire file. This embedding can then be used for tasks like speaker verification, where you can compare the embeddings of two speakers to determine how similar they are. Inputs Audio file**: The model accepts a single audio file as input, which can be in any format supported by the underlying audio library. Outputs Speaker embedding**: The model outputs a numpy array of shape (1, D), where D is the dimensionality of the speaker embedding. This embedding represents the speaker characteristics extracted from the entire input audio file. Capabilities The embedding model is capable of extracting robust speaker embeddings from audio data, which can be useful for a variety of applications like speaker verification, diarization, and identification. By using trainable SincNet features, the model is able to achieve strong performance on speaker verification tasks without the need for additional processing steps. What can I use it for? The embedding model can be used in a variety of applications that require speaker-level information, such as: Speaker verification**: The model can be used to generate speaker embeddings that can be compared to determine if two audio samples are from the same speaker. This is useful for applications like access control or fraud detection. Speaker diarization**: The model's embeddings can be used as input to a speaker diarization system to identify and segment different speakers within a longer audio recording. Speaker identification**: The model's embeddings can be used to identify specific speakers within a dataset, which can be useful for applications like transcription or meeting analysis. Things to try One interesting thing to try with the embedding model is to use it in combination with other audio processing techniques, such as voice activity detection (VAD) or probabilistic linear discriminant analysis (PLDA). By combining the model's speaker embeddings with these additional processing steps, you may be able to achieve even better performance on speaker verification and diarization tasks. Another interesting experiment would be to fine-tune the model on a specific dataset or domain of interest, which could potentially improve its performance on certain types of audio data. The maintainer's profile mentions that they offer consulting services to help users make the most of their open-source models, which could be a valuable resource for those looking to customize or optimize the embedding model for their specific needs.
Updated 4/29/2024