Speechbrain
Models by this creator
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spkrec-ecapa-voxceleb
132
The spkrec-ecapa-voxceleb model is a speaker verification system developed by the SpeechBrain team. It uses the ECAPA-TDNN architecture, which combines convolutional and residual blocks, to extract speaker embeddings from audio recordings. The model was trained on the Voxceleb 1 and Voxceleb 2 datasets, achieving an impressive Equal Error Rate (EER) of 0.8% on the Voxceleb1-test (Cleaned) dataset. Similar models include the VoxLingua107 ECAPA-TDNN Spoken Language Identification Model and the Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0 model, both of which leverage the ECAPA-TDNN architecture for different tasks. Model inputs and outputs Inputs Audio recordings, typically sampled at 16kHz (single channel) Outputs Speaker embeddings: A 768-dimensional vector that captures the speaker's voice characteristics Speaker verification score: A score indicating the likelihood that two audio recordings belong to the same speaker Capabilities The spkrec-ecapa-voxceleb model is highly capable at speaker verification tasks. It can be used to determine whether two audio recordings are from the same speaker by computing the cosine distance between their speaker embeddings. The model has demonstrated state-of-the-art performance on the Voxceleb benchmark, making it a reliable choice for applications that require accurate speaker identification. What can I use it for? The spkrec-ecapa-voxceleb model can be used in a variety of applications that require speaker verification, such as: Voice-based authentication systems: Verify the identity of users based on their voice characteristics. Speaker diarization: Identify and separate different speakers in an audio recording. Personalized digital assistants: Recognize the user's voice and tailor the experience accordingly. Biometric security: Enhance security by using voice as an additional biometric factor. Things to try One interesting thing to try with the spkrec-ecapa-voxceleb model is to use it as a feature extractor for other speaker-related tasks. The 768-dimensional speaker embeddings produced by the model can be a valuable input for training custom speaker recognition or speaker diarization models. Additionally, you could explore ways to combine the speaker embeddings with other modalities, such as text or visual information, to create multimodal speaker recognition systems.
Updated 5/28/2024
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tts-tacotron2-ljspeech
113
The tts-tacotron2-ljspeech model is a Text-to-Speech (TTS) model developed by SpeechBrain that uses the Tacotron2 architecture trained on the LJSpeech dataset. This model takes in text input and generates a spectrogram output, which can then be converted to an audio waveform using a vocoder like HiFiGAN. The model was trained to produce high-quality, natural-sounding speech. Compared to similar TTS models like XTTS-v2 and speecht5_tts, the tts-tacotron2-ljspeech model is focused specifically on English text-to-speech generation using the Tacotron2 architecture, while the other models offer more multilingual capabilities or additional tasks like speech translation. Model inputs and outputs Inputs Text**: The model accepts text input, which it then converts to a spectrogram. Outputs Spectrogram**: The model outputs a spectrogram representation of the generated speech. Alignment**: The model also outputs an alignment matrix, which shows the relationship between the input text and the generated spectrogram. Capabilities The tts-tacotron2-ljspeech model is capable of generating high-quality, natural-sounding English speech from text input. It can capture features like prosody and intonation, resulting in speech that sounds more human-like compared to simpler text-to-speech systems. What can I use it for? You can use the tts-tacotron2-ljspeech model to add text-to-speech capabilities to your applications, such as: Voice assistants**: Integrate the model into a voice assistant to allow users to interact with your application using natural language. Audiobook generation**: Generate high-quality audio narrations from text, such as for creating digital audiobooks. Language learning**: Use the model to provide pronunciations and examples of spoken English for language learners. Things to try One interesting aspect of the tts-tacotron2-ljspeech model is its ability to capture prosody and intonation in the generated speech. Try experimenting with different types of input text, such as sentences with various punctuation or emotional tone, to see how the model handles them. You can also try combining the model with a vocoder like HiFiGAN to generate the final audio waveform and listen to the results.
Updated 5/28/2024
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emotion-recognition-wav2vec2-IEMOCAP
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The emotion-recognition-wav2vec2-IEMOCAP model is a speech emotion recognition system developed by SpeechBrain. It uses a fine-tuned wav2vec2 model to classify audio recordings into one of several emotional categories. This model is similar to other speech emotion recognition models like wav2vec2-lg-xlsr-en-speech-emotion-recognition and wav2vec2-large-robust-12-ft-emotion-msp-dim, which also leverage the wav2vec2 architecture for this task. Model inputs and outputs Inputs Audio recordings**: The model takes raw audio recordings as input, which are automatically normalized to 16kHz single-channel format if needed. Outputs Emotion classification**: The model outputs a predicted emotion category, such as "angry", "calm", "disgust", etc. Confidence score**: The model also returns a confidence score for the predicted emotion. Capabilities The emotion-recognition-wav2vec2-IEMOCAP model can accurately classify the emotional content of audio recordings, achieving 78.7% accuracy on the IEMOCAP test set. This makes it a useful tool for applications that require understanding the emotional state of speakers, such as customer service, mental health monitoring, or interactive voice assistants. What can I use it for? This model could be integrated into a variety of applications that need to analyze the emotional tone of speech, such as: Call center analytics**: Analyze customer service calls to better understand customer sentiment and identify areas for improvement. Mental health monitoring**: Use the model to track changes in a patient's emotional state over time as part of remote mental health monitoring. Conversational AI**: Incorporate the model into a virtual assistant to enable more natural and empathetic interactions. Things to try One interesting thing to try with this model is to experiment with different audio preprocessing techniques, such as data augmentation or feature engineering, to see if you can further improve its performance on your specific use case. You could also explore combining this model with other speech technologies, like speaker verification, to create more advanced speech analysis systems.
Updated 5/28/2024
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lang-id-voxlingua107-ecapa
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The lang-id-voxlingua107-ecapa model is a spoken language recognition model trained on the VoxLingua107 dataset using the SpeechBrain framework. It utilizes the ECAPA-TDNN architecture, which has previously been used for speaker recognition tasks. The model can classify speech utterances into one of 107 different languages, including Abkhazian, Afrikaans, Amharic, and many more. This model was developed by the speechbrain team. The xlm-roberta-base-language-detection model is a fine-tuned version of the xlm-roberta-base model on the Language Identification dataset. It can classify text sequences into 20 different languages, including Arabic, English, French, and Chinese. This model was created by papluca. Model inputs and outputs Inputs Audio waveform (16 kHz, single channel) Outputs Language classification (one of 107 languages) Capabilities The lang-id-voxlingua107-ecapa model can accurately classify speech utterances into one of 107 different languages. This can be useful for various applications, such as language identification in multilingual environments, language-specific speech processing, and language-aware user interfaces. What can I use it for? The lang-id-voxlingua107-ecapa model can be used as a standalone language identification system or as a feature extractor for creating a custom language ID model on your own data. For example, you could use this model to build a multilingual chatbot or transcription service that can handle a wide variety of languages. Things to try One interesting thing to try with the lang-id-voxlingua107-ecapa model is to use it as a feature extractor for downstream tasks. By taking the utterance embeddings produced by the model, you can create a dedicated language ID model tailored to your specific use case, potentially improving performance beyond the general-purpose capabilities of the pre-trained model.
Updated 5/28/2024
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metricgan-plus-voicebank
51
The metricgan-plus-voicebank model is a speech enhancement model trained by the SpeechBrain team. This model uses the MetricGAN architecture to improve the quality of noisy speech signals. Similar models from SpeechBrain include the tts-tacotron2-ljspeech text-to-speech model and the spkrec-ecapa-voxceleb speaker verification model. Model inputs and outputs The metricgan-plus-voicebank model takes noisy speech signals as input and outputs enhanced, higher quality speech. The model was trained on the Voicebank dataset, which contains recordings of various speakers in noisy environments. Inputs Noisy speech signals, typically single-channel audio files sampled at 16kHz Outputs Enhanced, higher-quality speech signals Capabilities The metricgan-plus-voicebank model is capable of removing noise and improving the overall quality of speech recordings. It can be useful for tasks such as audio post-processing, speech enhancement for teleconferencing, and improving the quality of speech data for training other models. What can I use it for? The metricgan-plus-voicebank model can be used to enhance the quality of noisy speech recordings, which can be beneficial for a variety of applications. For example, it could be used to improve the audio quality of recordings for podcasts, online presentations, or customer service calls. Additionally, the enhanced speech data could be used to train other speech models, such as speech recognition or text-to-speech systems, leading to improved performance. Things to try One interesting thing to try with the metricgan-plus-voicebank model is to use it in combination with other SpeechBrain models, such as the tts-tacotron2-ljspeech text-to-speech model or the spkrec-ecapa-voxceleb speaker verification model. By using the speech enhancement capabilities of the metricgan-plus-voicebank model, you may be able to improve the overall performance of these other speech-related models.
Updated 9/6/2024
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sepformer-wsj02mix
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sepformer-wsj02mix The sepformer-wsj02mix is a model for performing audio source separation, implemented using the SpeechBrain toolkit. It has been trained on the WSJ0-2Mix dataset, and can separate a mixed audio signal into its individual sources. This model builds upon the SepFormer architecture, which has demonstrated strong performance on various audio separation tasks. The sepformer-wsj02mix model achieves a signal-to-distortion ratio improvement (SDRi) of 22.6 dB on the test set of the WSJ0-2Mix dataset. Inputs Mono audio signal sampled at 8kHz Outputs Separated audio signals for each speaker, each as a separate channel Capabilities The sepformer-wsj02mix model is capable of separating mixed audio signals into their individual sources. This can be useful in a variety of applications, such as: Speech enhancement: Separating a target speaker's voice from background noise or other speakers Music production: Isolating individual instruments or vocals from a mixed recording Podcast/interview transcription: Separating speakers in a multi-person conversation The model has been trained specifically on the WSJ0-2Mix dataset, so its performance may be best suited for tasks involving two-speaker audio mixtures. However, the SepFormer architecture is generally applicable to more complex mixtures as well. What can I use it for? The sepformer-wsj02mix model can be a valuable tool for a range of audio processing and analysis applications. Some potential use cases include: Transcription and diarization of multi-speaker recordings Karaoke or music remixing by isolating individual tracks Enhancement of teleconference/call quality by separating speakers Preprocessing of audio data for machine learning tasks like speaker identification or emotion recognition The model is available through the SpeechBrain toolkit, which provides a user-friendly interface for running source separation inference on custom audio files. By leveraging this pre-trained model, developers can quickly integrate high-quality audio separation capabilities into their applications without the need for extensive training data or model development. Things to try Some interesting experiments to try with the sepformer-wsj02mix model include: Evaluating performance on different audio domains**: While the model has been trained on the WSJ0-2Mix dataset, it may be interesting to test its capabilities on other types of audio mixtures, such as music, podcast interviews, or non-English speech. Comparing to other separation models**: The SepFormer architecture can be compared to other popular source separation models, such as those based on Permutation Invariant Training or Conv-TasNet, to assess its relative strengths and weaknesses. Exploring the model's generalization**: Investigating how the sepformer-wsj02mix model performs when the number of speakers in the mixture differs from the two-speaker setup it was trained on could yield interesting insights. By experimenting with the sepformer-wsj02mix model and exploring its capabilities, researchers and developers can gain a deeper understanding of its potential applications and limitations, and potentially uncover new use cases for this powerful audio separation technology.
Updated 9/6/2024
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spkrec-xvect-voxceleb
48
The spkrec-xvect-voxceleb model is a speaker verification system that uses x-vector embeddings trained on the VoxCeleb dataset. This model is provided by the SpeechBrain team, who are known for their general-purpose SpeechBrain toolkit. The SpeechBrain team has also released similar speaker verification models like the spkrec-ecapa-voxceleb which uses the ECAPA-TDNN architecture. Model inputs and outputs Inputs Audio recordings of speech, preferably sampled at 16 kHz Outputs Speaker embeddings, which are fixed-size vector representations of the input speech that can be used for speaker verification tasks Scores and predictions indicating whether two speech samples belong to the same speaker or not Capabilities This model can be used to extract speaker embeddings from audio recordings, which can then be used for tasks like speaker diarization, speaker clustering, and speaker recognition. The model achieves 3.2% equal error rate (EER) on the VoxCeleb1-test set, which is a strong performance for a speaker verification system. What can I use it for? The spkrec-xvect-voxceleb model can be used as a building block in various speech processing applications that require speaker recognition capabilities. For example, it could be used in a call center to identify speakers and route calls accordingly, or in a conferencing system to attribute each utterance to the correct participant. Additionally, the extracted speaker embeddings could be used as features in downstream machine learning models for tasks like speaker diarization or identification. Things to try One interesting thing to try with this model is to use the extracted speaker embeddings as inputs to a custom speaker recognition or diarization system. By leveraging the pre-trained embeddings, you may be able to achieve better performance on your specific use case compared to training a model from scratch. Additionally, you could experiment with combining this speaker verification model with other SpeechBrain models, such as the emotion-recognition-wav2vec2-IEMOCAP model, to create a more comprehensive speech processing pipeline.
Updated 9/6/2024